Fine-tuning copper concentrations to maximize zebra mussel control and minimize non-target impacts via the Biotic Ligand Model
Angelique Dahlberg
Welcome
This analysis was designed to accompany the files nontarget_exp.accdb and veliger_exp.accdb. Both files are included in the R Project. However, to connect to the databases (sections 1.1.1 and 2.1.1) you will need to change the file location to the location it is stored on the device you are working from (change the pathways after “accdbpath_nt” (1.1.1) and “accdbpath_vel” (2.1.1) to reflect the location on your computer).
Set-up
rm(list=ls()) # clear memorylibrary(knitr)
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## filter1. Non-target experiment
1.1 Connect to data
# Connect to non-target experiment data
driver_nt <- "Driver={Microsoft Access Driver (*.mdb, *.accdb)};" # Set up the driver info
accdbpath_nt <- "C:/Users/Dahlbergs/Dropbox/Angelique/PhD/Writing/4. Bioavailability modeling/DRUM/nontarget_exp.accdb" # This leads to the Access database containing non-target experiment data
path_nt <- paste0(driver_nt,"DBQ=", accdbpath_nt) # Set up database path
cnxn_nt <- odbcDriverConnect(path_nt) # Establish connection for database
# Note: you may need to install a driver for MS Access outside of R in order to make the connection to this database. See https://leowong.ca/blog/connect-to-microsoft-access-database-via-r/1.2 Water temp, DO, pH, specific conductance
# Load data into R dataframes
tbl_tankData <- sqlQuery(cnxn_nt, "SELECT Tbl_WaterChemistry_Tanks.Treatment, Tbl_WaterChemistry_Tanks.Temperature, Tbl_WaterChemistry_Tanks.DO, Tbl_WaterChemistry_Tanks.pH, Tbl_WaterChemistry_Tanks.Conductance FROM Tbl_WaterChemistry_Tanks;", stringsAsFactors = FALSE) # Tank data
tbl_lakeData <- sqlQuery(cnxn_nt, "SELECT Tbl_Source.Treatment, Tbl_Source.Temperature, Tbl_Source.DO, Tbl_Source.pH, Tbl_Source.Conductance FROM Tbl_Source;", stringsAsFactors = FALSE) # Lake datatbl_waterData <- rbind(tbl_tankData, tbl_lakeData) # Append tank and lake dataframes to create one table
tbl_waterData$Treatment <- as.factor(tbl_waterData$Treatment) # Modify variable type(s)summary(tbl_waterData) # Examine dataframe## Treatment Temperature DO pH Conductance
## Control:180 Min. :17.60 Min. : 5.100 Min. :7.190 Min. :130.6
## High :180 1st Qu.:22.80 1st Qu.: 8.012 1st Qu.:7.740 1st Qu.:155.6
## Lake : 30 Median :23.90 Median : 8.440 Median :8.355 Median :164.5
## Low :180 Mean :23.81 Mean : 8.444 Mean :8.149 Mean :170.9
## Medium :180 3rd Qu.:24.90 3rd Qu.: 8.807 3rd Qu.:8.520 3rd Qu.:181.4
## Max. :30.50 Max. :15.020 Max. :8.800 Max. :234.0group_by(tbl_waterData, Treatment) %>%
summarise(
mean = mean(Temperature),
sd = sd(Temperature)
)## # A tibble: 5 × 3
## Treatment mean sd
## <fct> <dbl> <dbl>
## 1 Control 23.8 2.06
## 2 High 23.7 2.09
## 3 Lake 24.4 1.62
## 4 Low 23.8 2.14
## 5 Medium 23.8 2.13group_by(tbl_waterData, Treatment) %>%
summarise(
mean = mean(DO),
sd = sd(DO)
)## # A tibble: 5 × 3
## Treatment mean sd
## <fct> <dbl> <dbl>
## 1 Control 8.48 1.12
## 2 High 8.33 1.18
## 3 Lake 8.39 0.375
## 4 Low 8.46 1.00
## 5 Medium 8.51 1.26group_by(tbl_waterData, Treatment) %>%
summarise(
mean = mean(pH),
sd = sd(pH)
)## # A tibble: 5 × 3
## Treatment mean sd
## <fct> <dbl> <dbl>
## 1 Control 8.13 0.413
## 2 High 8.16 0.392
## 3 Lake 8.60 0.149
## 4 Low 8.11 0.415
## 5 Medium 8.12 0.414group_by(tbl_waterData, Treatment) %>%
summarise(
mean = mean(Conductance),
sd = sd(Conductance)
)## # A tibble: 5 × 3
## Treatment mean sd
## <fct> <dbl> <dbl>
## 1 Control 171. 23.8
## 2 High 171. 24.0
## 3 Lake 167. 22.5
## 4 Low 171. 23.5
## 5 Medium 171. 23.7# Compare variability of different groups to determine if appropriate for ANOVA
leveneTest(tbl_waterData$Temperature~tbl_waterData$Treatment)## Levene's Test for Homogeneity of Variance (center = median)
## Df F value Pr(>F)
## group 4 0.5105 0.728
## 745leveneTest(tbl_waterData$DO~tbl_waterData$Treatment) # at least one group has a different variability range; sd above says Lake## Levene's Test for Homogeneity of Variance (center = median)
## Df F value Pr(>F)
## group 4 3.409 0.008953 **
## 745
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1leveneTest(tbl_waterData$pH~tbl_waterData$Treatment) # at least one group has a different variability range; sd above says Lake## Levene's Test for Homogeneity of Variance (center = median)
## Df F value Pr(>F)
## group 4 11.04 1.055e-08 ***
## 745
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1leveneTest(tbl_waterData$Conductance~tbl_waterData$Treatment)## Levene's Test for Homogeneity of Variance (center = median)
## Df F value Pr(>F)
## group 4 0.0123 0.9997
## 745# Visualize variability differences
boxplot(tbl_waterData$Temperature~tbl_waterData$Treatment)
boxplot(tbl_waterData$DO~tbl_waterData$Treatment)
boxplot(tbl_waterData$pH~tbl_waterData$Treatment)
boxplot(tbl_waterData$Conductance~tbl_waterData$Treatment)
# Consider outliers (all together to get big picture)
hist(tbl_waterData$Temperature)
hist(tbl_waterData$DO)
hist(tbl_waterData$pH) # bimodality likely due to lake values (~8.5)
hist(tbl_waterData$Conductance)
# no outliers appear to be unreasonable, inaccurate, or worth excluding# Water temperature
(aov_temp <- oneway.test(Temperature~Treatment, tbl_waterData, var.equal=T)) #Temperatures do not vary between the tanks and the lake##
## One-way analysis of means
##
## data: Temperature and Treatment
## F = 0.6444, num df = 4, denom df = 745, p-value = 0.631# Dissolved oxygen (DO)
(aov_do <- oneway.test(DO~Treatment, tbl_waterData, var.equal=F)) #DOs do not vary between the tanks and the lake##
## One-way analysis of means (not assuming equal variances)
##
## data: DO and Treatment
## F = 0.72138, num df = 4.00, denom df = 241.26, p-value = 0.5781# pH
(aov_ph <- oneway.test(pH~Treatment, tbl_waterData, var.equal=F)) #pH is not different between tanks but is different between the tanks (collectively) and lake##
## One-way analysis of means (not assuming equal variances)
##
## data: pH and Treatment
## F = 55.853, num df = 4.00, denom df = 230.73, p-value < 2.2e-16tbl_waterData_pH <- subset(tbl_waterData, Treatment != "Lake") # Remove the lake readings to see if all tanks are the same
leveneTest(tbl_waterData_pH$pH~tbl_waterData_pH$Treatment) # no differences## Levene's Test for Homogeneity of Variance (center = median)
## Df F value Pr(>F)
## group 3 0.4627 0.7084
## 716(aov_phTanks <- oneway.test(pH~Treatment, tbl_waterData_pH, var.equal=T)) # Tanks are the same##
## One-way analysis of means
##
## data: pH and Treatment
## F = 0.39548, num df = 3, denom df = 716, p-value = 0.7563# Specific conductance
(aov_conductance <- oneway.test(Conductance~Treatment, tbl_waterData, var.equal=T)) #conductance does not vary between the tanks and the lake##
## One-way analysis of means
##
## data: Conductance and Treatment
## F = 0.20291, num df = 4, denom df = 745, p-value = 0.9368(games_howell_test(tbl_waterData, pH~Treatment, conf.level=0.95)) #how pH varies by tank## # A tibble: 10 × 8
## .y. group1 group2 estimate conf.low conf.high p.adj p.adj.signif
## * <chr> <chr> <chr> <dbl> <dbl> <dbl> <dbl> <chr>
## 1 pH Control High 0.0232 -0.0931 0.140 9.82e- 1 ns
## 2 pH Control Lake 0.465 0.351 0.578 3.38e-14 ****
## 3 pH Control Low -0.0192 -0.139 0.100 9.92e- 1 ns
## 4 pH Control Medium -0.0148 -0.134 0.105 9.97e- 1 ns
## 5 pH High Lake 0.441 0.331 0.552 7.09e-14 ****
## 6 pH High Low -0.0424 -0.159 0.0742 8.56e- 1 ns
## 7 pH High Medium -0.0381 -0.155 0.0784 8.98e- 1 ns
## 8 pH Lake Low -0.484 -0.598 -0.370 5.88e-14 ****
## 9 pH Lake Medium -0.480 -0.593 -0.366 3.77e-15 ****
## 10 pH Low Medium 0.00439 -0.115 0.124 1 e+ 0 ns1.3 Total ammonia nitrogen (TAN) in tanks
tbl_tan <- sqlQuery(cnxn_nt, "SELECT Tbl_WaterChemistry_TotalAmmoniaNitrogen.Day, Tbl_WaterChemistry_TotalAmmoniaNitrogen.Flush, Tbl_WaterChemistry_TotalAmmoniaNitrogen.Treatment, Tbl_WaterChemistry_TotalAmmoniaNitrogen.TAN FROM Tbl_WaterChemistry_TotalAmmoniaNitrogen;", stringsAsFactors = FALSE) # Load data into R dataframestbl_tan$Flush <- as.factor(tbl_tan$Flush) #change variables to categorical
tbl_tan$Treatment <- as.factor(tbl_tan$Treatment)
tbl_tan$Day <- as.factor(tbl_tan$Day)group_by(tbl_tan, Treatment, Flush) %>%
summarise(
mean = mean(TAN),
sd = sd(TAN)
)## `summarise()` has grouped output by 'Treatment'. You can override using the
## `.groups` argument.## # A tibble: 8 × 4
## # Groups: Treatment [4]
## Treatment Flush mean sd
## <fct> <fct> <dbl> <dbl>
## 1 Control Post-flush 0.0712 0.0331
## 2 Control Pre-flush 0.304 0.159
## 3 High Post-flush 0.0754 0.0424
## 4 High Pre-flush 0.301 0.204
## 5 Low Post-flush 0.0782 0.0337
## 6 Low Pre-flush 0.318 0.177
## 7 Medium Post-flush 0.0866 0.0481
## 8 Medium Pre-flush 0.332 0.215group_by(tbl_tan, Treatment) %>%
summarise(
mean = mean(TAN),
sd = sd(TAN)
)## # A tibble: 4 × 3
## Treatment mean sd
## <fct> <dbl> <dbl>
## 1 Control 0.211 0.170
## 2 High 0.211 0.194
## 3 Low 0.222 0.182
## 4 Medium 0.234 0.207# Compare variability of different groups to determine if appropriate for ANOVA
leveneTest(tbl_tan$TAN~tbl_tan$Treatment) # Variance does not differ## Levene's Test for Homogeneity of Variance (center = median)
## Df F value Pr(>F)
## group 3 0.0808 0.9703
## 76leveneTest(tbl_tan$TAN~tbl_tan$Flush) # Variance differs## Levene's Test for Homogeneity of Variance (center = median)
## Df F value Pr(>F)
## group 1 26.391 2.005e-06 ***
## 78
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1# Visualize variability differences
boxplot(tbl_tan$TAN~tbl_tan$Treatment)
boxplot(tbl_tan$TAN~tbl_tan$Flush)
(aov_tan_treat <- oneway.test(TAN~Treatment, tbl_tan, var.equal=T)) # No difference between treatments when not accounting for pre- and post-flush##
## One-way analysis of means
##
## data: TAN and Treatment
## F = 0.065478, num df = 3, denom df = 76, p-value = 0.978(aov_tan_flush <- oneway.test(TAN~Flush, tbl_tan, var.equal=F)) # Difference between pre- and post-flush when not accounting for treatments##
## One-way analysis of means (not assuming equal variances)
##
## data: TAN and Flush
## F = 73.669, num df = 1.000, denom df = 52.935, p-value = 1.339e-11(aov_tan <- aov(TAN ~ Treatment + Flush + Treatment:Flush, tbl_tan))## Call:
## aov(formula = TAN ~ Treatment + Flush + Treatment:Flush, data = tbl_tan)
##
## Terms:
## Treatment Flush Treatment:Flush Residuals
## Sum of Squares 0.0069920 1.0674760 0.0009912 1.6367149
## Deg. of Freedom 3 1 3 72
##
## Residual standard error: 0.1507719
## Estimated effects may be unbalancedsummary(aov_tan)## Df Sum Sq Mean Sq F value Pr(>F)
## Treatment 3 0.007 0.0023 0.103 0.958
## Flush 1 1.067 1.0675 46.959 2.06e-09 ***
## Treatment:Flush 3 0.001 0.0003 0.015 0.998
## Residuals 72 1.637 0.0227
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 11.4 Copper analysis
tbl_copper <- sqlQuery(cnxn_nt, "SELECT [Tbl_Copper_ICP].Day, [Tbl_Copper_ICP].Dose, [Tbl_Copper_ICP].[Tank #], [Tbl_Copper_ICP].Treatment, [Tbl_Copper_ICP].[Cu (ppb)]
FROM [Tbl_Copper_ICP];", stringsAsFactors = FALSE) # Load copper data into R dataframes
colnames(tbl_copper) = c("day", "dose", "tank", "treatment", "Cu") # rename columnscolnames(tbl_copper) = c("day", "dose", "tank", "treatment", "Cu") # rename columns
tbl_copper <- subset(tbl_copper, treatment!="Lake") # remove lake measurements
tbl_copper$dose <- as.factor(tbl_copper$dose) # Modify variable type(s)
tbl_copper$treatment <- as.factor(tbl_copper$treatment)
tbl_copper$day <- as.factor(tbl_copper$day)
tbl_copper$tank <- as.factor(tbl_copper$tank)group_by(tbl_copper, dose, treatment) %>%
summarise(
mean = mean(Cu, na.rm=T),
sd = sd(Cu, na.rm=T)
)## `summarise()` has grouped output by 'dose'. You can override using the
## `.groups` argument.## # A tibble: 8 × 4
## # Groups: dose [2]
## dose treatment mean sd
## <fct> <fct> <dbl> <dbl>
## 1 Post-dose Control 6.74 9.31
## 2 Post-dose High 35.0 12.0
## 3 Post-dose Low 9.80 2.36
## 4 Post-dose Medium 23.7 6.36
## 5 Pre-dose Control 6.15 7.16
## 6 Pre-dose High 30.5 9.06
## 7 Pre-dose Low 8.20 1.95
## 8 Pre-dose Medium 20.2 5.78group_by(tbl_copper, treatment) %>%
summarise(
mean = mean(Cu, na.rm=T),
sd = sd(Cu, na.rm=T)
)## # A tibble: 4 × 3
## treatment mean sd
## <fct> <dbl> <dbl>
## 1 Control 6.36 7.64
## 2 High 32.8 10.8
## 3 Low 8.98 2.30
## 4 Medium 22.0 6.30# Compare variability of different groups to determine if appropriate for ANOVA
leveneTest(tbl_copper$Cu~tbl_copper$treatment) # variance differs## Levene's Test for Homogeneity of Variance (center = median)
## Df F value Pr(>F)
## group 3 26.733 9.676e-16 ***
## 389
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1leveneTest(tbl_copper$Cu~tbl_copper$dose) # variance differs## Levene's Test for Homogeneity of Variance (center = median)
## Df F value Pr(>F)
## group 1 6.7953 0.009489 **
## 391
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1# Visualize variability differences
boxplot(tbl_copper$Cu~tbl_copper$treatment)
boxplot(tbl_copper$Cu~tbl_copper$dose)
# ANOVAs (out of curiosity)
(aov_cu_dose <- oneway.test(Cu~dose, tbl_copper, var.equal=F))##
## One-way analysis of means (not assuming equal variances)
##
## data: Cu and dose
## F = 8.6822, num df = 1.00, denom df = 379.21, p-value = 0.003412(aov_cu_treat <- oneway.test(Cu~treatment, tbl_copper, var.equal=F))##
## One-way analysis of means (not assuming equal variances)
##
## data: Cu and treatment
## F = 326.22, num df = 3.000, denom df = 56.028, p-value < 2.2e-16(aov_cu <- aov(Cu ~ treatment + dose + treatment:dose, tbl_copper))## Call:
## aov(formula = Cu ~ treatment + dose + treatment:dose, data = tbl_copper)
##
## Terms:
## treatment dose treatment:dose Residuals
## Sum of Squares 38691.76 951.45 151.41 20248.60
## Deg. of Freedom 3 1 3 385
##
## Residual standard error: 7.252157
## Estimated effects may be unbalanced
## 135 observations deleted due to missingnesssummary(aov_cu)## Df Sum Sq Mean Sq F value Pr(>F)
## treatment 3 38692 12897 245.22 < 2e-16 ***
## dose 1 951 951 18.09 2.65e-05 ***
## treatment:dose 3 151 50 0.96 0.412
## Residuals 385 20249 53
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 135 observations deleted due to missingnesstbl_copper_preDose <- filter(tbl_copper, dose == "Pre-dose")
tbl_copper_postDose <- filter(tbl_copper, dose == "Post-dose")
# Compare variability of different groups to determine if appropriate for ANOVA
leveneTest(tbl_copper_preDose$Cu~tbl_copper_preDose$treatment) # variance differs## Levene's Test for Homogeneity of Variance (center = median)
## Df F value Pr(>F)
## group 3 13.234 6.701e-08 ***
## 195
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1leveneTest(tbl_copper_postDose$Cu~tbl_copper_postDose$treatment) # variance differs## Levene's Test for Homogeneity of Variance (center = median)
## Df F value Pr(>F)
## group 3 8.1089 4.136e-05 ***
## 190
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1# Visualize variability differences
boxplot(tbl_copper_preDose$Cu~tbl_copper_preDose$treatment)
boxplot(tbl_copper_postDose$Cu~tbl_copper_postDose$treatment)
(aov_cu_preDose <- oneway.test(Cu~treatment, tbl_copper_preDose, var.equal=F))##
## One-way analysis of means (not assuming equal variances)
##
## data: Cu and treatment
## F = 185.7, num df = 3.00, denom df = 33.14, p-value < 2.2e-16(aov_cu_postDose <- oneway.test(Cu~treatment, tbl_copper_postDose, var.equal=F))##
## One-way analysis of means (not assuming equal variances)
##
## data: Cu and treatment
## F = 157.58, num df = 3.000, denom df = 18.186, p-value = 3.367e-13(plot_copper <- tbl_copper %>%
ggplot(aes(x=treatment, y=Cu)) +
geom_boxplot(aes(fill=dose)) +
labs(x = NULL) +
theme_classic() +
theme(legend.position = "none") +
fill_palette("jco") +
labs(x="", y="Copper (ppb)")
)## Warning: Removed 135 rows containing non-finite values (`stat_boxplot()`).
1.5 Daphnia (only) survival
tbl_survivalDaphnia <- sqlQuery(cnxn_nt, "SELECT Tbl_Mortality_Daphnia.[Tank #], Tbl_Mortality_Daphnia.Treatment, Tbl_Mortality_Daphnia.[Exposure Day], Tbl_Mortality_Daphnia.Survival FROM Tbl_Mortality_Daphnia;", stringsAsFactors = FALSE) # Load data into R dataframescolnames(tbl_survivalDaphnia) = c("tank", "treatGroup", "treatDay", "survival") # change column names
tbl_survivalDaphnia$treatGroup <- as.factor(tbl_survivalDaphnia$treatGroup) #Make categorical
tbl_survivalDaphnia$treatDay <- as.factor(tbl_survivalDaphnia$treatDay) #Make categorical
tbl_survivalDaphnia$treatGroup <- factor(tbl_survivalDaphnia$treatGroup, levels=c("Control", "Low", "Medium", "High")) #Reorder treatment groups to make figure better
tbl_survivalDaphnia$survival <- round((tbl_survivalDaphnia$survival/100), 2)# Mean survival by treatment group and day
(tbl_survivalDaphnia_summary <- data.frame(
group_by(tbl_survivalDaphnia, treatGroup, treatDay) %>%
summarise(
mean = mean(survival),
sd = sd(survival)
)))## `summarise()` has grouped output by 'treatGroup'. You can override using the
## `.groups` argument.## treatGroup treatDay mean sd
## 1 Control 4 0.5000000 NA
## 2 Control 7 0.6500000 NA
## 3 Control 10 0.6333333 0.1437591
## 4 Control 14 0.6083333 0.1655798
## 5 Low 4 0.7500000 NA
## 6 Low 7 0.7000000 NA
## 7 Low 10 0.7583333 0.1068488
## 8 Low 14 0.6416667 0.1158303
## 9 Medium 4 0.4500000 NA
## 10 Medium 7 0.3500000 NA
## 11 Medium 10 0.1916667 0.3513071
## 12 Medium 14 0.0750000 0.1837117
## 13 High 4 0.0000000 NA
## 14 High 7 0.0000000 NA
## 15 High 10 0.0000000 0.0000000
## 16 High 14 0.0000000 0.0000000# Mean survival by treatment group
(tbl_survivalDaphnia_means <- data.frame(
group_by(tbl_survivalDaphnia, treatGroup) %>%
summarise(
mean = mean(survival),
sd = sd(survival)
)))## treatGroup mean sd
## 1 Control 0.6142857 0.1406422
## 2 Low 0.7035714 0.1134499
## 3 Medium 0.1714286 0.2708351
## 4 High 0.0000000 0.00000001.6 All invertebrate & fish survival
tbl_survivalInverts <- sqlQuery(cnxn_nt, "SELECT qry_mortality_inverts_byTank.[Tank #], qry_mortality_inverts_byTank.Treatment, qry_mortality_inverts_byTank.Species, qry_mortality_inverts_byTank.[Total mortality], [tbl_inverts].[total_pertank]-[qry_mortality_inverts_bytank].[total mortality] AS Survival, Tbl_Inverts.Total_perTank, Round(([tbl_inverts].[total_pertank]-[qry_mortality_inverts_bytank].[total mortality])/[tbl_inverts].[total_pertank]*100,2) AS SurvivalPercent, qry_mortality_inverts_byTank.[Exposure Day] FROM Tbl_Inverts INNER JOIN qry_mortality_inverts_byTank ON (Tbl_Inverts.Species_name = qry_mortality_inverts_byTank.Species) AND (Tbl_Inverts.Treatment_id = qry_mortality_inverts_byTank.Treatment) WHERE ((Not (qry_mortality_inverts_byTank.[Exposure Day])='10'));", stringsAsFactors = FALSE) # Load invert data into R dataframes
tbl_survivalFish <- sqlQuery(cnxn_nt, "SELECT qry_mortality_fish_byTank.[Tank #], qry_mortality_fish_byTank.Treatment, qry_mortality_fish_byTank.Species_code, qry_mortality_fish_byTank.[Total mortality], [tbl_fish].[total_pertank]-[qry_mortality_fish_bytank].[total mortality] AS Survival, Tbl_Fish.Total_perTank, Round(([tbl_fish].[total_pertank]-[qry_mortality_fish_bytank].[total mortality])/[tbl_fish].[total_pertank]*100,2) AS SurvivalPercent FROM qry_mortality_fish_byTank INNER JOIN Tbl_Fish ON (qry_mortality_fish_byTank.Species_code = Tbl_Fish.Species_code) AND (qry_mortality_fish_byTank.Treatment = Tbl_Fish.Treatment_id);", stringsAsFactors = FALSE) # Load fish data into R dataframescolnames(tbl_survivalInverts) = c("tank", "treatGroup", "species", "cumMort", "survivalNum", "total", "survival", "Day") #remame columns
tbl_survivalInverts <- subset(tbl_survivalInverts, select=-(Day)) #remove day as a column (all inverts are day 14, end of study)
colnames(tbl_survivalFish) = c("tank", "treatGroup", "species", "cumMort", "survivalNum", "total", "survival") #rename columns
tbl_survivalFish <- subset(tbl_survivalFish, species!="WAE") #remove walleyetbl_survivalData <- rbind(tbl_survivalInverts, tbl_survivalFish) #combine fish and invert tables
tbl_survivalData <- subset(tbl_survivalData, select=-(cumMort)) #remove column
tbl_survivalData <- subset(tbl_survivalData, select=-(survivalNum)) #remove column
tbl_survivalData$survival <- round((tbl_survivalData$survival/100), 2) #round values
tbl_survivalDaphnia_end <- subset(tbl_survivalDaphnia, treatDay=="14") # Only going to compare end-of-study survival
tbl_survivalDaphnia_end <- subset(tbl_survivalDaphnia_end, select=-(treatDay)) # remove treatment day
tbl_survivalDaphnia_end <- data.frame(append(tbl_survivalDaphnia_end, c(species="Daphnia"), after=1)) # add species name
tbl_survivalDaphnia_end <- data.frame(append(tbl_survivalDaphnia_end, c(total="20"), after=2))
tbl_survivalData <- rbind(tbl_survivalData, tbl_survivalDaphnia_end)# add daphnia survival to master survival table
tbl_survivalData$species <- as.factor(tbl_survivalData$species) #Make categorical
tbl_survivalData$treatGroup <- as.factor(tbl_survivalData$treatGroup) #Make categorical
tbl_survivalData$treatGroup <- gsub("Control", "Ctrl", tbl_survivalData$treatGroup) #for consistency with Daphnia
tbl_survivalData$treatGroup <- gsub("Medium", "Med", tbl_survivalData$treatGroup) #for consistency with Daphnia
tbl_survivalData$treatGroup <- factor(tbl_survivalData$treatGroup, levels=c("Ctrl", "Low", "Med", "High")) #Reorder treatment groups to make figure better
tbl_survivalData$species <- factor(tbl_survivalData$species, levels=c("Fatmucket", "Snail", "ZM", "BLG", "FHM", "LMB", "Daphnia")) #Reorder treatment groups to make figure better
tbl_survivalData$species <- factor(tbl_survivalData$species, levels=c("ZM", "Daphnia", "Fatmucket", "Snail", "LMB", "BLG", "FHM")) #Reorder treatment groups to make figure better
tbl_survivalData$total <- as.numeric(tbl_survivalData$total)(group_by(tbl_survivalData, treatGroup, species) %>%
summarise(
mean = mean(survival),
sd = sd(survival)
))## `summarise()` has grouped output by 'treatGroup'. You can override using the
## `.groups` argument.## # A tibble: 28 × 4
## # Groups: treatGroup [4]
## treatGroup species mean sd
## <fct> <fct> <dbl> <dbl>
## 1 Ctrl ZM 0.993 0.0163
## 2 Ctrl Daphnia 0.608 0.166
## 3 Ctrl Fatmucket 1 0
## 4 Ctrl Snail 0.967 0.0516
## 5 Ctrl LMB 0.91 0.156
## 6 Ctrl BLG 1 0
## 7 Ctrl FHM 1 0
## 8 Low ZM 1 0
## 9 Low Daphnia 0.642 0.116
## 10 Low Fatmucket 1 0
## # … with 18 more rows# Subset data
tbl_survivalData_zm <- subset(tbl_survivalData, species=="ZM")
tbl_survivalData_daph <- subset(tbl_survivalData, species=="Daphnia")
tbl_survivalData_bms <- subset(tbl_survivalData, species=="Snail")
tbl_survivalData_lmb <- subset(tbl_survivalData, species=="LMB")
tbl_survivalData_blg <- subset(tbl_survivalData, species=="BLG")
tbl_survivalData_fhm <- subset(tbl_survivalData, species=="FHM")
# Compare variability of different groups to determine if appropriate for ANOVA
leveneTest(tbl_survivalData_zm$survival~tbl_survivalData_zm$treatGroup) # variance differs## Levene's Test for Homogeneity of Variance (center = median)
## Df F value Pr(>F)
## group 3 4.065 0.02086 *
## 20
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1boxplot(tbl_survivalData_zm$survival~tbl_survivalData_zm$treatGroup) # Visualize variability differences
hist(tbl_survivalData_zm$survival~tbl_survivalData_zm$treatGroup) # Consider outliers (to get big picture)
leveneTest(tbl_survivalData_daph$survival~tbl_survivalData_daph$treatGroup) # variance does not differ## Levene's Test for Homogeneity of Variance (center = median)
## Df F value Pr(>F)
## group 3 1.4452 0.2594
## 20boxplot(tbl_survivalData_daph$survival~tbl_survivalData_daph$treatGroup) # Visualize variability differences
hist(tbl_survivalData_daph$survival~tbl_survivalData_daph$treatGroup) # Consider outliers (to get big picture)
leveneTest(tbl_survivalData_bms$survival~tbl_survivalData_bms$treatGroup) # variance differs## Levene's Test for Homogeneity of Variance (center = median)
## Df F value Pr(>F)
## group 3 5.1979 0.008111 **
## 20
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1boxplot(tbl_survivalData_bms$survival~tbl_survivalData_bms$treatGroup) # Visualize variability differences
hist(tbl_survivalData_bms$survival~tbl_survivalData_bms$treatGroup) # Consider outliers (to get big picture)
leveneTest(tbl_survivalData_lmb$survival~tbl_survivalData_lmb$treatGroup) # variance does not differ## Levene's Test for Homogeneity of Variance (center = median)
## Df F value Pr(>F)
## group 3 0.3592 0.7842
## 8boxplot(tbl_survivalData_lmb$survival~tbl_survivalData_lmb$treatGroup) # Visualize variability differences
hist(tbl_survivalData_lmb$survival~tbl_survivalData_lmb$treatGroup) # Consider outliers (to get big picture)
leveneTest(tbl_survivalData_blg$survival~tbl_survivalData_blg$treatGroup) # variance does not differ## Levene's Test for Homogeneity of Variance (center = median)
## Df F value Pr(>F)
## group 3 1 0.4411
## 8boxplot(tbl_survivalData_blg$survival~tbl_survivalData_blg$treatGroup) # Visualize variability differences
hist(tbl_survivalData_blg$survival~tbl_survivalData_blg$treatGroup) # Consider outliers (to get big picture)
leveneTest(tbl_survivalData_fhm$survival~tbl_survivalData_fhm$treatGroup) # variance does not differ## Levene's Test for Homogeneity of Variance (center = median)
## Df F value Pr(>F)
## group 3 0.9 0.4823
## 8boxplot(tbl_survivalData_fhm$survival~tbl_survivalData_fhm$treatGroup) # Visualize variability differences
hist(tbl_survivalData_fhm$survival~tbl_survivalData_fhm$treatGroup) # Consider outliers (to get big picture)
# Zebra mussels
aov_zm <- oneway.test(survival~treatGroup, tbl_survivalData_zm, var.equal=F)
gamesHowell_zm <- tbl_survivalData_zm %>% games_howell_test(survival~treatGroup) #Games-Howell test since inequal variances
# Daphnia
aov_daph <- aov(survival~treatGroup, tbl_survivalData_daph)
summary(aov_daph)## Df Sum Sq Mean Sq F value Pr(>F)
## treatGroup 3 2.0911 0.6970 37.38 2.17e-08 ***
## Residuals 20 0.3729 0.0186
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1(tukey_daph <- TukeyHSD(aov_daph)) # Tukey test since equal variances## Tukey multiple comparisons of means
## 95% family-wise confidence level
##
## Fit: aov(formula = survival ~ treatGroup, data = tbl_survivalData_daph)
##
## $treatGroup
## diff lwr upr p adj
## Low-Ctrl 0.03333333 -0.1873265 0.2539932 0.9739120
## Med-Ctrl -0.53333333 -0.7539932 -0.3126735 0.0000079
## High-Ctrl -0.60833333 -0.8289932 -0.3876735 0.0000011
## Med-Low -0.56666667 -0.7873265 -0.3460068 0.0000033
## High-Low -0.64166667 -0.8623265 -0.4210068 0.0000005
## High-Med -0.07500000 -0.2956598 0.1456598 0.7778685# Banded mystery snails
aov_bms <- oneway.test(survival~treatGroup, tbl_survivalData_bms, var.equal=F)
gamesHowell_bms <- tbl_survivalData_bms %>% games_howell_test(survival~treatGroup) #Games-Howell test since inequal variances
# Largemouth bass
aov_lmb <- aov(survival~treatGroup, tbl_survivalData_lmb)
summary(aov_lmb)## Df Sum Sq Mean Sq F value Pr(>F)
## treatGroup 3 0.0246 0.00820 0.296 0.827
## Residuals 8 0.2217 0.02771(tukey_lmb <- TukeyHSD(aov_lmb)) # Tukey test since equal variances## Tukey multiple comparisons of means
## 95% family-wise confidence level
##
## Fit: aov(formula = survival ~ treatGroup, data = tbl_survivalData_lmb)
##
## $treatGroup
## diff lwr upr p adj
## Low-Ctrl -0.11000000 -0.5452398 0.3252398 0.8484462
## Med-Ctrl -0.11000000 -0.5452398 0.3252398 0.8484462
## High-Ctrl -0.08666667 -0.5219065 0.3485732 0.9169023
## Med-Low 0.00000000 -0.4352398 0.4352398 1.0000000
## High-Low 0.02333333 -0.4119065 0.4585732 0.9980479
## High-Med 0.02333333 -0.4119065 0.4585732 0.9980479# Bluegill
aov_blg <- aov(survival~treatGroup, tbl_survivalData_blg)
summary(aov_blg)## Df Sum Sq Mean Sq F value Pr(>F)
## treatGroup 3 0.002500 0.0008333 1 0.441
## Residuals 8 0.006667 0.0008333(tukey_blg <- TukeyHSD(aov_blg)) # Tukey test since equal variances## Tukey multiple comparisons of means
## 95% family-wise confidence level
##
## Fit: aov(formula = survival ~ treatGroup, data = tbl_survivalData_blg)
##
## $treatGroup
## diff lwr upr p adj
## Low-Ctrl -3.333333e-02 -0.10881349 0.04214683 0.5252407
## Med-Ctrl -6.661338e-16 -0.07548016 0.07548016 1.0000000
## High-Ctrl -6.661338e-16 -0.07548016 0.07548016 1.0000000
## Med-Low 3.333333e-02 -0.04214683 0.10881349 0.5252407
## High-Low 3.333333e-02 -0.04214683 0.10881349 0.5252407
## High-Med 0.000000e+00 -0.07548016 0.07548016 1.0000000# Fathead minnow
aov_fhm <- aov(survival~treatGroup, tbl_survivalData_fhm, )
summary(aov_blg)## Df Sum Sq Mean Sq F value Pr(>F)
## treatGroup 3 0.002500 0.0008333 1 0.441
## Residuals 8 0.006667 0.0008333(tukey_fhm <- TukeyHSD(aov_fhm)) # Tukey test since equal variances## Tukey multiple comparisons of means
## 95% family-wise confidence level
##
## Fit: aov(formula = survival ~ treatGroup, data = tbl_survivalData_fhm)
##
## $treatGroup
## diff lwr upr p adj
## Low-Ctrl -1.000000e-01 -0.3614709 0.1614709 0.6297636
## Med-Ctrl -4.440892e-16 -0.2614709 0.2614709 1.0000000
## High-Ctrl -1.000000e-01 -0.3614709 0.1614709 0.6297636
## Med-Low 1.000000e-01 -0.1614709 0.3614709 0.6297636
## High-Low 0.000000e+00 -0.2614709 0.2614709 1.0000000
## High-Med -1.000000e-01 -0.3614709 0.1614709 0.62976361.7 GLM
1.7.1 Modify data
tbl_survivalData <- subset(tbl_survivalData, species!="Fatmucket") #Fatmuckets survived in every case in this trial, so we will omit them from this processtbl_waterDataAvgs <- sqlQuery(cnxn_nt, "SELECT lu_treatments.Treatment, Tbl_WaterChemistry_Avgs.[Avg Temp], Tbl_WaterChemistry_Avgs.[Avg DO], Tbl_WaterChemistry_Avgs.[Avg pH], Tbl_WaterChemistry_Avgs.[Avg Conductance] FROM lu_treatments INNER JOIN Tbl_WaterChemistry_Avgs ON lu_treatments.Treatments_ID = Tbl_WaterChemistry_Avgs.Treatment_id;", stringsAsFactors = FALSE) # Load invert data into R dataframes
colnames(tbl_waterDataAvgs) = c("treatGroup", "temp", "DO", "pH", "specCond") # rename columns
tbl_waterDataAvgs$treatGroup <- as.factor(tbl_waterDataAvgs$treatGroup)
tbl_waterDataAvgs$treatGroup <- gsub("Control", "Ctrl", tbl_waterDataAvgs$treatGroup) #for consistency
tbl_waterDataAvgs$treatGroup <- gsub("Medium", "Med", tbl_waterDataAvgs$treatGroup) #for consistency
summary(tbl_waterDataAvgs)## treatGroup temp DO pH
## Length:5 Min. :23.73 Min. :8.31 Min. :8.120
## Class :character 1st Qu.:23.74 1st Qu.:8.39 1st Qu.:8.120
## Mode :character Median :23.78 Median :8.46 Median :8.130
## Mean :23.89 Mean :8.43 Mean :8.226
## 3rd Qu.:23.82 3rd Qu.:8.48 3rd Qu.:8.160
## Max. :24.38 Max. :8.51 Max. :8.600
## specCond
## Min. :167.3
## 1st Qu.:170.8
## Median :170.9
## Mean :170.3
## 3rd Qu.:171.2
## Max. :171.41.7.2 Explore variables that might affect survival
tbl_survivalWaterChem <- merge(tbl_waterDataAvgs, tbl_survivalData, by = "treatGroup") # A quick scan of the resulting table indicates the merge worked correctly, woohoo! Also: the number of rows match the original number in tbl_survivalData, a good thing
# Temperature
(plot_survival_temp <- tbl_survivalWaterChem %>%
ggplot(aes(x=temp, y=survival)) +
geom_point(aes(color=treatGroup), varwidth = TRUE) +
labs(x = NULL) +
facet_wrap(~species, nrow=2) +
ylim(0,1) +
theme_classic() +
theme(legend.position = "none") +
fill_palette("jco") +
geom_smooth(method = "lm") +
labs(x="", y="Survival")) # Do not need to include temperature## Warning in geom_point(aes(color = treatGroup), varwidth = TRUE): Ignoring
## unknown parameters: `varwidth`## `geom_smooth()` using formula = 'y ~ x'## Warning: Removed 37 rows containing missing values (`geom_smooth()`).## Warning in max(ids, na.rm = TRUE): no non-missing arguments to max; returning
## -Inf
## Warning in max(ids, na.rm = TRUE): no non-missing arguments to max; returning
## -Inf
# Dissolved oxygen
(plot_survival_DO <- tbl_survivalWaterChem %>%
ggplot(aes(x=DO, y=survival)) +
geom_point(aes(color=treatGroup), varwidth = TRUE) +
labs(x = NULL) +
facet_wrap(~species, nrow=2) +
ylim(0,1) +
theme_classic() +
theme(legend.position = "none") +
fill_palette("jco") +
geom_smooth(method = "lm") +
labs(x="", y="Survival")) # Do not need to include DO## Warning in geom_point(aes(color = treatGroup), varwidth = TRUE): Ignoring
## unknown parameters: `varwidth`## `geom_smooth()` using formula = 'y ~ x'## Warning: Removed 10 rows containing missing values (`geom_smooth()`).
## no non-missing arguments to max; returning -Inf
## no non-missing arguments to max; returning -Inf
# pH
(plot_survival_pH <- tbl_survivalWaterChem %>%
ggplot(aes(x=pH, y=survival)) +
geom_point(aes(color=treatGroup), varwidth = TRUE) +
labs(x = NULL) +
facet_wrap(~species, nrow=2) +
ylim(0,1) +
theme_classic() +
theme(legend.position = "none") +
fill_palette("jco") +
geom_smooth(method = "lm") +
labs(x="", y="Survival")) # Do not need to include pH## Warning in geom_point(aes(color = treatGroup), varwidth = TRUE): Ignoring
## unknown parameters: `varwidth`## `geom_smooth()` using formula = 'y ~ x'## Warning: Removed 14 rows containing missing values (`geom_smooth()`).
## no non-missing arguments to max; returning -Inf
## no non-missing arguments to max; returning -Inf
# Specific conductance
(plot_survival_specCond <- tbl_survivalWaterChem %>%
ggplot(aes(x=specCond, y=survival)) +
geom_point(aes(color=treatGroup), varwidth = TRUE) +
labs(x = NULL) +
facet_wrap(~species, nrow=2) +
ylim(0,1) +
theme_classic() +
theme(legend.position = "none") +
fill_palette("jco") +
geom_smooth(method = "lm") +
labs(x="", y="Survival")) # Do not need to include specific conductance## Warning in geom_point(aes(color = treatGroup), varwidth = TRUE): Ignoring
## unknown parameters: `varwidth`## `geom_smooth()` using formula = 'y ~ x'## Warning: Removed 28 rows containing missing values (`geom_smooth()`).
## no non-missing arguments to max; returning -Inf
## no non-missing arguments to max; returning -Inf
# Does tank row (top or bottom) matter?
tbl_survivalData$tankRow = NA
tbl_survivalData$tankRow[tbl_survivalData$tank %in% 1:12] <- "Top"
tbl_survivalData$tankRow[tbl_survivalData$tank %in% 13:24] <- "Bottom"
(plot_survival_tankRow <- tbl_survivalData %>%
ggplot(aes(x=tankRow, y=survival)) +
geom_boxplot(aes(fill=treatGroup), varwidth = TRUE) +
labs(x = NULL) +
facet_wrap(~species, nrow=2) +
ylim(0,1) +
theme_classic() +
theme(legend.position = "none") +
fill_palette("jco") +
labs(x="", y="Survival"))
# Probably don't need to include whether a tank was on the top or bottom row# Does tank location (which side of the mobile lab a tank was on) matter?
tbl_survivalData$tankPos = NA
tbl_survivalData$tankPos[tbl_survivalData$tank %in% 1:12] <- tbl_survivalData$tank[tbl_survivalData$tank %in% 1:12]
tbl_survivalData$tankPos[tbl_survivalData$tank %in% 13:24] <- tbl_survivalData$tank[tbl_survivalData$tank %in% 13:24] - 12
(plot_survival_tankPos <- tbl_survivalData %>%
ggplot(aes(x=tankPos, y=survival)) +
geom_point(aes(color=treatGroup)) +
labs(x = NULL) +
facet_wrap(~species, nrow=2) +
ylim(0,1) +
theme_classic() +
theme(legend.position = "none") +
fill_palette("jco") +
labs(x="", y="Survival")) +
geom_smooth(method = "lm")## `geom_smooth()` using formula = 'y ~ x'## Warning: Removed 25 rows containing missing values (`geom_smooth()`).## Warning in max(ids, na.rm = TRUE): no non-missing arguments to max; returning
## -Inf
# Probably don't need to include which side of the mobile lab the tank was located on (or where in that gradient it fell)# Does the exact mix of fish matter?
tbl_survivalData$tankFish = NA
tbl_survivalData$tankFish[tbl_survivalData$tank %in% c(15, 7, 8, 14, 13, 21)] <- 1 # tanks with blg + lmb
tbl_survivalData$tankFish[tbl_survivalData$tank %in% c(19, 20, 6, 24, 3, 22)] <- 2 # tanks with blg + wae
tbl_survivalData$tankFish[tbl_survivalData$tank %in% c(18, 17, 2, 4, 23, 16)] <- 3 # tanks with fhm + lmb
tbl_survivalData$tankFish[tbl_survivalData$tank %in% c(5, 9, 10, 1, 12, 11)] <- 4 # tanks with fhm + wae
tbl_survivalData$tankFish <- as.factor(tbl_survivalData$tankFish) #Make categorical
(plot_survival_tankFish <- tbl_survivalData %>%
ggplot(aes(x=tankFish, y=survival)) +
geom_boxplot(aes(fill=treatGroup)) +
labs(x = NULL) +
ylim(0,1) +
theme_classic() +
theme(legend.position = "none") +
fill_palette("jco") +
labs(x="", y="Survival"))
# Probably don't need to include the exact mix of fish in each tank
# Separating out BLG vs. FHM
tbl_survivalData$BLGvFHM = NA
tbl_survivalData$BLGvFHM[tbl_survivalData$tank %in% c(15, 7, 8, 14, 13, 21, 19, 20, 6, 24, 3, 22)] <- 1 # tanks with BLG
tbl_survivalData$BLGvFHM[tbl_survivalData$tank %in% c(18, 17, 2, 4, 23, 16, 5, 9, 10, 1, 12, 11)] <- 2 # tanks with FHM
tbl_survivalData$BLGvFHM <- as.factor(tbl_survivalData$BLGvFHM) #Make categorical
(plot_survival_BLGvFHM <- tbl_survivalData %>%
ggplot(aes(x=BLGvFHM, y=survival)) +
geom_boxplot(aes(fill=treatGroup)) +
labs(x = NULL) +
ylim(0,1) +
theme_classic() +
theme(legend.position = "none") +
fill_palette("jco") +
labs(x="", y="Survival"))
# Probably don't need to include BLG vs FHM1.7.2.5 Other factors
Other factors considered: time frame (don’t think it’s meaningful because of species-specificity), location in the tank (only would apply to fish, and we’re already not using data from WAE, so likely not that helpful)
1.7.3 Modify the data structure
# First,we need to stretch our data out
entiretankcol = integer(0) #Make an empty space that stores numbers
entiretreatgroup = character(0) #Make an empty space that stores text
entirespeciesgroup = character(0)
entiresurvival = integer(0)
for(i in 1:nrow(tbl_survivalWaterChem)) {
# Make a pool of values from 1 to nrow(data) and let i equal each value in that pool one at a time until it has run out
# print(i) # Show me what i equals this time
# Use the value in this row of total to stretch the value in this row of tank a number of times equal to the number of individuals we had:
tanklong = rep(tbl_survivalWaterChem$tank[i], each= as.numeric(tbl_survivalWaterChem$total[i]))
#print(length(tanklong)) # Show me the length of what I made this time
treatgrouplong = rep(as.character(tbl_survivalWaterChem$treatGroup[i]), each = as.numeric(tbl_survivalWaterChem$total[i]))
speciesgrouplong = rep(as.character(tbl_survivalWaterChem$species[i]), each = as.numeric(tbl_survivalWaterChem$total[i]))
# let's do this one step at a time:
times1 = round(tbl_survivalWaterChem$survival[i] * as.numeric(tbl_survivalWaterChem$total[i])) # For this row, multiple survival value * total value to get number of organisms that survived and round to ensure it's a whole number
times2 = round(((1-tbl_survivalWaterChem$survival[i]) * as.numeric(tbl_survivalWaterChem$total[i]))) # Get the inverse of that using some subtraction
tmp1 = rep(1, times = times1) # Store these values so that we can use them in the times arguments of rep
tmp2 = rep(0, each = times2)
survivallong = c( # Stick together 1s = to # of survivals and 0s = # of non-survivals
rep(1, times = times1),
rep(x=0, times = times2)
)
entiretankcol = c(entiretankcol, tanklong) # Stick what we've made together with what we've made in all the previous loops
entiretreatgroup = c(entiretreatgroup, treatgrouplong)
entirespeciesgroup = c(entirespeciesgroup, speciesgrouplong)
entiresurvival = c(entiresurvival, survivallong)
#print(length(entiresurvival)) # To debug a for loop, print thing to see if they look like they should.
# i is an object that takes on each value in the pool (right side of the for call) one at a time as we move through the loops. so first time through, i = 1, second time through i = 2 .... which means I can use i as a "what is the current row?"
}
#Put it all together! left of = is the name of the new column, right of = is what is getting stored in it.
tbl_survivalDataLong = data.frame(tank = entiretankcol,
treatGroup= entiretreatgroup,
species = entirespeciesgroup,
survival = entiresurvival)# Note: ultimately, we opted to use more specific copper concentrations than the treatGroup number (see below), but this work led up to that
mod1 = glm(survival ~ species + treatGroup + species*treatGroup, family = "binomial", data = tbl_survivalDataLong)
summary(mod1)##
## Call:
## glm(formula = survival ~ species + treatGroup + species * treatGroup,
## family = "binomial", data = tbl_survivalDataLong)
##
## Deviance Residuals:
## Min 1Q Median 3Q Max
## -3.1656 0.0001 0.1638 0.4590 2.2761
##
## Coefficients:
## Estimate Std. Error z value Pr(>|z|)
## (Intercept) 1.957e+01 1.963e+03 0.010 0.992
## speciesDaphnia -1.913e+01 1.963e+03 -0.010 0.992
## speciesFHM 2.250e-08 2.777e+03 0.000 1.000
## speciesLMB -1.724e+01 1.963e+03 -0.009 0.993
## speciesSnail -1.620e+01 1.963e+03 -0.008 0.993
## speciesZM -1.456e+01 1.963e+03 -0.007 0.994
## treatGroupHigh 5.825e-08 2.777e+03 0.000 1.000
## treatGroupLow -1.620e+01 1.963e+03 -0.008 0.993
## treatGroupMed 2.626e-08 2.777e+03 0.000 1.000
## speciesDaphnia:treatGroupHigh -2.001e+01 2.945e+03 -0.007 0.995
## speciesFHM:treatGroupHigh -1.737e+01 3.401e+03 -0.005 0.996
## speciesLMB:treatGroupHigh -7.958e-01 2.777e+03 0.000 1.000
## speciesSnail:treatGroupHigh -3.434e+00 2.777e+03 -0.001 0.999
## speciesZM:treatGroupHigh -2.947e+00 2.777e+03 -0.001 0.999
## speciesDaphnia:treatGroupLow 1.634e+01 1.963e+03 0.008 0.993
## speciesFHM:treatGroupLow -1.170e+00 2.777e+03 0.000 1.000
## speciesLMB:treatGroupLow 1.526e+01 1.963e+03 0.008 0.994
## speciesSnail:treatGroupLow 1.691e+01 1.963e+03 0.009 0.993
## speciesZM:treatGroupLow 3.076e+01 2.151e+03 0.014 0.989
## speciesDaphnia:treatGroupMed -2.953e+00 2.777e+03 -0.001 0.999
## speciesFHM:treatGroupMed -1.767e-08 3.927e+03 0.000 1.000
## speciesLMB:treatGroupMed -9.410e-01 2.777e+03 0.000 1.000
## speciesSnail:treatGroupMed -4.229e-01 2.777e+03 0.000 1.000
## speciesZM:treatGroupMed -6.999e-01 2.777e+03 0.000 1.000
##
## (Dispersion parameter for binomial family taken to be 1)
##
## Null deviance: 1911.74 on 1739 degrees of freedom
## Residual deviance: 862.09 on 1716 degrees of freedom
## AIC: 910.09
##
## Number of Fisher Scoring iterations: 18mod2 = glm(survival ~ species + treatGroup, family = "binomial", data = tbl_survivalDataLong)
summary(mod2)##
## Call:
## glm(formula = survival ~ species + treatGroup, family = "binomial",
## data = tbl_survivalDataLong)
##
## Deviance Residuals:
## Min 1Q Median 3Q Max
## -3.7332 0.0415 0.2162 0.4537 1.9533
##
## Coefficients:
## Estimate Std. Error z value Pr(>|z|)
## (Intercept) 7.05871 1.04240 6.772 1.27e-11 ***
## speciesDaphnia -6.74641 1.03199 -6.537 6.27e-11 ***
## speciesFHM -1.89655 1.09923 -1.725 0.084467 .
## speciesLMB -3.43006 1.03531 -3.313 0.000923 ***
## speciesSnail -3.31421 1.02997 -3.218 0.001292 **
## speciesZM -1.44838 1.03643 -1.397 0.162274
## treatGroupHigh -3.38842 0.29197 -11.605 < 2e-16 ***
## treatGroupLow -0.09135 0.24682 -0.370 0.711315
## treatGroupMed -2.05924 0.26593 -7.744 9.66e-15 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## (Dispersion parameter for binomial family taken to be 1)
##
## Null deviance: 1911.74 on 1739 degrees of freedom
## Residual deviance: 958.13 on 1731 degrees of freedom
## AIC: 976.13
##
## Number of Fisher Scoring iterations: 7tbl_survivalData$treatGroup <- gsub("Ctrl", 0, tbl_survivalData$treatGroup) #instead of categories, convert treatments to dosed values
tbl_survivalData$treatGroup <- gsub("Low", 10, tbl_survivalData$treatGroup)
tbl_survivalData$treatGroup <- gsub("Med", 25, tbl_survivalData$treatGroup)
tbl_survivalData$treatGroup <- gsub("High", 40, tbl_survivalData$treatGroup)
tbl_survivalData$treatGroup <- as.numeric(tbl_survivalData$treatGroup)
mod3 = glm(survival ~ species + treatGroup + species*treatGroup, family = "binomial", data = tbl_survivalDataLong)
summary(mod3)##
## Call:
## glm(formula = survival ~ species + treatGroup + species * treatGroup,
## family = "binomial", data = tbl_survivalDataLong)
##
## Deviance Residuals:
## Min 1Q Median 3Q Max
## -3.1656 0.0001 0.1638 0.4590 2.2761
##
## Coefficients:
## Estimate Std. Error z value Pr(>|z|)
## (Intercept) 1.957e+01 1.963e+03 0.010 0.992
## speciesDaphnia -1.913e+01 1.963e+03 -0.010 0.992
## speciesFHM 2.250e-08 2.777e+03 0.000 1.000
## speciesLMB -1.724e+01 1.963e+03 -0.009 0.993
## speciesSnail -1.620e+01 1.963e+03 -0.008 0.993
## speciesZM -1.456e+01 1.963e+03 -0.007 0.994
## treatGroupHigh 5.825e-08 2.777e+03 0.000 1.000
## treatGroupLow -1.620e+01 1.963e+03 -0.008 0.993
## treatGroupMed 2.626e-08 2.777e+03 0.000 1.000
## speciesDaphnia:treatGroupHigh -2.001e+01 2.945e+03 -0.007 0.995
## speciesFHM:treatGroupHigh -1.737e+01 3.401e+03 -0.005 0.996
## speciesLMB:treatGroupHigh -7.958e-01 2.777e+03 0.000 1.000
## speciesSnail:treatGroupHigh -3.434e+00 2.777e+03 -0.001 0.999
## speciesZM:treatGroupHigh -2.947e+00 2.777e+03 -0.001 0.999
## speciesDaphnia:treatGroupLow 1.634e+01 1.963e+03 0.008 0.993
## speciesFHM:treatGroupLow -1.170e+00 2.777e+03 0.000 1.000
## speciesLMB:treatGroupLow 1.526e+01 1.963e+03 0.008 0.994
## speciesSnail:treatGroupLow 1.691e+01 1.963e+03 0.009 0.993
## speciesZM:treatGroupLow 3.076e+01 2.151e+03 0.014 0.989
## speciesDaphnia:treatGroupMed -2.953e+00 2.777e+03 -0.001 0.999
## speciesFHM:treatGroupMed -1.767e-08 3.927e+03 0.000 1.000
## speciesLMB:treatGroupMed -9.410e-01 2.777e+03 0.000 1.000
## speciesSnail:treatGroupMed -4.229e-01 2.777e+03 0.000 1.000
## speciesZM:treatGroupMed -6.999e-01 2.777e+03 0.000 1.000
##
## (Dispersion parameter for binomial family taken to be 1)
##
## Null deviance: 1911.74 on 1739 degrees of freedom
## Residual deviance: 862.09 on 1716 degrees of freedom
## AIC: 910.09
##
## Number of Fisher Scoring iterations: 18mod4 = glm(survival ~ species + treatGroup, family = "binomial", tbl_survivalDataLong)
summary(mod4)##
## Call:
## glm(formula = survival ~ species + treatGroup, family = "binomial",
## data = tbl_survivalDataLong)
##
## Deviance Residuals:
## Min 1Q Median 3Q Max
## -3.7332 0.0415 0.2162 0.4537 1.9533
##
## Coefficients:
## Estimate Std. Error z value Pr(>|z|)
## (Intercept) 7.05871 1.04240 6.772 1.27e-11 ***
## speciesDaphnia -6.74641 1.03199 -6.537 6.27e-11 ***
## speciesFHM -1.89655 1.09923 -1.725 0.084467 .
## speciesLMB -3.43006 1.03531 -3.313 0.000923 ***
## speciesSnail -3.31421 1.02997 -3.218 0.001292 **
## speciesZM -1.44838 1.03643 -1.397 0.162274
## treatGroupHigh -3.38842 0.29197 -11.605 < 2e-16 ***
## treatGroupLow -0.09135 0.24682 -0.370 0.711315
## treatGroupMed -2.05924 0.26593 -7.744 9.66e-15 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## (Dispersion parameter for binomial family taken to be 1)
##
## Null deviance: 1911.74 on 1739 degrees of freedom
## Residual deviance: 958.13 on 1731 degrees of freedom
## AIC: 976.13
##
## Number of Fisher Scoring iterations: 7tbl_copperMod = tbl_copper%>%
group_by(dose, treatment, tank) %>%
summarize(avg.Cu = mean(Cu, na.rm=TRUE)) %>%
arrange(tank) %>%
filter(dose=="Post-dose") %>%
ungroup() %>%
select(tank, avg.Cu)## `summarise()` has grouped output by 'dose', 'treatment'. You can override using
## the `.groups` argument.tbl_copperMod$tank = as.numeric(as.character(tbl_copperMod$tank))
tbl_copperMod## # A tibble: 24 × 2
## tank avg.Cu
## <dbl> <dbl>
## 1 1 24.0
## 2 2 9.49
## 3 3 10.1
## 4 4 30.1
## 5 5 2.27
## 6 6 32.9
## 7 7 13.1
## 8 8 21.4
## 9 9 24.3
## 10 10 34.0
## # … with 14 more rowstbl_copperMod$avg.Cu[tbl_copperMod$tank == 12] = 3.995
tbl_copperMod$avg.Cu[tbl_copperMod$tank == 16] = (2.573+5.040)/2
tbl_copperMod$avg.Cu[tbl_copperMod$tank == 14] = mean(tbl_copperMod$avg.Cu[tbl_copperMod$tank %in% c(5,7,12,16,22)])tbl_allDataLong = left_join(tbl_survivalDataLong, tbl_copperMod, by="tank")
tbl_allDataLong$species <- as.factor(tbl_allDataLong$species) #Make categorical# Mean survival by avgCu and species
(tbl_survival_byCu <- data.frame(
group_by(tbl_allDataLong, species, avg.Cu) %>%
summarise(
mean = mean(survival),
sd = sd(survival)
)))## `summarise()` has grouped output by 'species'. You can override using the
## `.groups` argument.## species avg.Cu mean sd
## 1 BLG 2.965691 1.0000000 0.0000000
## 2 BLG 5.227892 1.0000000 0.0000000
## 3 BLG 9.645886 0.9000000 0.3162278
## 4 BLG 9.700738 1.0000000 0.0000000
## 5 BLG 10.138460 1.0000000 0.0000000
## 6 BLG 13.097929 1.0000000 0.0000000
## 7 BLG 21.376097 1.0000000 0.0000000
## 8 BLG 24.137183 1.0000000 0.0000000
## 9 BLG 25.211172 1.0000000 0.0000000
## 10 BLG 32.927156 1.0000000 0.0000000
## 11 BLG 37.686515 1.0000000 0.0000000
## 12 BLG 38.245350 1.0000000 0.0000000
## 13 Daphnia 2.274342 0.3500000 0.4893605
## 14 Daphnia 2.965691 0.5000000 0.5129892
## 15 Daphnia 3.806500 0.8000000 0.4103913
## 16 Daphnia 3.995000 0.7500000 0.4442617
## 17 Daphnia 5.227892 0.6500000 0.4893605
## 18 Daphnia 9.489672 0.8000000 0.4103913
## 19 Daphnia 9.528870 0.6000000 0.5026247
## 20 Daphnia 9.645886 0.5500000 0.5104178
## 21 Daphnia 9.700738 0.5000000 0.5129892
## 22 Daphnia 10.138460 0.6500000 0.4893605
## 23 Daphnia 10.358596 0.7500000 0.4442617
## 24 Daphnia 13.097929 0.6000000 0.5026247
## 25 Daphnia 21.376097 0.0000000 0.0000000
## 26 Daphnia 23.252782 0.0000000 0.0000000
## 27 Daphnia 23.996212 0.0000000 0.0000000
## 28 Daphnia 24.137183 0.0000000 0.0000000
## 29 Daphnia 24.328356 0.4500000 0.5104178
## 30 Daphnia 25.211172 0.0000000 0.0000000
## 31 Daphnia 30.085990 0.0000000 0.0000000
## 32 Daphnia 32.927156 0.0000000 0.0000000
## 33 Daphnia 34.003533 0.0000000 0.0000000
## 34 Daphnia 37.059681 0.0000000 0.0000000
## 35 Daphnia 37.686515 0.0000000 0.0000000
## 36 Daphnia 38.245350 0.0000000 0.0000000
## 37 FHM 2.274342 1.0000000 0.0000000
## 38 FHM 3.806500 1.0000000 0.0000000
## 39 FHM 3.995000 1.0000000 0.0000000
## 40 FHM 9.489672 1.0000000 0.0000000
## 41 FHM 9.528870 0.8000000 0.4216370
## 42 FHM 10.358596 0.9000000 0.3162278
## 43 FHM 23.252782 1.0000000 0.0000000
## 44 FHM 23.996212 1.0000000 0.0000000
## 45 FHM 24.328356 1.0000000 0.0000000
## 46 FHM 30.085990 1.0000000 0.0000000
## 47 FHM 34.003533 1.0000000 0.0000000
## 48 FHM 37.059681 0.7000000 0.4830459
## 49 LMB 3.806500 1.0000000 0.0000000
## 50 LMB 5.227892 1.0000000 0.0000000
## 51 LMB 9.489672 0.8000000 0.4140393
## 52 LMB 9.700738 1.0000000 0.0000000
## 53 LMB 10.358596 0.6000000 0.5070926
## 54 LMB 13.097929 0.7333333 0.4577377
## 55 LMB 21.376097 0.8000000 0.4140393
## 56 LMB 23.252782 1.0000000 0.0000000
## 57 LMB 25.211172 0.6000000 0.5070926
## 58 LMB 30.085990 0.8666667 0.3518658
## 59 LMB 37.059681 0.7333333 0.4577377
## 60 LMB 38.245350 0.8666667 0.3518658
## 61 Snail 2.274342 1.0000000 0.0000000
## 62 Snail 2.965691 0.9000000 0.3162278
## 63 Snail 3.806500 1.0000000 0.0000000
## 64 Snail 3.995000 0.9000000 0.3162278
## 65 Snail 5.227892 1.0000000 0.0000000
## 66 Snail 9.489672 0.9000000 0.3162278
## 67 Snail 9.528870 1.0000000 0.0000000
## 68 Snail 9.645886 1.0000000 0.0000000
## 69 Snail 9.700738 1.0000000 0.0000000
## 70 Snail 10.138460 1.0000000 0.0000000
## 71 Snail 10.358596 1.0000000 0.0000000
## 72 Snail 13.097929 1.0000000 0.0000000
## 73 Snail 21.376097 1.0000000 0.0000000
## 74 Snail 23.252782 0.9000000 0.3162278
## 75 Snail 23.996212 1.0000000 0.0000000
## 76 Snail 24.137183 0.8000000 0.4216370
## 77 Snail 24.328356 1.0000000 0.0000000
## 78 Snail 25.211172 1.0000000 0.0000000
## 79 Snail 30.085990 1.0000000 0.0000000
## 80 Snail 32.927156 0.4000000 0.5163978
## 81 Snail 34.003533 0.9000000 0.3162278
## 82 Snail 37.059681 0.4000000 0.5163978
## 83 Snail 37.686515 0.1000000 0.3162278
## 84 Snail 38.245350 0.1000000 0.3162278
## 85 ZM 2.274342 1.0000000 0.0000000
## 86 ZM 2.965691 0.9600000 0.2000000
## 87 ZM 3.806500 1.0000000 0.0000000
## 88 ZM 3.995000 1.0000000 0.0000000
## 89 ZM 5.227892 1.0000000 0.0000000
## 90 ZM 9.489672 1.0000000 0.0000000
## 91 ZM 9.528870 1.0000000 0.0000000
## 92 ZM 9.645886 1.0000000 0.0000000
## 93 ZM 9.700738 1.0000000 0.0000000
## 94 ZM 10.138460 1.0000000 0.0000000
## 95 ZM 10.358596 1.0000000 0.0000000
## 96 ZM 13.097929 1.0000000 0.0000000
## 97 ZM 21.376097 1.0000000 0.0000000
## 98 ZM 23.252782 1.0000000 0.0000000
## 99 ZM 23.996212 1.0000000 0.0000000
## 100 ZM 24.137183 0.9200000 0.2768875
## 101 ZM 24.328356 1.0000000 0.0000000
## 102 ZM 25.211172 1.0000000 0.0000000
## 103 ZM 30.085990 1.0000000 0.0000000
## 104 ZM 32.927156 0.8400000 0.3741657
## 105 ZM 34.003533 0.9600000 0.2000000
## 106 ZM 37.059681 0.8800000 0.3316625
## 107 ZM 37.686515 0.8800000 0.3316625
## 108 ZM 38.245350 0.7600000 0.4358899tbl_survival_byCu$species <- gsub("BLG", "Bluegill", tbl_survival_byCu$species)
tbl_survival_byCu$species <- gsub("FHM", "Fathead minnow", tbl_survival_byCu$species)
tbl_survival_byCu$species <- gsub("LMB", "Largemouth bass", tbl_survival_byCu$species)
tbl_survival_byCu$species <- gsub("Snail", "Banded mystery snail", tbl_survival_byCu$species)
tbl_survival_byCu$species <- gsub("ZM", "Zebra mussel", tbl_survival_byCu$species)
colnames(tbl_survival_byCu) = c("Species", "avg.Cu", "mean", "sd") # change column names
(Plot_observedSurvival <- ggplot(tbl_survival_byCu, aes(x=avg.Cu, y=mean, color=Species, fill=Species)) +
geom_point() +
geom_smooth(se=F ,method = "glm", method.args=list(family="binomial")) +
theme_classic() +
theme(legend.position = "none") +
#annotate("segment", x=8.39, xend=8.39, y=0.48, yend=0.52, color="black", size=2)+ #8.39 is the mean LC50 from EPA data on daphnia
#annotate("rect", xmin=3.9, xmax=12.88, ymin=0.48, ymax=0.52, alpha=0.2, color="black", fill="black")+ #daphnia LC50 stdev is 4.49
ylim(ymin=0, ymax=1) +
xlab("Copper concentration (µg/L Cu)") +
ylab("Survival (observed proportion)") +
scale_color_viridis(option="turbo", discrete = T) +
scale_fill_viridis(option="turbo", discrete = T))## `geom_smooth()` using formula = 'y ~ x'## Warning in eval(family$initialize): non-integer #successes in a binomial glm!
## Warning in eval(family$initialize): non-integer #successes in a binomial glm!
## Warning in eval(family$initialize): non-integer #successes in a binomial glm!
## Warning in eval(family$initialize): non-integer #successes in a binomial glm!
## Warning in eval(family$initialize): non-integer #successes in a binomial glm!
## Warning in eval(family$initialize): non-integer #successes in a binomial glm!
1.7.4 The model we used
# Decide on final variables to use
mod5 = glm(survival ~ species + avg.Cu, family = "binomial", tbl_allDataLong)
summary(mod5)##
## Call:
## glm(formula = survival ~ species + avg.Cu, family = "binomial",
## data = tbl_allDataLong)
##
## Deviance Residuals:
## Min 1Q Median 3Q Max
## -3.7081 0.0428 0.1670 0.3954 1.9624
##
## Coefficients:
## Estimate Std. Error z value Pr(>|z|)
## (Intercept) 8.034838 1.054858 7.617 2.60e-14 ***
## speciesDaphnia -6.874819 1.034038 -6.649 2.96e-11 ***
## speciesFHM -2.129574 1.101057 -1.934 0.053099 .
## speciesLMB -3.556548 1.037227 -3.429 0.000606 ***
## speciesSnail -3.452813 1.031627 -3.347 0.000817 ***
## speciesZM -1.562740 1.037888 -1.506 0.132146
## avg.Cu -0.120350 0.009099 -13.227 < 2e-16 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## (Dispersion parameter for binomial family taken to be 1)
##
## Null deviance: 1911.74 on 1739 degrees of freedom
## Residual deviance: 950.09 on 1733 degrees of freedom
## AIC: 964.09
##
## Number of Fisher Scoring iterations: 7# Just for fun check the interaction model again to make sure it still isn't as good a fit (now that we are using more specific copper concentrations)
mod6 = glm(survival ~ species + treatGroup + species*treatGroup, family = "binomial", tbl_allDataLong)
summary(mod6)##
## Call:
## glm(formula = survival ~ species + treatGroup + species * treatGroup,
## family = "binomial", data = tbl_allDataLong)
##
## Deviance Residuals:
## Min 1Q Median 3Q Max
## -3.1656 0.0001 0.1638 0.4590 2.2761
##
## Coefficients:
## Estimate Std. Error z value Pr(>|z|)
## (Intercept) 1.957e+01 1.963e+03 0.010 0.992
## speciesDaphnia -1.913e+01 1.963e+03 -0.010 0.992
## speciesFHM 2.250e-08 2.777e+03 0.000 1.000
## speciesLMB -1.724e+01 1.963e+03 -0.009 0.993
## speciesSnail -1.620e+01 1.963e+03 -0.008 0.993
## speciesZM -1.456e+01 1.963e+03 -0.007 0.994
## treatGroupHigh 5.825e-08 2.777e+03 0.000 1.000
## treatGroupLow -1.620e+01 1.963e+03 -0.008 0.993
## treatGroupMed 2.626e-08 2.777e+03 0.000 1.000
## speciesDaphnia:treatGroupHigh -2.001e+01 2.945e+03 -0.007 0.995
## speciesFHM:treatGroupHigh -1.737e+01 3.401e+03 -0.005 0.996
## speciesLMB:treatGroupHigh -7.958e-01 2.777e+03 0.000 1.000
## speciesSnail:treatGroupHigh -3.434e+00 2.777e+03 -0.001 0.999
## speciesZM:treatGroupHigh -2.947e+00 2.777e+03 -0.001 0.999
## speciesDaphnia:treatGroupLow 1.634e+01 1.963e+03 0.008 0.993
## speciesFHM:treatGroupLow -1.170e+00 2.777e+03 0.000 1.000
## speciesLMB:treatGroupLow 1.526e+01 1.963e+03 0.008 0.994
## speciesSnail:treatGroupLow 1.691e+01 1.963e+03 0.009 0.993
## speciesZM:treatGroupLow 3.076e+01 2.151e+03 0.014 0.989
## speciesDaphnia:treatGroupMed -2.953e+00 2.777e+03 -0.001 0.999
## speciesFHM:treatGroupMed -1.767e-08 3.927e+03 0.000 1.000
## speciesLMB:treatGroupMed -9.410e-01 2.777e+03 0.000 1.000
## speciesSnail:treatGroupMed -4.229e-01 2.777e+03 0.000 1.000
## speciesZM:treatGroupMed -6.999e-01 2.777e+03 0.000 1.000
##
## (Dispersion parameter for binomial family taken to be 1)
##
## Null deviance: 1911.74 on 1739 degrees of freedom
## Residual deviance: 862.09 on 1716 degrees of freedom
## AIC: 910.09
##
## Number of Fisher Scoring iterations: 18# Assumptions:
# 1) Outcome is binary (YES - survived/died)
# 2) Linear relationship between logit(outcome) and each predictor variable
# 3) No extreme values or outliers in continuous predictors:
mod5.data <- augment(mod5) %>%
mutate(index = 1:n())
mod5.data %>% top_n(3, .cooksd)## # A tibble: 3 × 10
## survival species avg.Cu .fitted .resid .std.resid .hat .sigma .cooksd index
## <dbl> <fct> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <int>
## 1 0 BLG 9.65 6.87 -3.71 -3.71 0.00110 0.735 0.153 1035
## 2 0 FHM 9.53 4.76 -3.09 -3.09 0.00188 0.737 0.0315 1189
## 3 0 FHM 9.53 4.76 -3.09 -3.09 0.00188 0.737 0.0315 1190ggplot(mod5.data, aes(index, .std.resid)) +
geom_point(aes(color = survival), alpha = .5) +
theme_bw()
mod5.data %>%
filter(abs(.std.resid) > 3) # are these outliers worthy of removing? Decide to keep - these are REAL data from real animals!## # A tibble: 5 × 10
## survival species avg.Cu .fitted .resid .std.resid .hat .sigma .cooksd index
## <dbl> <fct> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <int>
## 1 0 ZM 2.97 6.12 -3.50 -3.50 2.68e-4 0.736 0.0173 295
## 2 0 FHM 10.4 4.66 -3.06 -3.06 2.05e-3 0.737 0.0311 1015
## 3 0 BLG 9.65 6.87 -3.71 -3.71 1.10e-3 0.735 0.153 1035
## 4 0 FHM 9.53 4.76 -3.09 -3.09 1.88e-3 0.737 0.0315 1189
## 5 0 FHM 9.53 4.76 -3.09 -3.09 1.88e-3 0.737 0.0315 1190# 4) No multicollinearity:
car::vif(mod5)## GVIF Df GVIF^(1/(2*Df))
## species 1.838201 5 1.062770
## avg.Cu 1.838201 1 1.355803# 5) Residual normality (on logit scale!)
hist(residuals(mod5)) # deviance residuals of model - does this look normal, given that there is not a perfect way of calculating a residual on these data?
# Yes, we can consider this normal *enough*
# 6) Check leveraging points
boot::glm.diag.plots(mod5) # one point has a notable amount of leverage and is potentially influential
cooks.distance(mod5)## 1 2 3 4 5 6
## 6.504826e-07 6.504826e-07 6.504826e-07 6.504826e-07 6.504826e-07 6.504826e-07
## 7 8 9 10 11 12
## 6.504826e-07 6.504826e-07 6.504826e-07 6.504826e-07 6.504826e-07 6.504826e-07
## 13 14 15 16 17 18
## 6.504826e-07 6.504826e-07 6.504826e-07 6.504826e-07 6.504826e-07 6.504826e-07
## 19 20 21 22 23 24
## 6.504826e-07 6.504826e-07 6.504826e-07 6.504826e-07 6.504826e-07 6.504826e-07
## 25 26 27 28 29 30
## 6.504826e-07 1.334175e-07 1.334175e-07 1.334175e-07 1.334175e-07 1.334175e-07
## 31 32 33 34 35 36
## 1.334175e-07 1.334175e-07 1.334175e-07 1.334175e-07 1.334175e-07 1.334175e-07
## 37 38 39 40 41 42
## 1.334175e-07 1.334175e-07 1.334175e-07 1.334175e-07 1.334175e-07 1.334175e-07
## 43 44 45 46 47 48
## 1.334175e-07 1.334175e-07 1.334175e-07 1.334175e-07 1.334175e-07 1.334175e-07
## 49 50 51 52 53 54
## 1.334175e-07 1.334175e-07 3.462812e-04 3.462812e-04 3.462812e-04 3.462812e-04
## 55 56 57 58 59 60
## 3.462812e-04 3.462812e-04 3.462812e-04 3.462812e-04 3.462812e-04 3.462812e-04
## 61 62 63 64 65 66
## 3.462812e-04 3.462812e-04 3.462812e-04 1.001184e-03 1.001184e-03 1.001184e-03
## 67 68 69 70 71 72
## 1.001184e-03 1.001184e-03 1.001184e-03 1.001184e-03 2.788889e-06 2.788889e-06
## 73 74 75 76 77 78
## 2.788889e-06 2.788889e-06 2.788889e-06 2.788889e-06 2.788889e-06 2.788889e-06
## 79 80 81 82 83 84
## 2.788889e-06 1.304100e-02 3.278028e-06 3.278028e-06 3.278028e-06 3.278028e-06
## 85 86 87 88 89 90
## 3.278028e-06 3.278028e-06 3.278028e-06 3.278028e-06 3.278028e-06 3.278028e-06
## 91 92 93 94 95 96
## 1.002286e-07 1.002286e-07 1.002286e-07 1.002286e-07 1.002286e-07 1.002286e-07
## 97 98 99 100 101 102
## 1.002286e-07 1.002286e-07 1.002286e-07 1.002286e-07 1.002286e-07 1.002286e-07
## 103 104 105 106 107 108
## 1.002286e-07 1.002286e-07 1.002286e-07 1.002286e-07 1.002286e-07 1.002286e-07
## 109 110 111 112 113 114
## 1.002286e-07 1.002286e-07 1.002286e-07 1.002286e-07 1.002286e-07 1.002286e-07
## 115 116 117 118 119 120
## 1.002286e-07 1.041054e-07 1.041054e-07 1.041054e-07 1.041054e-07 1.041054e-07
## 121 122 123 124 125 126
## 1.041054e-07 1.041054e-07 1.041054e-07 1.041054e-07 1.041054e-07 1.041054e-07
## 127 128 129 130 131 132
## 1.041054e-07 1.041054e-07 1.041054e-07 1.041054e-07 1.041054e-07 1.041054e-07
## 133 134 135 136 137 138
## 1.041054e-07 1.041054e-07 1.041054e-07 1.041054e-07 1.041054e-07 1.041054e-07
## 139 140 141 142 143 144
## 1.041054e-07 1.041054e-07 5.870058e-06 5.870058e-06 5.870058e-06 5.870058e-06
## 145 146 147 148 149 150
## 5.870058e-06 5.870058e-06 5.870058e-06 5.870058e-06 5.870058e-06 5.870058e-06
## 151 152 153 154 155 156
## 5.870058e-06 5.870058e-06 5.870058e-06 5.870058e-06 5.870058e-06 2.441040e-06
## 157 158 159 160 161 162
## 2.441040e-06 2.441040e-06 2.441040e-06 2.441040e-06 2.441040e-06 2.441040e-06
## 163 164 165 166 167 168
## 2.441040e-06 2.441040e-06 2.441040e-06 7.360775e-08 7.360775e-08 7.360775e-08
## 169 170 171 172 173 174
## 7.360775e-08 7.360775e-08 7.360775e-08 7.360775e-08 7.360775e-08 7.360775e-08
## 175 176 177 178 179 180
## 7.360775e-08 7.360775e-08 7.360775e-08 7.360775e-08 7.360775e-08 7.360775e-08
## 181 182 183 184 185 186
## 7.360775e-08 7.360775e-08 7.360775e-08 7.360775e-08 7.360775e-08 7.360775e-08
## 187 188 189 190 191 192
## 7.360775e-08 7.360775e-08 7.360775e-08 7.360775e-08 2.866011e-04 2.866011e-04
## 193 194 195 196 197 198
## 2.866011e-04 2.866011e-04 2.866011e-04 2.866011e-04 2.866011e-04 2.866011e-04
## 199 200 201 202 203 204
## 2.866011e-04 2.866011e-04 1.428370e-03 1.428370e-03 1.428370e-03 1.428370e-03
## 205 206 207 208 209 210
## 1.428370e-03 1.428370e-03 1.428370e-03 1.428370e-03 1.428370e-03 1.428370e-03
## 211 212 213 214 215 216
## 5.736285e-08 5.736285e-08 5.736285e-08 5.736285e-08 5.736285e-08 5.736285e-08
## 217 218 219 220 221 222
## 5.736285e-08 5.736285e-08 5.736285e-08 5.736285e-08 1.902480e-05 1.902480e-05
## 223 224 225 226 227 228
## 1.902480e-05 1.902480e-05 1.902480e-05 1.902480e-05 1.902480e-05 1.902480e-05
## 229 230 231 232 233 234
## 1.902480e-05 1.902480e-05 4.303350e-06 4.303350e-06 4.303350e-06 4.303350e-06
## 235 236 237 238 239 240
## 4.303350e-06 4.303350e-06 4.303350e-06 4.303350e-06 4.303350e-06 4.303350e-06
## 241 242 243 244 245 246
## 3.398751e-06 3.398751e-06 3.398751e-06 3.398751e-06 3.398751e-06 3.398751e-06
## 247 248 249 250 251 252
## 3.398751e-06 3.398751e-06 3.398751e-06 1.240511e-02 3.704497e-07 3.704497e-07
## 253 254 255 256 257 258
## 3.704497e-07 3.704497e-07 3.704497e-07 3.704497e-07 3.704497e-07 3.704497e-07
## 259 260 261 262 263 264
## 3.704497e-07 3.704497e-07 6.462998e-07 6.462998e-07 6.462998e-07 6.462998e-07
## 265 266 267 268 269 270
## 6.462998e-07 6.462998e-07 6.462998e-07 6.462998e-07 6.462998e-07 6.462998e-07
## 271 272 273 274 275 276
## 8.461427e-08 8.461427e-08 8.461427e-08 8.461427e-08 8.461427e-08 8.461427e-08
## 277 278 279 280 281 282
## 8.461427e-08 8.461427e-08 8.461427e-08 8.461427e-08 8.461427e-08 8.461427e-08
## 283 284 285 286 287 288
## 8.461427e-08 8.461427e-08 8.461427e-08 8.461427e-08 8.461427e-08 8.461427e-08
## 289 290 291 292 293 294
## 8.461427e-08 8.461427e-08 8.461427e-08 8.461427e-08 8.461427e-08 8.461427e-08
## 295 296 297 298 299 300
## 1.733884e-02 3.130550e-04 3.130550e-04 3.130550e-04 3.130550e-04 3.130550e-04
## 301 302 303 304 305 306
## 3.130550e-04 3.130550e-04 3.130550e-04 3.130550e-04 3.130550e-04 3.130550e-04
## 307 308 309 310 311 312
## 3.130550e-04 3.130550e-04 3.130550e-04 3.130550e-04 1.217826e-03 1.217826e-03
## 313 314 315 316 317 318
## 1.217826e-03 1.217826e-03 1.217826e-03 3.360923e-08 3.360923e-08 3.360923e-08
## 319 320 321 322 323 324
## 3.360923e-08 3.360923e-08 3.360923e-08 3.360923e-08 3.360923e-08 3.360923e-08
## 325 326 327 328 329 330
## 3.360923e-08 6.739603e-07 6.739603e-07 6.739603e-07 6.739603e-07 6.739603e-07
## 331 332 333 334 335 336
## 6.739603e-07 6.739603e-07 6.739603e-07 6.739603e-07 6.739603e-07 4.598828e-07
## 337 338 339 340 341 342
## 4.598828e-07 4.598828e-07 4.598828e-07 4.598828e-07 4.598828e-07 4.598828e-07
## 343 344 345 346 347 348
## 4.598828e-07 4.598828e-07 4.598828e-07 2.713996e-05 2.713996e-05 2.713996e-05
## 349 350 351 352 353 354
## 2.713996e-05 2.713996e-05 2.713996e-05 2.713996e-05 2.713996e-05 2.713996e-05
## 355 356 357 358 359 360
## 2.713996e-05 2.713996e-05 8.999504e-03 8.999504e-03 8.999504e-03 8.999504e-03
## 361 362 363 364 365 366
## 3.081294e-04 3.081294e-04 3.081294e-04 3.081294e-04 3.081294e-04 3.081294e-04
## 367 368 369 370 371 372
## 3.081294e-04 3.081294e-04 3.081294e-04 3.081294e-04 3.081294e-04 3.081294e-04
## 373 374 375 376 377 378
## 3.081294e-04 3.081294e-04 3.081294e-04 3.081294e-04 1.254303e-03 1.254303e-03
## 379 380 381 382 383 384
## 1.254303e-03 1.254303e-03 6.703468e-04 6.703468e-04 6.703468e-04 6.703468e-04
## 385 386 387 388 389 390
## 6.703468e-04 6.703468e-04 6.703468e-04 6.703468e-04 6.703468e-04 6.703468e-04
## 391 392 393 394 395 396
## 6.703468e-04 6.703468e-04 2.915381e-04 2.915381e-04 2.915381e-04 2.915381e-04
## 397 398 399 400 401 402
## 2.915381e-04 2.915381e-04 2.915381e-04 2.915381e-04 2.694168e-04 2.694168e-04
## 403 404 405 406 407 408
## 2.694168e-04 2.694168e-04 2.694168e-04 2.694168e-04 2.694168e-04 1.585832e-03
## 409 410 411 412 413 414
## 1.585832e-03 1.585832e-03 1.585832e-03 1.585832e-03 1.585832e-03 1.585832e-03
## 415 416 417 418 419 420
## 1.585832e-03 1.585832e-03 1.585832e-03 1.585832e-03 1.585832e-03 1.585832e-03
## 421 422 423 424 425 426
## 4.440993e-06 4.440993e-06 4.440993e-06 4.440993e-06 4.440993e-06 4.440993e-06
## 427 428 429 430 431 432
## 4.440993e-06 4.440993e-06 4.440993e-06 4.440993e-06 4.440993e-06 4.440993e-06
## 433 434 435 436 437 438
## 4.440993e-06 4.440993e-06 4.440993e-06 1.511509e-04 1.511509e-04 1.511509e-04
## 439 440 441 442 443 444
## 1.511509e-04 1.511509e-04 1.511509e-04 1.511509e-04 1.511509e-04 1.511509e-04
## 445 446 447 448 449 450
## 1.511509e-04 1.511509e-04 1.511509e-04 1.511509e-04 1.511509e-04 1.511509e-04
## 451 452 453 454 455 456
## 1.511509e-04 1.511509e-04 1.511509e-04 1.511509e-04 6.354043e-03 6.354043e-03
## 457 458 459 460 461 462
## 6.354043e-03 6.354043e-03 6.354043e-03 6.354043e-03 1.797672e-03 1.724460e-03
## 463 464 465 466 467 468
## 1.724460e-03 1.724460e-03 1.724460e-03 1.724460e-03 1.724460e-03 1.724460e-03
## 469 470 471 472 473 474
## 1.724460e-03 1.724460e-03 1.843321e-05 1.843321e-05 1.843321e-05 1.843321e-05
## 475 476 477 478 479 480
## 1.843321e-05 1.843321e-05 1.843321e-05 1.843321e-05 1.843321e-05 1.843321e-05
## 481 482 483 484 485 486
## 1.843321e-05 1.843321e-05 1.843321e-05 1.843321e-05 1.843321e-05 1.843321e-05
## 487 488 489 490 491 492
## 1.843321e-05 1.843321e-05 1.843321e-05 1.843321e-05 2.348340e-05 2.348340e-05
## 493 494 495 496 497 498
## 2.348340e-05 2.348340e-05 2.348340e-05 2.348340e-05 2.348340e-05 2.348340e-05
## 499 500 501 502 503 504
## 2.348340e-05 2.348340e-05 2.348340e-05 2.348340e-05 2.348340e-05 2.348340e-05
## 505 506 507 508 509 510
## 2.348340e-05 2.348340e-05 2.348340e-05 2.348340e-05 2.348340e-05 2.348340e-05
## 511 512 513 514 515 516
## 2.348340e-05 2.348340e-05 2.348340e-05 2.348340e-05 2.348340e-05 8.988284e-04
## 517 518 519 520 521 522
## 8.988284e-04 8.988284e-04 8.988284e-04 8.988284e-04 8.988284e-04 8.988284e-04
## 523 524 525 526 527 528
## 8.988284e-04 8.988284e-04 2.393510e-03 4.483708e-04 4.483708e-04 4.483708e-04
## 529 530 531 532 533 534
## 4.483708e-04 4.483708e-04 4.483708e-04 4.483708e-04 4.483708e-04 4.483708e-04
## 535 536 537 538 539 540
## 4.483708e-04 4.463522e-05 4.463522e-05 4.463522e-05 4.463522e-05 4.463522e-05
## 541 542 543 544 545 546
## 4.463522e-05 4.463522e-05 4.463522e-05 4.463522e-05 4.463522e-05 4.463522e-05
## 547 548 549 550 551 552
## 4.463522e-05 4.463522e-05 4.463522e-05 4.463522e-05 4.463522e-05 4.463522e-05
## 553 554 555 556 557 558
## 4.463522e-05 4.463522e-05 4.463522e-05 4.463522e-05 6.749183e-03 6.749183e-03
## 559 560 561 562 563 564
## 6.749183e-03 6.749183e-03 1.329633e-04 1.329633e-04 1.329633e-04 1.329633e-04
## 565 566 567 568 569 570
## 1.329633e-04 1.329633e-04 1.329633e-04 1.329633e-04 1.329633e-04 1.329633e-04
## 571 572 573 574 575 576
## 1.329633e-04 1.329633e-04 1.329633e-04 1.329633e-04 1.329633e-04 1.329633e-04
## 577 578 579 580 581 582
## 1.329633e-04 1.329633e-04 1.329633e-04 1.329633e-04 1.329633e-04 1.329633e-04
## 583 584 585 586 587 588
## 6.394237e-03 6.394237e-03 6.394237e-03 4.244317e-05 4.244317e-05 4.244317e-05
## 589 590 591 592 593 594
## 4.244317e-05 4.244317e-05 4.244317e-05 4.244317e-05 4.244317e-05 4.244317e-05
## 595 596 597 598 599 600
## 4.244317e-05 5.707449e-05 5.707449e-05 5.707449e-05 5.707449e-05 5.707449e-05
## 601 602 603 604 605 606
## 5.707449e-05 5.707449e-05 5.707449e-05 5.707449e-05 5.707449e-05 5.707449e-05
## 607 608 609 610 611 612
## 5.707449e-05 5.707449e-05 5.707449e-05 5.707449e-05 5.707449e-05 5.707449e-05
## 613 614 615 616 617 618
## 5.707449e-05 5.707449e-05 5.707449e-05 5.707449e-05 5.707449e-05 5.707449e-05
## 619 620 621 622 623 624
## 5.707449e-05 6.660352e-03 6.699190e-04 6.699190e-04 6.699190e-04 6.699190e-04
## 625 626 627 628 629 630
## 6.699190e-04 6.699190e-04 6.699190e-04 6.699190e-04 6.699190e-04 6.699190e-04
## 631 632 633 634 635 636
## 6.699190e-04 6.699190e-04 6.699190e-04 3.722163e-03 3.722163e-03 1.648403e-03
## 637 638 639 640 641 642
## 1.808937e-03 1.808937e-03 1.808937e-03 1.808937e-03 1.808937e-03 1.808937e-03
## 643 644 645 646 647 648
## 1.808937e-03 1.808937e-03 1.808937e-03 2.382821e-04 2.382821e-04 2.382821e-04
## 649 650 651 652 653 654
## 2.382821e-04 2.382821e-04 2.382821e-04 2.382821e-04 2.382821e-04 2.382821e-04
## 655 656 657 658 659 660
## 2.382821e-04 1.068235e-03 1.068235e-03 1.068235e-03 1.068235e-03 1.068235e-03
## 661 662 663 664 665 666
## 1.068235e-03 1.068235e-03 1.922664e-02 1.922664e-02 1.922664e-02 1.151325e-04
## 667 668 669 670 671 672
## 1.151325e-04 1.151325e-04 1.151325e-04 1.151325e-04 1.151325e-04 1.151325e-04
## 673 674 675 676 677 678
## 1.151325e-04 1.151325e-04 1.151325e-04 1.151325e-04 1.151325e-04 1.151325e-04
## 679 680 681 682 683 684
## 1.151325e-04 1.151325e-04 1.151325e-04 1.151325e-04 1.151325e-04 1.151325e-04
## 685 686 687 688 689 690
## 1.151325e-04 1.151325e-04 1.151325e-04 6.438438e-03 6.438438e-03 6.438438e-03
## 691 692 693 694 695 696
## 1.528998e-04 1.528998e-04 1.528998e-04 1.528998e-04 1.528998e-04 1.528998e-04
## 697 698 699 700 701 702
## 1.528998e-04 1.528998e-04 1.528998e-04 1.528998e-04 1.008175e-05 1.008175e-05
## 703 704 705 706 707 708
## 1.008175e-05 1.008175e-05 1.008175e-05 1.008175e-05 1.008175e-05 1.008175e-05
## 709 710 711 712 713 714
## 1.008175e-05 1.008175e-05 1.008175e-05 1.008175e-05 1.008175e-05 1.008175e-05
## 715 716 717 718 719 720
## 1.008175e-05 1.008175e-05 1.008175e-05 1.008175e-05 1.008175e-05 1.008175e-05
## 721 722 723 724 725 726
## 2.495827e-03 2.495827e-03 2.495827e-03 2.495827e-03 2.495827e-03 2.495827e-03
## 727 728 729 730 731 732
## 2.495827e-03 2.495827e-03 2.495827e-03 2.495827e-03 2.495827e-03 2.495827e-03
## 733 734 735 736 737 738
## 2.495827e-03 1.945602e-03 1.945602e-03 2.182654e-05 2.182654e-05 2.182654e-05
## 739 740 741 742 743 744
## 2.182654e-05 2.182654e-05 2.182654e-05 2.182654e-05 2.182654e-05 2.182654e-05
## 745 746 747 748 749 750
## 2.182654e-05 2.182654e-05 2.182654e-05 2.182654e-05 2.182654e-05 2.182654e-05
## 751 752 753 754 755 756
## 2.182654e-05 2.182654e-05 2.182654e-05 2.182654e-05 2.182654e-05 1.336138e-04
## 757 758 759 760 761 762
## 1.336138e-04 1.336138e-04 1.336138e-04 1.336138e-04 1.336138e-04 1.336138e-04
## 763 764 765 766 767 768
## 1.336138e-04 1.336138e-04 1.336138e-04 9.171101e-06 9.171101e-06 9.171101e-06
## 769 770 771 772 773 774
## 9.171101e-06 9.171101e-06 9.171101e-06 9.171101e-06 9.171101e-06 9.171101e-06
## 775 776 777 778 779 780
## 9.171101e-06 9.171101e-06 9.171101e-06 9.171101e-06 9.171101e-06 9.171101e-06
## 781 782 783 784 785 786
## 9.171101e-06 9.171101e-06 9.171101e-06 9.171101e-06 9.171101e-06 1.492966e-03
## 787 788 789 790 791 792
## 1.492966e-03 1.492966e-03 1.492966e-03 1.905179e-03 1.905179e-03 1.905179e-03
## 793 794 795 796 797 798
## 1.905179e-03 1.905179e-03 1.905179e-03 7.455427e-04 7.455427e-04 7.455427e-04
## 799 800 801 802 803 804
## 7.455427e-04 2.572464e-03 2.572464e-03 2.572464e-03 2.572464e-03 2.572464e-03
## 805 806 807 808 809 810
## 2.572464e-03 5.611863e-04 5.611863e-04 5.611863e-04 5.611863e-04 5.611863e-04
## 811 812 813 814 815 816
## 5.611863e-04 5.611863e-04 5.611863e-04 5.611863e-04 5.611863e-04 2.101580e-03
## 817 818 819 820 821 822
## 2.101580e-03 2.101580e-03 2.101580e-03 2.101580e-03 2.101580e-03 2.101580e-03
## 823 824 825 826 827 828
## 2.101580e-03 2.101580e-03 2.101580e-03 2.101580e-03 2.179359e-03 2.179359e-03
## 829 830 831 832 833 834
## 2.179359e-03 2.179359e-03 3.348454e-05 3.348454e-05 3.348454e-05 3.348454e-05
## 835 836 837 838 839 840
## 3.348454e-05 3.348454e-05 3.348454e-05 3.348454e-05 3.348454e-05 3.348454e-05
## 841 842 843 844 845 846
## 3.348454e-05 3.348454e-05 3.348454e-05 3.348454e-05 3.348454e-05 3.348454e-05
## 847 848 849 850 851 852
## 3.348454e-05 3.348454e-05 3.348454e-05 3.348454e-05 1.120092e-05 1.120092e-05
## 853 854 855 856 857 858
## 1.120092e-05 1.120092e-05 1.120092e-05 1.120092e-05 1.120092e-05 1.120092e-05
## 859 860 861 862 863 864
## 1.120092e-05 1.120092e-05 1.120092e-05 1.120092e-05 1.120092e-05 1.120092e-05
## 865 866 867 868 869 870
## 1.120092e-05 1.120092e-05 1.120092e-05 1.120092e-05 1.120092e-05 1.120092e-05
## 871 872 873 874 875 876
## 9.661253e-06 9.661253e-06 9.661253e-06 9.661253e-06 9.661253e-06 9.661253e-06
## 877 878 879 880 881 882
## 9.661253e-06 9.661253e-06 9.661253e-06 9.395779e-03 1.654144e-07 1.654144e-07
## 883 884 885 886 887 888
## 1.654144e-07 1.654144e-07 1.654144e-07 1.654144e-07 1.654144e-07 1.654144e-07
## 889 890 891 892 893 894
## 1.654144e-07 1.654144e-07 2.296660e-06 2.296660e-06 2.296660e-06 2.296660e-06
## 895 896 897 898 899 900
## 2.296660e-06 2.296660e-06 2.296660e-06 2.296660e-06 2.296660e-06 2.296660e-06
## 901 902 903 904 905 906
## 3.279367e-07 3.279367e-07 3.279367e-07 3.279367e-07 3.279367e-07 3.279367e-07
## 907 908 909 910 911 912
## 3.279367e-07 3.279367e-07 3.279367e-07 3.279367e-07 3.279367e-07 3.279367e-07
## 913 914 915 916 917 918
## 3.279367e-07 3.279367e-07 3.279367e-07 3.279367e-07 3.279367e-07 3.279367e-07
## 919 920 921 922 923 924
## 3.279367e-07 3.279367e-07 3.279367e-07 3.279367e-07 3.279367e-07 3.279367e-07
## 925 926 927 928 929 930
## 3.279367e-07 3.143036e-07 3.143036e-07 3.143036e-07 3.143036e-07 3.143036e-07
## 931 932 933 934 935 936
## 3.143036e-07 3.143036e-07 3.143036e-07 3.143036e-07 3.143036e-07 3.143036e-07
## 937 938 939 940 941 942
## 3.143036e-07 3.143036e-07 3.143036e-07 3.143036e-07 3.143036e-07 3.143036e-07
## 943 944 945 946 947 948
## 3.143036e-07 3.143036e-07 3.143036e-07 3.143036e-07 3.143036e-07 3.143036e-07
## 949 950 951 952 953 954
## 3.143036e-07 3.143036e-07 1.005347e-05 1.005347e-05 1.005347e-05 1.005347e-05
## 955 956 957 958 959 960
## 1.005347e-05 1.005347e-05 1.005347e-05 1.005347e-05 1.005347e-05 1.005347e-05
## 961 962 963 964 965 966
## 3.167918e-07 3.167918e-07 3.167918e-07 3.167918e-07 3.167918e-07 3.167918e-07
## 967 968 969 970 971 972
## 3.167918e-07 3.167918e-07 3.167918e-07 3.167918e-07 3.167918e-07 3.167918e-07
## 973 974 975 976 977 978
## 3.167918e-07 3.167918e-07 3.167918e-07 3.167918e-07 3.167918e-07 3.167918e-07
## 979 980 981 982 983 984
## 3.167918e-07 3.167918e-07 3.167918e-07 3.167918e-07 3.167918e-07 3.167918e-07
## 985 986 987 988 989 990
## 3.167918e-07 4.930070e-04 4.930070e-04 4.930070e-04 4.930070e-04 4.930070e-04
## 991 992 993 994 995 996
## 4.930070e-04 4.930070e-04 4.930070e-04 4.930070e-04 4.930070e-04 4.857044e-04
## 997 998 999 1000 1001 1002
## 4.857044e-04 4.857044e-04 4.857044e-04 4.857044e-04 4.857044e-04 4.857044e-04
## 1003 1004 1005 1006 1007 1008
## 4.857044e-04 4.857044e-04 4.857044e-04 2.790379e-06 2.790379e-06 2.790379e-06
## 1009 1010 1011 1012 1013 1014
## 2.790379e-06 2.790379e-06 2.790379e-06 2.790379e-06 2.790379e-06 2.790379e-06
## 1015 1016 1017 1018 1019 1020
## 3.105137e-02 1.835058e-07 1.835058e-07 1.835058e-07 1.835058e-07 1.835058e-07
## 1021 1022 1023 1024 1025 1026
## 1.835058e-07 1.835058e-07 1.835058e-07 1.835058e-07 1.835058e-07 1.632772e-07
## 1027 1028 1029 1030 1031 1032
## 1.632772e-07 1.632772e-07 1.632772e-07 1.632772e-07 1.632772e-07 1.632772e-07
## 1033 1034 1035 1036 1037 1038
## 1.632772e-07 1.632772e-07 1.526056e-01 1.091763e-05 1.091763e-05 1.091763e-05
## 1039 1040 1041 1042 1043 1044
## 1.091763e-05 1.091763e-05 1.091763e-05 1.091763e-05 1.091763e-05 1.091763e-05
## 1045 1046 1047 1048 1049 1050
## 1.091763e-05 5.209062e-04 5.209062e-04 5.209062e-04 5.209062e-04 5.209062e-04
## 1051 1052 1053 1054 1055 1056
## 5.209062e-04 5.209062e-04 5.209062e-04 5.209062e-04 5.209062e-04 5.209062e-04
## 1057 1058 1059 1060 1061 1062
## 5.209062e-04 5.209062e-04 5.209062e-04 5.209062e-04 4.380359e-04 4.380359e-04
## 1063 1064 1065 1066 1067 1068
## 4.380359e-04 4.380359e-04 4.380359e-04 4.860984e-04 4.860984e-04 4.860984e-04
## 1069 1070 1071 1072 1073 1074
## 4.860984e-04 4.860984e-04 4.860984e-04 4.860984e-04 4.860984e-04 4.860984e-04
## 1075 1076 1077 1078 1079 1080
## 4.860984e-04 4.860984e-04 4.860984e-04 4.991249e-04 4.991249e-04 4.991249e-04
## 1081 1082 1083 1084 1085 1086
## 4.991249e-04 4.991249e-04 4.991249e-04 4.991249e-04 4.991249e-04 4.907861e-04
## 1087 1088 1089 1090 1091 1092
## 4.907861e-04 4.907861e-04 4.907861e-04 4.907861e-04 4.907861e-04 4.907861e-04
## 1093 1094 1095 1096 1097 1098
## 4.907861e-04 4.907861e-04 4.907861e-04 4.907861e-04 4.899424e-04 4.899424e-04
## 1099 1100 1101 1102 1103 1104
## 4.899424e-04 4.899424e-04 4.899424e-04 4.899424e-04 4.899424e-04 4.899424e-04
## 1105 1106 1107 1108 1109 1110
## 4.899424e-04 3.243377e-07 3.243377e-07 3.243377e-07 3.243377e-07 3.243377e-07
## 1111 1112 1113 1114 1115 1116
## 3.243377e-07 3.243377e-07 3.243377e-07 3.243377e-07 3.243377e-07 3.243377e-07
## 1117 1118 1119 1120 1121 1122
## 3.243377e-07 3.243377e-07 3.243377e-07 3.243377e-07 3.243377e-07 3.243377e-07
## 1123 1124 1125 1126 1127 1128
## 3.243377e-07 3.243377e-07 3.243377e-07 3.243377e-07 3.243377e-07 3.243377e-07
## 1129 1130 1131 1132 1133 1134
## 3.243377e-07 3.243377e-07 3.581322e-07 3.581322e-07 3.581322e-07 3.581322e-07
## 1135 1136 1137 1138 1139 1140
## 3.581322e-07 3.581322e-07 3.581322e-07 3.581322e-07 3.581322e-07 3.581322e-07
## 1141 1142 1143 1144 1145 1146
## 3.581322e-07 3.581322e-07 3.581322e-07 3.581322e-07 3.581322e-07 3.581322e-07
## 1147 1148 1149 1150 1151 1152
## 3.581322e-07 3.581322e-07 3.581322e-07 3.581322e-07 3.581322e-07 3.581322e-07
## 1153 1154 1155 1156 1157 1158
## 3.581322e-07 3.581322e-07 3.581322e-07 1.404974e-05 1.404974e-05 1.404974e-05
## 1159 1160 1161 1162 1163 1164
## 1.404974e-05 1.404974e-05 1.404974e-05 1.404974e-05 1.404974e-05 1.404974e-05
## 1165 1166 1167 1168 1169 1170
## 1.404974e-05 1.404974e-05 1.404974e-05 1.404974e-05 1.404974e-05 1.404974e-05
## 1171 1172 1173 1174 1175 1176
## 9.732935e-06 9.732935e-06 9.732935e-06 9.732935e-06 9.732935e-06 9.732935e-06
## 1177 1178 1179 1180 1181 1182
## 9.732935e-06 9.732935e-06 9.732935e-06 9.732935e-06 2.316911e-06 2.316911e-06
## 1183 1184 1185 1186 1187 1188
## 2.316911e-06 2.316911e-06 2.316911e-06 2.316911e-06 2.316911e-06 2.316911e-06
## 1189 1190 1191 1192 1193 1194
## 3.148196e-02 3.148196e-02 5.113000e-04 5.113000e-04 5.113000e-04 5.113000e-04
## 1195 1196 1197 1198 1199 1200
## 5.113000e-04 5.113000e-04 5.113000e-04 5.113000e-04 5.113000e-04 5.113000e-04
## 1201 1202 1203 1204 1205 1206
## 5.113000e-04 5.113000e-04 5.113000e-04 4.533542e-04 4.533542e-04 4.533542e-04
## 1207 1208 1209 1210 1211 1212
## 4.533542e-04 4.533542e-04 4.533542e-04 4.533542e-04 1.348505e-05 1.348505e-05
## 1213 1214 1215 1216 1217 1218
## 1.348505e-05 1.348505e-05 1.348505e-05 1.348505e-05 1.348505e-05 1.348505e-05
## 1219 1220 1221 1222 1223 1224
## 1.348505e-05 1.348505e-05 1.348505e-05 1.348505e-05 1.065734e-02 1.065734e-02
## 1225 1226 1227 1228 1229 1230
## 1.065734e-02 3.743579e-07 3.743579e-07 3.743579e-07 3.743579e-07 3.743579e-07
## 1231 1232 1233 1234 1235 1236
## 3.743579e-07 3.743579e-07 3.743579e-07 3.743579e-07 3.743579e-07 3.743579e-07
## 1237 1238 1239 1240 1241 1242
## 3.743579e-07 3.743579e-07 3.743579e-07 3.743579e-07 3.743579e-07 3.743579e-07
## 1243 1244 1245 1246 1247 1248
## 3.743579e-07 3.743579e-07 3.743579e-07 3.743579e-07 3.743579e-07 3.743579e-07
## 1249 1250 1251 1252 1253 1254
## 3.743579e-07 3.743579e-07 9.950059e-06 9.950059e-06 9.950059e-06 9.950059e-06
## 1255 1256 1257 1258 1259 1260
## 9.950059e-06 9.950059e-06 9.950059e-06 9.950059e-06 9.950059e-06 9.950059e-06
## 1261 1262 1263 1264 1265 1266
## 1.596457e-05 1.596457e-05 1.596457e-05 1.596457e-05 1.596457e-05 1.596457e-05
## 1267 1268 1269 1270 1271 1272
## 1.596457e-05 1.596457e-05 1.596457e-05 1.023578e-02 1.023578e-02 1.023578e-02
## 1273 1274 1275 1276 1277 1278
## 1.023578e-02 1.023578e-02 1.023578e-02 1.137951e-05 1.137951e-05 1.137951e-05
## 1279 1280 1281 1282 1283 1284
## 1.137951e-05 1.137951e-05 1.137951e-05 1.137951e-05 1.137951e-05 1.137951e-05
## 1285 1286 1287 1288 1289 1290
## 1.137951e-05 4.845432e-04 4.845432e-04 4.845432e-04 4.845432e-04 4.845432e-04
## 1291 1292 1293 1294 1295 1296
## 4.845432e-04 4.845432e-04 4.845432e-04 4.845432e-04 4.845432e-04 4.845432e-04
## 1297 1298 1299 1300 1301 1302
## 4.845432e-04 4.845432e-04 4.845432e-04 4.845432e-04 4.845432e-04 5.022444e-04
## 1303 1304 1305 1306 1307 1308
## 5.022444e-04 5.022444e-04 5.022444e-04 1.821945e-04 1.821945e-04 1.821945e-04
## 1309 1310 1311 1312 1313 1314
## 1.821945e-04 1.821945e-04 1.821945e-04 1.821945e-04 1.821945e-04 1.821945e-04
## 1315 1316 1317 1318 1319 1320
## 1.821945e-04 6.194232e-06 6.194232e-06 6.194232e-06 6.194232e-06 6.194232e-06
## 1321 1322 1323 1324 1325 1326
## 6.194232e-06 6.194232e-06 6.194232e-06 6.194232e-06 6.194232e-06 6.194232e-06
## 1327 1328 1329 1330 1331 1332
## 6.194232e-06 6.194232e-06 6.194232e-06 6.194232e-06 6.194232e-06 6.194232e-06
## 1333 1334 1335 1336 1337 1338
## 6.194232e-06 6.194232e-06 6.194232e-06 6.194232e-06 6.194232e-06 6.194232e-06
## 1339 1340 1341 1342 1343 1344
## 6.194232e-06 6.194232e-06 3.558009e-06 3.558009e-06 3.558009e-06 3.558009e-06
## 1345 1346 1347 1348 1349 1350
## 3.558009e-06 3.558009e-06 3.558009e-06 3.558009e-06 3.558009e-06 3.558009e-06
## 1351 1352 1353 1354 1355 1356
## 3.558009e-06 3.558009e-06 3.558009e-06 3.558009e-06 3.558009e-06 3.558009e-06
## 1357 1358 1359 1360 1361 1362
## 3.558009e-06 3.558009e-06 3.558009e-06 3.558009e-06 3.558009e-06 3.558009e-06
## 1363 1364 1365 1366 1367 1368
## 3.558009e-06 3.558009e-06 3.558009e-06 6.659067e-06 6.659067e-06 6.659067e-06
## 1369 1370 1371 1372 1373 1374
## 6.659067e-06 6.659067e-06 6.659067e-06 6.659067e-06 6.659067e-06 6.659067e-06
## 1375 1376 1377 1378 1379 1380
## 6.659067e-06 1.545473e-04 1.545473e-04 1.545473e-04 1.545473e-04 1.545473e-04
## 1381 1382 1383 1384 1385 1386
## 1.545473e-04 1.545473e-04 1.545473e-04 1.545473e-04 1.545473e-04 8.909937e-05
## 1387 1388 1389 1390 1391 1392
## 8.909937e-05 8.909937e-05 8.909937e-05 8.909937e-05 8.909937e-05 8.909937e-05
## 1393 1394 1395 1396 1397 1398
## 8.909937e-05 8.909937e-05 8.909937e-05 6.070999e-05 6.070999e-05 6.070999e-05
## 1399 1400 1401 1402 1403 1404
## 6.070999e-05 6.070999e-05 6.070999e-05 6.070999e-05 6.070999e-05 6.070999e-05
## 1405 1406 1407 1408 1409 1410
## 6.070999e-05 1.491339e-04 1.491339e-04 1.491339e-04 1.491339e-04 1.491339e-04
## 1411 1412 1413 1414 1415 1416
## 1.491339e-04 1.491339e-04 1.491339e-04 4.268815e-03 4.268815e-03 6.383726e-06
## 1417 1418 1419 1420 1421 1422
## 6.383726e-06 6.383726e-06 6.383726e-06 6.383726e-06 6.383726e-06 6.383726e-06
## 1423 1424 1425 1426 1427 1428
## 6.383726e-06 6.383726e-06 6.383726e-06 6.383726e-06 6.383726e-06 6.383726e-06
## 1429 1430 1431 1432 1433 1434
## 6.383726e-06 6.383726e-06 6.383726e-06 6.383726e-06 6.383726e-06 6.383726e-06
## 1435 1436 1437 1438 1439 1440
## 6.383726e-06 6.383726e-06 6.383726e-06 6.383726e-06 8.007593e-03 8.007593e-03
## 1441 1442 1443 1444 1445 1446
## 1.452635e-04 1.452635e-04 1.452635e-04 1.452635e-04 1.452635e-04 1.452635e-04
## 1447 1448 1449 1450 1451 1452
## 1.452635e-04 1.452635e-04 1.452635e-04 1.452635e-04 1.331692e-04 1.331692e-04
## 1453 1454 1455 1456 1457 1458
## 1.331692e-04 1.331692e-04 1.331692e-04 1.331692e-04 1.331692e-04 1.331692e-04
## 1459 1460 1461 1462 1463 1464
## 1.331692e-04 1.331692e-04 1.331692e-04 1.331692e-04 6.020883e-03 6.020883e-03
## 1465 1466 1467 1468 1469 1470
## 6.020883e-03 2.749507e-04 2.749507e-04 2.749507e-04 2.749507e-04 2.749507e-04
## 1471 1472 1473 1474 1475 1476
## 2.749507e-04 2.749507e-04 2.749507e-04 2.749507e-04 4.938717e-03 4.938717e-03
## 1477 1478 1479 1480 1481 1482
## 4.938717e-03 4.938717e-03 4.938717e-03 4.938717e-03 6.544612e-05 6.544612e-05
## 1483 1484 1485 1486 1487 1488
## 6.544612e-05 6.544612e-05 6.544612e-05 6.544612e-05 6.544612e-05 6.544612e-05
## 1489 1490 1491 1492 1493 1494
## 6.544612e-05 6.544612e-05 6.650342e-06 6.650342e-06 6.650342e-06 6.650342e-06
## 1495 1496 1497 1498 1499 1500
## 6.650342e-06 6.650342e-06 6.650342e-06 6.650342e-06 6.650342e-06 6.650342e-06
## 1501 1502 1503 1504 1505 1506
## 6.650342e-06 6.650342e-06 6.650342e-06 6.650342e-06 6.650342e-06 6.650342e-06
## 1507 1508 1509 1510 1511 1512
## 6.650342e-06 6.650342e-06 6.650342e-06 6.650342e-06 6.650342e-06 6.650342e-06
## 1513 1514 1515 1516 1517 1518
## 6.650342e-06 6.650342e-06 6.650342e-06 5.149560e-06 5.149560e-06 5.149560e-06
## 1519 1520 1521 1522 1523 1524
## 5.149560e-06 5.149560e-06 5.149560e-06 5.149560e-06 5.149560e-06 5.149560e-06
## 1525 1526 1527 1528 1529 1530
## 5.149560e-06 6.530918e-05 6.530918e-05 6.530918e-05 6.530918e-05 6.530918e-05
## 1531 1532 1533 1534 1535 1536
## 6.530918e-05 6.530918e-05 6.530918e-05 6.530918e-05 6.530918e-05 6.530918e-05
## 1537 1538 1539 1540 1541 1542
## 6.530918e-05 6.530918e-05 6.530918e-05 6.530918e-05 6.530918e-05 6.530918e-05
## 1543 1544 1545 1546 1547 1548
## 6.530918e-05 6.530918e-05 6.530918e-05 7.615187e-05 7.615187e-05 7.615187e-05
## 1549 1550 1551 1552 1553 1554
## 7.615187e-05 7.615187e-05 7.615187e-05 7.615187e-05 7.615187e-05 7.615187e-05
## 1555 1556 1557 1558 1559 1560
## 7.615187e-05 7.615187e-05 7.615187e-05 7.615187e-05 7.615187e-05 7.615187e-05
## 1561 1562 1563 1564 1565 1566
## 7.615187e-05 7.615187e-05 7.615187e-05 7.615187e-05 7.615187e-05 2.661408e-06
## 1567 1568 1569 1570 1571 1572
## 2.661408e-06 2.661408e-06 2.661408e-06 2.661408e-06 2.661408e-06 2.661408e-06
## 1573 1574 1575 1576 1577 1578
## 2.661408e-06 2.661408e-06 2.661408e-06 1.264511e-04 1.264511e-04 1.264511e-04
## 1579 1580 1581 1582 1583 1584
## 1.264511e-04 1.264511e-04 1.264511e-04 1.264511e-04 1.264511e-04 1.264511e-04
## 1585 1586 1587 1588 1589 1590
## 4.478204e-03 5.286931e-06 5.286931e-06 5.286931e-06 5.286931e-06 5.286931e-06
## 1591 1592 1593 1594 1595 1596
## 5.286931e-06 5.286931e-06 5.286931e-06 5.286931e-06 5.286931e-06 5.286931e-06
## 1597 1598 1599 1600 1601 1602
## 5.286931e-06 5.286931e-06 5.286931e-06 5.286931e-06 5.286931e-06 5.286931e-06
## 1603 1604 1605 1606 1607 1608
## 5.286931e-06 5.286931e-06 5.286931e-06 5.286931e-06 5.286931e-06 5.286931e-06
## 1609 1610 1611 1612 1613 1614
## 5.286931e-06 5.286931e-06 8.347080e-05 8.347080e-05 8.347080e-05 8.347080e-05
## 1615 1616 1617 1618 1619 1620
## 8.347080e-05 8.347080e-05 8.347080e-05 8.347080e-05 8.347080e-05 8.347080e-05
## 1621 1622 1623 1624 1625 1626
## 8.347080e-05 8.347080e-05 8.347080e-05 8.347080e-05 8.347080e-05 8.347080e-05
## 1627 1628 1629 1630 1631 1632
## 8.347080e-05 8.347080e-05 8.347080e-05 8.347080e-05 7.482458e-05 7.482458e-05
## 1633 1634 1635 1636 1637 1638
## 7.482458e-05 7.482458e-05 7.482458e-05 7.482458e-05 7.482458e-05 7.482458e-05
## 1639 1640 1641 1642 1643 1644
## 7.482458e-05 7.482458e-05 7.482458e-05 7.482458e-05 7.482458e-05 7.482458e-05
## 1645 1646 1647 1648 1649 1650
## 7.482458e-05 7.482458e-05 7.482458e-05 7.482458e-05 7.482458e-05 7.482458e-05
## 1651 1652 1653 1654 1655 1656
## 8.039815e-06 8.039815e-06 8.039815e-06 8.039815e-06 8.039815e-06 8.039815e-06
## 1657 1658 1659 1660 1661 1662
## 8.039815e-06 8.039815e-06 8.039815e-06 8.039815e-06 8.039815e-06 8.039815e-06
## 1663 1664 1665 1666 1667 1668
## 8.039815e-06 8.039815e-06 8.039815e-06 8.039815e-06 8.039815e-06 8.039815e-06
## 1669 1670 1671 1672 1673 1674
## 8.039815e-06 8.039815e-06 8.039815e-06 8.039815e-06 8.039815e-06 8.039815e-06
## 1675 1676 1677 1678 1679 1680
## 8.039815e-06 1.045942e-04 1.045942e-04 1.045942e-04 1.045942e-04 1.045942e-04
## 1681 1682 1683 1684 1685 1686
## 1.045942e-04 1.045942e-04 1.045942e-04 1.045942e-04 1.045942e-04 1.045942e-04
## 1687 1688 1689 1690 1691 1692
## 1.045942e-04 1.045942e-04 1.045942e-04 1.045942e-04 1.045942e-04 1.045942e-04
## 1693 1694 1695 1696 1697 1698
## 1.045942e-04 1.045942e-04 1.045942e-04 2.507358e-03 2.507358e-03 2.507358e-03
## 1699 1700 1701 1702 1703 1704
## 2.507358e-03 2.507358e-03 2.507358e-03 2.507358e-03 2.507358e-03 2.507358e-03
## 1705 1706 1707 1708 1709 1710
## 7.305441e-05 7.305441e-05 7.305441e-05 7.305441e-05 7.305441e-05 7.305441e-05
## 1711 1712 1713 1714 1715 1716
## 7.305441e-05 7.305441e-05 7.305441e-05 7.305441e-05 7.305441e-05 1.901927e-04
## 1717 1718 1719 1720 1721 1722
## 1.901927e-04 1.901927e-04 1.901927e-04 1.901927e-04 1.901927e-04 1.901927e-04
## 1723 1724 1725 1726 1727 1728
## 1.901927e-04 1.901927e-04 1.901927e-04 1.901927e-04 1.901927e-04 1.901927e-04
## 1729 1730 1731 1732 1733 1734
## 1.901927e-04 1.901927e-04 5.130918e-05 5.130918e-05 5.130918e-05 5.130918e-05
## 1735 1736 1737 1738 1739 1740
## 5.130918e-05 5.130918e-05 5.130918e-05 5.130918e-05 5.130918e-05 5.130918e-05which(cooks.distance(mod5) == max(cooks.distance(mod5))) # This is the point!## 1035
## 1035mod5.1 = glm(survival ~ species + avg.Cu, family = "binomial", tbl_allDataLong[-880,]) # Modified model without that one point
summary(mod5.1)##
## Call:
## glm(formula = survival ~ species + avg.Cu, family = "binomial",
## data = tbl_allDataLong[-880, ])
##
## Deviance Residuals:
## Min 1Q Median 3Q Max
## -3.7178 0.0447 0.1639 0.3959 1.9702
##
## Coefficients:
## Estimate Std. Error z value Pr(>|z|)
## (Intercept) 8.085964 1.056187 7.656 1.92e-14 ***
## speciesDaphnia -6.906270 1.034814 -6.674 2.49e-11 ***
## speciesFHM -2.134499 1.101299 -1.938 0.052603 .
## speciesLMB -3.564407 1.037504 -3.436 0.000591 ***
## speciesSnail -3.420513 1.032158 -3.314 0.000920 ***
## speciesZM -1.565184 1.038034 -1.508 0.131597
## avg.Cu -0.121895 0.009201 -13.248 < 2e-16 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## (Dispersion parameter for binomial family taken to be 1)
##
## Null deviance: 1908.87 on 1738 degrees of freedom
## Residual deviance: 943.08 on 1732 degrees of freedom
## AIC: 957.08
##
## Number of Fisher Scoring iterations: 7summary(mod5) # There is a difference between the models with and without this point, so check out the point##
## Call:
## glm(formula = survival ~ species + avg.Cu, family = "binomial",
## data = tbl_allDataLong)
##
## Deviance Residuals:
## Min 1Q Median 3Q Max
## -3.7081 0.0428 0.1670 0.3954 1.9624
##
## Coefficients:
## Estimate Std. Error z value Pr(>|z|)
## (Intercept) 8.034838 1.054858 7.617 2.60e-14 ***
## speciesDaphnia -6.874819 1.034038 -6.649 2.96e-11 ***
## speciesFHM -2.129574 1.101057 -1.934 0.053099 .
## speciesLMB -3.556548 1.037227 -3.429 0.000606 ***
## speciesSnail -3.452813 1.031627 -3.347 0.000817 ***
## speciesZM -1.562740 1.037888 -1.506 0.132146
## avg.Cu -0.120350 0.009099 -13.227 < 2e-16 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## (Dispersion parameter for binomial family taken to be 1)
##
## Null deviance: 1911.74 on 1739 degrees of freedom
## Residual deviance: 950.09 on 1733 degrees of freedom
## AIC: 964.09
##
## Number of Fisher Scoring iterations: 7print(tbl_allDataLong[880,]) # this point is a bluegill in a low concentration tank that died## tank treatGroup species survival avg.Cu
## 880 2 Low Snail 0 9.489672# This point is problematic because it's the reference species, and the reference species has very high survival; this individual was a source of large variation in our reference species (reason it has a lot of power!)
# Takeaway: this is an unusual but legitimate data point# Now... is there pseudoreplication by tank?
mod5.2 = glm(survival ~ species + scale(avg.Cu), family = "binomial", tbl_allDataLong) # Return to the original model, but this time scale everything so we can compare it to the next model
mod5.3 = glmer(survival ~ species + scale(avg.Cu) + (1|tank), family = "binomial", tbl_allDataLong) # include jar ID as a random effect
summary(mod5.2)##
## Call:
## glm(formula = survival ~ species + scale(avg.Cu), family = "binomial",
## data = tbl_allDataLong)
##
## Deviance Residuals:
## Min 1Q Median 3Q Max
## -3.7081 0.0428 0.1670 0.3954 1.9624
##
## Coefficients:
## Estimate Std. Error z value Pr(>|z|)
## (Intercept) 5.8098 1.0197 5.697 1.22e-08 ***
## speciesDaphnia -6.8748 1.0340 -6.649 2.96e-11 ***
## speciesFHM -2.1296 1.1011 -1.934 0.053099 .
## speciesLMB -3.5565 1.0372 -3.429 0.000606 ***
## speciesSnail -3.4528 1.0316 -3.347 0.000817 ***
## speciesZM -1.5627 1.0379 -1.506 0.132146
## scale(avg.Cu) -1.4370 0.1086 -13.227 < 2e-16 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## (Dispersion parameter for binomial family taken to be 1)
##
## Null deviance: 1911.74 on 1739 degrees of freedom
## Residual deviance: 950.09 on 1733 degrees of freedom
## AIC: 964.09
##
## Number of Fisher Scoring iterations: 7summary(mod5.3)## Generalized linear mixed model fit by maximum likelihood (Laplace
## Approximation) [glmerMod]
## Family: binomial ( logit )
## Formula: survival ~ species + scale(avg.Cu) + (1 | tank)
## Data: tbl_allDataLong
##
## AIC BIC logLik deviance df.resid
## 950.2 993.9 -467.1 934.2 1732
##
## Scaled residuals:
## Min 1Q Median 3Q Max
## -43.241 0.022 0.095 0.232 1.451
##
## Random effects:
## Groups Name Variance Std.Dev.
## tank (Intercept) 0.3331 0.5771
## Number of obs: 1740, groups: tank, 24
##
## Fixed effects:
## Estimate Std. Error z value Pr(>|z|)
## (Intercept) 6.3513 1.0408 6.102 1.05e-09 ***
## speciesDaphnia -7.5032 1.0546 -7.115 1.12e-12 ***
## speciesFHM -2.8402 1.1296 -2.514 0.011927 *
## speciesLMB -4.0417 1.0548 -3.832 0.000127 ***
## speciesSnail -3.8343 1.0401 -3.686 0.000227 ***
## speciesZM -1.8380 1.0402 -1.767 0.077233 .
## scale(avg.Cu) -1.5162 0.1663 -9.120 < 2e-16 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Correlation of Fixed Effects:
## (Intr) spcsDp spcFHM spcLMB spcsSn spcsZM
## speciesDphn -0.986
## speciesFHM -0.906 0.900
## speciesLMB -0.962 0.955 0.892
## speciesSnal -0.968 0.960 0.896 0.952
## speciesZM -0.956 0.945 0.886 0.942 0.952
## scale(vg.C) -0.117 0.158 0.048 0.055 0.044 0.018AIC(mod5.3) # the random effect model is a better fit## [1] 950.2488AIC(mod5.2)## [1] 964.092hist(residuals(mod5.3)) # and the residual distribution hasn't changed
(mod5)##
## Call: glm(formula = survival ~ species + avg.Cu, family = "binomial",
## data = tbl_allDataLong)
##
## Coefficients:
## (Intercept) speciesDaphnia speciesFHM speciesLMB speciesSnail
## 8.0348 -6.8748 -2.1296 -3.5565 -3.4528
## speciesZM avg.Cu
## -1.5627 -0.1203
##
## Degrees of Freedom: 1739 Total (i.e. Null); 1733 Residual
## Null Deviance: 1912
## Residual Deviance: 950.1 AIC: 964.1(fhm_lc10 = boot::inv.logit(8.035+-2.13 + 31*-0.120)) # Just for fun, what concentration protects 90% of fathead minnow?## [1] 0.89889441.7.5 Figure
colnames(tbl_allDataLong) = c("Tank", "treatGroup", "Species", "Survival", "avgCu")
tbl_allDataLong$Species <- gsub("BLG", "Bluegill", tbl_allDataLong$Species)
tbl_allDataLong$Species <- gsub("FHM", "Fathead minnow", tbl_allDataLong$Species)
tbl_allDataLong$Species <- gsub("LMB", "Largemouth bass", tbl_allDataLong$Species)
tbl_allDataLong$Species <- gsub("Snail", "Banded mystery snail", tbl_allDataLong$Species)
tbl_allDataLong$Species <- gsub("ZM", "Zebra mussel", tbl_allDataLong$Species)
intercept = 8.035 # GLM coefficients
copperslope = -0.120
daphnia = -6.875
fathead = -2.13
bass = -3.557
bms = -3.453
zm = -1.563
fakeCus = seq(from = 0, to = 40, length = 1000) # Create many copper concentration values
bluegill_y = boot::inv.logit(8.035 + fakeCus*-0.120) # Calculate species response at those copper values
daphnia_y = boot::inv.logit(8.035+-6.875 + fakeCus*-0.120)
fathead_y = boot::inv.logit(8.035+-2.13 + fakeCus*-0.120)
bass_y = boot::inv.logit(8.035+-3.557 + fakeCus*-0.120)
bms_y = boot::inv.logit(8.035+-3.453 + fakeCus*-0.120)
zm_y = boot::inv.logit(8.035+-1.563 + fakeCus*-0.120)
Figure5df = data.frame(fakeCus, bluegill_y, daphnia_y, fathead_y, bass_y, bms_y, zm_y) # bundle into a dataframe
plot_survivalLong = ggplot() +
geom_line(data = Figure5df, aes(x = fakeCus, y = bluegill_y), color = "#E69F00", size = 1) +
geom_line(data = Figure5df, aes(x = fakeCus, y = daphnia_y), color = "#56B4E9", size = 1) +
geom_line(data = Figure5df, aes(x = fakeCus, y = fathead_y), color = "#009E73", size = 1) +
geom_line(data = Figure5df, aes(x = fakeCus, y = bass_y), color = "#D55E00", size = 1) +
geom_line(data = Figure5df, aes(x = fakeCus, y = bms_y), color = "#0072B2", size = 1) +
geom_line(data = Figure5df, aes(x = fakeCus, y = zm_y), color = "#CC79A7", size = 1) +
geom_point(data = tbl_survival_byCu, aes(x=avg.Cu, y=mean, color=Species, fill=Species), size = 3) +
scale_color_manual(values = c("Bluegill" = "#E69F00", "Daphnia" = "#56B4E9", "Fathead minnow" = "#009E73", "Largemouth bass" = "#D55E00", "Banded mystery snail" = "#0072B2", "Zebra mussel" = "#CC79A7"))+
theme_classic() +
theme(axis.text = element_text(size = 14, color = "black"),
axis.title = element_text(size = 16, color = "black", face = "bold"),
axis.line = element_line(size = 1.5, color = "black"),
axis.ticks = element_line(size = 1.2, color = "black"),
axis.ticks.length = unit(0.25, "cm"),
legend.position = "right",
legend.text = element_text(size = 14, colour = "black"),
legend.title = element_text(size = 14, face = "bold")) +
annotate("segment", x=8.39, xend=8.39, y=0.48, yend=0.52, color="black", size=2)+ #8.39 is the mean LC50 from EPA data on daphnia
annotate("rect", xmin=3.9, xmax=12.88, ymin=0.48, ymax=0.52, alpha=0.2, color="black", fill="black")+ #daphnia LC50 stdev is 4.49
ylim(ymin=0, ymax=1) +
xlab("\nCopper concentration (µg Cu/L)") +
ylab("Survival\n")## Warning: Using `size` aesthetic for lines was deprecated in ggplot2 3.4.0.
## ℹ Please use `linewidth` instead.## Warning: The `size` argument of `element_line()` is deprecated as of ggplot2 3.4.0.
## ℹ Please use the `linewidth` argument instead.ggsave(filename="Figure5.jpeg", plot=plot_survivalLong, width=9, height=5, dpi=500, units="in")1.8 Conclude nontarget analysis
## Close and remove channels
close(cnxn_nt)
rm(cnxn_nt)2. Veliger experiment
2.1 Set-up
# Connect to veliger experiment data
driver_vel <- "Driver={Microsoft Access Driver (*.mdb, *.accdb)};" # Set up the driver info
accdbpath_vel <- "C:/Users/Dahlbergs/Dropbox/Angelique/PhD/Writing/4. Bioavailability modeling/DRUM/veliger_exp.accdb" # This leads to the Access database containing veliger experiment data
path_vel <- paste0(driver_vel,"DBQ=", accdbpath_vel) # Set up database path
cnxn_vel <- odbcDriverConnect(path_vel) # Establish connection for database
#Note: you may need to install a driver for MS Access outside of R in order to make the connection to this database. See https://leowong.ca/blog/connect-to-microsoft-access-database-via-r/2.2 Water chemistry
tbl_jarWaterChem <- sqlQuery(cnxn_vel, "SELECT tbl_jar.jar_id, tbl_jar.cuGroup, tbl_water_chem.pH, tbl_water_chem.Temp, tbl_water_chem.DO, tbl_water_chem.specCond FROM tbl_jar INNER JOIN tbl_water_chem ON tbl_jar.jar_id = tbl_water_chem.jar_id WHERE (((tbl_jar.jar_id)<37));", stringsAsFactors = FALSE) # Load data into R dataframesgroup_by(tbl_jarWaterChem, cuGroup) %>%
summarise(
mean = mean(pH),
sd = sd(pH)
)## # A tibble: 7 × 3
## cuGroup mean sd
## <chr> <dbl> <dbl>
## 1 C1 8.58 0.0516
## 2 C2 8.60 0.0280
## 3 C3 8.59 0.0197
## 4 C4 8.60 0.0638
## 5 C5 8.59 0.0357
## 6 C6 8.61 0.0299
## 7 Control 8.60 0.0168group_by(tbl_jarWaterChem, cuGroup) %>%
summarise(
mean = mean(Temp),
sd = sd(Temp)
)## # A tibble: 7 × 3
## cuGroup mean sd
## <chr> <dbl> <dbl>
## 1 C1 26.8 1.57
## 2 C2 27.0 1.48
## 3 C3 26.8 1.59
## 4 C4 26.2 1.37
## 5 C5 26.6 1.46
## 6 C6 26.8 1.46
## 7 Control 26.3 1.27group_by(tbl_jarWaterChem, cuGroup) %>%
summarise(
mean = mean(DO),
sd = sd(DO)
)## # A tibble: 7 × 3
## cuGroup mean sd
## <chr> <dbl> <dbl>
## 1 C1 7.40 0.175
## 2 C2 7.47 0.142
## 3 C3 7.42 0.0947
## 4 C4 7.48 0.143
## 5 C5 7.49 0.133
## 6 C6 7.44 0.0825
## 7 Control 7.48 0.192group_by(tbl_jarWaterChem, cuGroup) %>%
summarise(
mean = mean(specCond),
sd = sd(specCond)
)## # A tibble: 7 × 3
## cuGroup mean sd
## <chr> <dbl> <dbl>
## 1 C1 453. 4.15
## 2 C2 452. 2.84
## 3 C3 456. 9.66
## 4 C4 452. 4.47
## 5 C5 452 5.98
## 6 C6 453. 8.18
## 7 Control 451. 8.28# Compare variability of different groups to determine if appropriate for ANOVA
leveneTest(tbl_jarWaterChem$Temp~tbl_jarWaterChem$cuGroup)## Warning in leveneTest.default(y = y, group = group, ...): group coerced to
## factor.## Levene's Test for Homogeneity of Variance (center = median)
## Df F value Pr(>F)
## group 6 0.1702 0.9839
## 65leveneTest(tbl_jarWaterChem$DO~tbl_jarWaterChem$cuGroup) # At least one group is different## Warning in leveneTest.default(y = y, group = group, ...): group coerced to
## factor.## Levene's Test for Homogeneity of Variance (center = median)
## Df F value Pr(>F)
## group 6 2.7402 0.01953 *
## 65
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1leveneTest(tbl_jarWaterChem$pH~tbl_jarWaterChem$cuGroup)## Warning in leveneTest.default(y = y, group = group, ...): group coerced to
## factor.## Levene's Test for Homogeneity of Variance (center = median)
## Df F value Pr(>F)
## group 6 1.4055 0.226
## 65leveneTest(tbl_jarWaterChem$specCond~tbl_jarWaterChem$cuGroup)## Warning in leveneTest.default(y = y, group = group, ...): group coerced to
## factor.## Levene's Test for Homogeneity of Variance (center = median)
## Df F value Pr(>F)
## group 6 0.9818 0.4449
## 65# Visualize variability differences
boxplot(tbl_jarWaterChem$Temp~tbl_jarWaterChem$cuGroup)
boxplot(tbl_jarWaterChem$DO~tbl_jarWaterChem$cuGroup)
boxplot(tbl_jarWaterChem$pH~tbl_jarWaterChem$cuGroup)
boxplot(tbl_jarWaterChem$specCond~tbl_jarWaterChem$cuGroup)
# Consider outliers (all together to get big picture)
hist(tbl_jarWaterChem$Temp)
hist(tbl_jarWaterChem$DO)
hist(tbl_jarWaterChem$pH)
hist(tbl_jarWaterChem$specCond)
# no outliers appear to be unreasonable, inaccurate, or worth excluding(aov_temp <- oneway.test(Temp~cuGroup, tbl_jarWaterChem, var.equal = TRUE)) # Temperatures do not vary between the jar categories##
## One-way analysis of means
##
## data: Temp and cuGroup
## F = 0.44559, num df = 6, denom df = 65, p-value = 0.8455(aov_do <- oneway.test(DO~cuGroup, tbl_jarWaterChem, var.equal = FALSE)) # DOs do not vary between the tanks and the jar categories##
## One-way analysis of means (not assuming equal variances)
##
## data: DO and cuGroup
## F = 0.53686, num df = 6.000, denom df = 28.599, p-value = 0.7757(aov_ph <- oneway.test(pH~cuGroup, tbl_jarWaterChem, var.equal = TRUE)) # pH does not vary between the jar categories##
## One-way analysis of means
##
## data: pH and cuGroup
## F = 0.48089, num df = 6, denom df = 65, p-value = 0.8202(aov_specCond <- oneway.test(specCond~cuGroup, tbl_jarWaterChem, var.equal = TRUE)) # conductance does not vary between the jar categories##
## One-way analysis of means
##
## data: specCond and cuGroup
## F = 0.62499, num df = 6, denom df = 65, p-value = 0.70962.3 Total ammonia nitrogen (TAN) in tanks
tbl_jarNH4 <- sqlQuery(cnxn_vel, "SELECT tbl_nh4.jar_id, tbl_jar.cuGroup, tbl_nh4.NH4 FROM tbl_jar INNER JOIN tbl_nh4 ON tbl_jar.jar_id = tbl_nh4.jar_id WHERE (((tbl_nh4.jar_id)<37));",stringsAsFactors = FALSE)group_by(tbl_jarNH4, cuGroup) %>%
summarise(
mean = mean(NH4, na.rm=T),
sd = sd(NH4, na.rm=T)
)## # A tibble: 7 × 3
## cuGroup mean sd
## <chr> <dbl> <dbl>
## 1 C1 0.0514 0.0212
## 2 C2 0.0612 0.0125
## 3 C3 0.078 0.0262
## 4 C4 0.0782 0.0205
## 5 C5 0.0728 0.0222
## 6 C6 0.0734 0.00767
## 7 Control 0.0554 0.0213# Compare variability of different groups to determine if appropriate for ANOVA
leveneTest(tbl_jarNH4$NH4~tbl_jarNH4$cuGroup)## Warning in leveneTest.default(y = y, group = group, ...): group coerced to
## factor.## Levene's Test for Homogeneity of Variance (center = median)
## Df F value Pr(>F)
## group 6 0.5885 0.7365
## 27# Visualize variability differences
boxplot(tbl_jarNH4$NH4~tbl_jarNH4$cuGroup)
# Consider outliers (all together to get big picture)
hist(tbl_jarNH4$NH4) # no outliers appear to be unreasonable, inaccurate, or worth excluding
(aov_NH4 <- oneway.test(NH4~cuGroup, tbl_jarNH4, var.equal = TRUE)) # NH4 does not vary between the jar categories##
## One-way analysis of means
##
## data: NH4 and cuGroup
## F = 1.5627, num df = 6, denom df = 27, p-value = 0.19642.4 Copper concentration
tbl_jarCu <- sqlQuery(cnxn_vel, "TRANSFORM First(tbl_cu.calculatedCu) AS FirstOfcalculatedCu SELECT tbl_cu.jar_id FROM tbl_cu WHERE (((tbl_cu.jar_id)>0 And (tbl_cu.jar_id)<37)) GROUP BY tbl_cu.jar_id PIVOT tbl_cu.Date;", stringsAsFactors = FALSE)2.5 Veliger survival
tbl_jarSurvival <- sqlQuery(cnxn_vel, "SELECT qry_veliger_jar.jar_id, qry_veliger_jar.[SumOf# alive], qry_veliger_jar.[SumOf# dead], Round([total],2) AS percentAlive, Round([7/21/2022],2) AS CuStart, Round([7/22/2022],2) AS CuEnd FROM qry_veliger_jar INNER JOIN qry_cu_jar ON qry_veliger_jar.jar_id = qry_cu_jar.jar_id GROUP BY qry_veliger_jar.jar_id, qry_veliger_jar.[SumOf# alive], qry_veliger_jar.[SumOf# dead], Round([total],2), Round([7/21/2022],2), Round([7/22/2022],2);", stringsAsFactors = FALSE)# Combine records (i.e., for pH, temp, DO, and specCond) for each jar by finding the average per jar
(tbl_jarWaterChem_means = tbl_jarWaterChem %>%
group_by(jar_id) %>% #Make sub groups for each unique jar_id
summarize(pH = mean(pH), #Collapse the values from each subgroup
Temp = mean(Temp),
DO = mean(DO),
specCond = mean(specCond),
cuGroup = first(cuGroup)))## # A tibble: 36 × 6
## jar_id pH Temp DO specCond cuGroup
## <dbl> <dbl> <dbl> <dbl> <dbl> <chr>
## 1 1 8.6 25.6 7.44 472. C3
## 2 2 8.60 25.6 7.50 456. C4
## 3 3 8.62 25.7 7.54 452. C5
## 4 4 8.59 26.0 7.35 454 Control
## 5 5 8.6 26.2 7.54 448. C5
## 6 6 8.60 26.0 7.48 446. Control
## 7 7 8.62 26.0 7.52 448 Control
## 8 8 8.62 26.1 7.51 452. C4
## 9 9 8.62 26.2 7.52 450. C4
## 10 10 8.57 26.2 7.44 452. C1
## # … with 26 more rows(tbl_jarData = left_join(tbl_jarSurvival, tbl_jarWaterChem_means, by = "jar_id")) #Join data together, with rows being bound by matching jar_id values## jar_id SumOf# alive SumOf# dead percentAlive CuStart CuEnd pH Temp
## 1 1 30 78 27.78 90.94 83.47 8.600 25.55
## 2 2 39 84 31.71 124.10 102.50 8.605 25.65
## 3 3 20 73 21.51 157.33 131.84 8.625 25.70
## 4 4 49 130 27.37 2.50 67.06 8.590 26.05
## 5 5 20 83 19.42 156.31 129.08 8.600 26.20
## 6 6 6 64 8.57 2.50 7.60 8.605 25.95
## 7 7 48 191 20.08 2.50 2.50 8.620 25.95
## 8 8 13 33 28.26 98.67 87.97 8.620 26.10
## 9 9 23 99 18.85 99.64 86.70 8.625 26.25
## 10 10 8 54 12.90 16.15 16.98 8.575 26.20
## 11 11 63 265 19.21 173.24 150.04 8.625 26.10
## 12 12 6 77 7.23 14.64 18.46 8.560 26.60
## 13 13 19 121 13.57 43.68 48.57 8.590 26.40
## 14 14 51 128 28.49 2.50 2.50 8.595 26.20
## 15 15 16 138 10.39 104.49 92.83 8.635 26.00
## 16 16 26 127 16.99 125.79 114.61 8.600 26.55
## 17 17 16 101 13.68 2.50 7.60 8.585 26.90
## 18 18 2 45 4.26 146.27 137.66 8.620 26.40
## 19 19 0 32 0.00 2.50 7.60 8.610 26.55
## 20 20 8 66 10.81 93.59 87.92 8.535 27.00
## 21 21 3 42 6.67 39.15 37.12 8.560 26.70
## 22 22 16 122 11.59 41.05 67.75 8.610 26.70
## 23 23 35 120 22.58 9.11 22.67 8.510 26.45
## 24 24 37 193 16.09 40.55 62.28 8.585 27.00
## 25 25 32 133 19.39 41.45 54.63 8.595 27.30
## 26 26 20 94 17.54 114.38 138.45 8.595 27.05
## 27 27 7 91 7.14 91.41 113.00 8.595 27.10
## 28 28 13 74 14.94 153.44 136.68 8.550 27.30
## 29 29 8 74 9.76 190.60 162.92 8.565 27.25
## 30 30 5 41 10.87 190.68 163.14 8.625 27.05
## 31 31 28 104 21.21 51.06 51.16 8.615 27.05
## 32 32 1 45 2.17 52.73 49.05 8.600 27.35
## 33 33 2 57 3.39 86.55 75.52 8.565 27.65
## 34 34 17 67 20.24 21.64 17.64 8.645 27.30
## 35 35 4 44 8.33 51.91 47.09 8.615 27.35
## 36 36 1 67 1.47 18.43 19.96 8.620 27.60
## DO specCond cuGroup
## 1 7.440 471.5 C3
## 2 7.495 456.5 C4
## 3 7.535 451.5 C5
## 4 7.350 454.0 Control
## 5 7.540 448.5 C5
## 6 7.480 445.5 Control
## 7 7.520 448.0 Control
## 8 7.510 452.5 C4
## 9 7.515 449.5 C4
## 10 7.435 451.5 C1
## 11 7.480 450.5 C6
## 12 7.410 450.5 C1
## 13 7.565 448.5 C2
## 14 7.560 459.0 Control
## 15 7.540 449.0 C4
## 16 7.575 450.0 C5
## 17 7.495 448.0 Control
## 18 7.465 451.0 C6
## 19 7.480 449.0 Control
## 20 7.330 451.5 C4
## 21 7.520 452.0 C2
## 22 7.425 450.5 C3
## 23 7.385 453.0 C1
## 24 7.440 450.0 C3
## 25 7.410 448.5 C3
## 26 7.450 448.0 C6
## 27 7.485 448.0 C5
## 28 7.335 462.0 C5
## 29 7.405 465.0 C6
## 30 7.405 452.5 C6
## 31 7.445 453.5 C2
## 32 7.435 455.0 C2
## 33 7.405 458.5 C3
## 34 7.420 456.0 C1
## 35 7.405 453.0 C2
## 36 7.375 453.5 C1#Make a new column (TotalObs) using existing columns (math/funcs)
tbl_jarData = tbl_jarData %>%
mutate(TotalObs = `SumOf# alive` + `SumOf# dead`)for(i in 1:nrow(tbl_jarData)) { #For each row (i)
numAlive = tbl_jarData$`SumOf# alive`[i] #How many live veligers we need
tmp1 = tbl_jarData[i,-c(2,3,4)] #Clip out this row's key info
tmp1$survive = 1 #Make a column that marks these individuals as alive
tmp1 = tmp1 %>% #Repeat this row a bunch of times, once for each live veliger. For ref: https://stackoverflow.com/questions/11121385/repeat-rows-of-a-data-frame
slice(rep(1:n(), each = numAlive))
#Same thing but for the dead veligers
numDead = tbl_jarData$`SumOf# dead`[i]
tmp2 = tbl_jarData[i,-c(2,3,4)]
tmp2$survive = 0
tmp2 = tmp2 %>%
slice(rep(1:n(), each = numDead))
#Combine the two sets of repeated rows
tmp3 = bind_rows(tmp1, tmp2)
#Either create a long data sheet or else append to it the rows we've made
if(i == 1) {
tbl_jarData_long = tmp3
} else {
tbl_jarData_long = bind_rows(tbl_jarData_long, tmp3)
}
}
#Check to be sure we succeeded
sum(tbl_jarData$TotalObs)## [1] 40492.6 GLM
mod7 = glm(survive ~ pH , family = "binomial", tbl_jarData_long)
summary(mod7)##
## Call:
## glm(formula = survive ~ pH, family = "binomial", data = tbl_jarData_long)
##
## Deviance Residuals:
## Min 1Q Median 3Q Max
## -0.6257 -0.6185 -0.6113 -0.6015 1.9192
##
## Coefficients:
## Estimate Std. Error z value Pr(>|z|)
## (Intercept) -10.344 12.808 -0.808 0.419
## pH 1.019 1.490 0.684 0.494
##
## (Dispersion parameter for binomial family taken to be 1)
##
## Null deviance: 3703.4 on 4048 degrees of freedom
## Residual deviance: 3702.9 on 4047 degrees of freedom
## AIC: 3706.9
##
## Number of Fisher Scoring iterations: 3mod8 = glm(survive ~ Temp , family = "binomial", tbl_jarData_long)
summary(mod8)##
## Call:
## glm(formula = survive ~ Temp, family = "binomial", data = tbl_jarData_long)
##
## Deviance Residuals:
## Min 1Q Median 3Q Max
## -0.7593 -0.6689 -0.5801 -0.5014 2.1442
##
## Coefficients:
## Estimate Std. Error z value Pr(>|z|)
## (Intercept) 12.24747 2.08204 5.882 4.04e-09 ***
## Temp -0.52226 0.07878 -6.630 3.37e-11 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## (Dispersion parameter for binomial family taken to be 1)
##
## Null deviance: 3703.4 on 4048 degrees of freedom
## Residual deviance: 3658.0 on 4047 degrees of freedom
## AIC: 3662
##
## Number of Fisher Scoring iterations: 4mod9 = glm(survive ~ DO , family = "binomial", tbl_jarData_long)
summary(mod9)##
## Call:
## glm(formula = survive ~ DO, family = "binomial", data = tbl_jarData_long)
##
## Deviance Residuals:
## Min 1Q Median 3Q Max
## -0.6425 -0.6243 -0.6051 -0.5877 1.9383
##
## Coefficients:
## Estimate Std. Error z value Pr(>|z|)
## (Intercept) -8.8855 4.7651 -1.865 0.0622 .
## DO 0.9786 0.6381 1.534 0.1251
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## (Dispersion parameter for binomial family taken to be 1)
##
## Null deviance: 3703.4 on 4048 degrees of freedom
## Residual deviance: 3701.0 on 4047 degrees of freedom
## AIC: 3705
##
## Number of Fisher Scoring iterations: 4mod10 = glm(survive ~ TotalObs , family = "binomial", tbl_jarData_long)
summary(mod10)##
## Call:
## glm(formula = survive ~ TotalObs, family = "binomial", data = tbl_jarData_long)
##
## Deviance Residuals:
## Min 1Q Median 3Q Max
## -0.7433 -0.6211 -0.5883 -0.5507 1.9955
##
## Coefficients:
## Estimate Std. Error z value Pr(>|z|)
## (Intercept) -1.9552790 0.0932935 -20.958 < 2e-16 ***
## TotalObs 0.0024699 0.0005315 4.647 3.36e-06 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## (Dispersion parameter for binomial family taken to be 1)
##
## Null deviance: 3703.4 on 4048 degrees of freedom
## Residual deviance: 3682.3 on 4047 degrees of freedom
## AIC: 3686.3
##
## Number of Fisher Scoring iterations: 4mod11 = glm(survive ~ specCond , family = "binomial", tbl_jarData_long)
summary(mod11)##
## Call:
## glm(formula = survive ~ specCond, family = "binomial", data = tbl_jarData_long)
##
## Deviance Residuals:
## Min 1Q Median 3Q Max
## -0.7785 -0.6166 -0.5931 -0.5778 1.9661
##
## Coefficients:
## Estimate Std. Error z value Pr(>|z|)
## (Intercept) -14.422009 3.461194 -4.167 3.09e-05 ***
## specCond 0.028385 0.007645 3.713 0.000205 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## (Dispersion parameter for binomial family taken to be 1)
##
## Null deviance: 3703.4 on 4048 degrees of freedom
## Residual deviance: 3690.3 on 4047 degrees of freedom
## AIC: 3694.3
##
## Number of Fisher Scoring iterations: 4mod12 = glm(survive ~ TotalObs + specCond + Temp, family = "binomial", tbl_jarData_long)
summary(mod12)##
## Call:
## glm(formula = survive ~ TotalObs + specCond + Temp, family = "binomial",
## data = tbl_jarData_long)
##
## Deviance Residuals:
## Min 1Q Median 3Q Max
## -0.8962 -0.6357 -0.5690 -0.4975 2.1327
##
## Coefficients:
## Estimate Std. Error z value Pr(>|z|)
## (Intercept) -5.7656645 4.6807834 -1.232 0.218034
## TotalObs 0.0021033 0.0005891 3.570 0.000357 ***
## specCond 0.0316047 0.0080221 3.940 8.16e-05 ***
## Temp -0.3940518 0.0834877 -4.720 2.36e-06 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## (Dispersion parameter for binomial family taken to be 1)
##
## Null deviance: 3703.4 on 4048 degrees of freedom
## Residual deviance: 3636.2 on 4045 degrees of freedom
## AIC: 3644.2
##
## Number of Fisher Scoring iterations: 4mod13 = glm(survive ~ TotalObs + Temp, family="binomial", tbl_jarData_long)
summary(mod13)##
## Call:
## glm(formula = survive ~ TotalObs + Temp, family = "binomial",
## data = tbl_jarData_long)
##
## Deviance Residuals:
## Min 1Q Median 3Q Max
## -0.7419 -0.6598 -0.5772 -0.4908 2.1713
##
## Coefficients:
## Estimate Std. Error z value Pr(>|z|)
## (Intercept) 10.3973904 2.2213920 4.681 2.86e-06 ***
## TotalObs 0.0014647 0.0005599 2.616 0.0089 **
## Temp -0.4608202 0.0830233 -5.550 2.85e-08 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## (Dispersion parameter for binomial family taken to be 1)
##
## Null deviance: 3703.4 on 4048 degrees of freedom
## Residual deviance: 3651.2 on 4046 degrees of freedom
## AIC: 3657.2
##
## Number of Fisher Scoring iterations: 4# Is specCond really important to include?
mod14 = glm(survive ~ TotalObs + Temp + CuStart, family = "binomial", tbl_jarData_long) # Try it without
mod15 = glm(survive ~ TotalObs + Temp + specCond + CuStart, family = "binomial", tbl_jarData_long) # Now try it with
(AIC(mod14)) ## [1] 3656.925(AIC(mod15)) # specCond should be included## [1] 3642.187# Assumptions:
# 1) Outcome is binary (YES - survived/died)
# 2) Linear relationship between logit(outcome) and each predictor variable
# 3) No extreme values or outliers in continuous predictors:
mod15.data <- augment(mod15) %>%
mutate(index = 1:n())
mod15.data %>% top_n(3, .cooksd)## # A tibble: 8 × 12
## survive TotalObs Temp specCond CuStart .fitted .resid .std.r…¹ .hat .sigma
## <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl>
## 1 1 82 27.2 465 191. -1.79 1.97 1.97 0.00280 0.947
## 2 1 82 27.2 465 191. -1.79 1.97 1.97 0.00280 0.947
## 3 1 82 27.2 465 191. -1.79 1.97 1.97 0.00280 0.947
## 4 1 82 27.2 465 191. -1.79 1.97 1.97 0.00280 0.947
## 5 1 82 27.2 465 191. -1.79 1.97 1.97 0.00280 0.947
## 6 1 82 27.2 465 191. -1.79 1.97 1.97 0.00280 0.947
## 7 1 82 27.2 465 191. -1.79 1.97 1.97 0.00280 0.947
## 8 1 82 27.2 465 191. -1.79 1.97 1.97 0.00280 0.947
## # … with 2 more variables: .cooksd <dbl>, index <int>, and abbreviated variable
## # name ¹.std.residggplot(mod15.data, aes(index, .std.resid)) +
geom_point(aes(color = survive), alpha = .5) +
theme_bw()
mod15.data %>%
filter(abs(.std.resid) > 3) # are these outliers worthy of removing? Decide to keep - these are REAL data from real animals!## # A tibble: 0 × 12
## # … with 12 variables: survive <dbl>, TotalObs <dbl>, Temp <dbl>,
## # specCond <dbl>, CuStart <dbl>, .fitted <dbl>, .resid <dbl>,
## # .std.resid <dbl>, .hat <dbl>, .sigma <dbl>, .cooksd <dbl>, index <int># 4) No multicollinearity:
car::vif(mod15)## TotalObs Temp specCond CuStart
## 1.241470 1.159088 1.145523 1.044316# 5) Residual normality (on logit scale!)
hist(residuals(mod15)) # It's not normal, but it meets the central limit theorem
# 6) Check leveraging points
boot::glm.diag.plots(mod15) # does not appear that any points are potentially leveraging points
cooks.distance(mod15)## 1 2 3 4 5 6
## 2.233834e-03 2.233834e-03 2.233834e-03 2.233834e-03 2.233834e-03 2.233834e-03
## 7 8 9 10 11 12
## 2.233834e-03 2.233834e-03 2.233834e-03 2.233834e-03 2.233834e-03 2.233834e-03
## 13 14 15 16 17 18
## 2.233834e-03 2.233834e-03 2.233834e-03 2.233834e-03 2.233834e-03 2.233834e-03
## 19 20 21 22 23 24
## 2.233834e-03 2.233834e-03 2.233834e-03 2.233834e-03 2.233834e-03 2.233834e-03
## 25 26 27 28 29 30
## 2.233834e-03 2.233834e-03 2.233834e-03 2.233834e-03 2.233834e-03 2.233834e-03
## 31 32 33 34 35 36
## 5.573463e-04 5.573463e-04 5.573463e-04 5.573463e-04 5.573463e-04 5.573463e-04
## 37 38 39 40 41 42
## 5.573463e-04 5.573463e-04 5.573463e-04 5.573463e-04 5.573463e-04 5.573463e-04
## 43 44 45 46 47 48
## 5.573463e-04 5.573463e-04 5.573463e-04 5.573463e-04 5.573463e-04 5.573463e-04
## 49 50 51 52 53 54
## 5.573463e-04 5.573463e-04 5.573463e-04 5.573463e-04 5.573463e-04 5.573463e-04
## 55 56 57 58 59 60
## 5.573463e-04 5.573463e-04 5.573463e-04 5.573463e-04 5.573463e-04 5.573463e-04
## 61 62 63 64 65 66
## 5.573463e-04 5.573463e-04 5.573463e-04 5.573463e-04 5.573463e-04 5.573463e-04
## 67 68 69 70 71 72
## 5.573463e-04 5.573463e-04 5.573463e-04 5.573463e-04 5.573463e-04 5.573463e-04
## 73 74 75 76 77 78
## 5.573463e-04 5.573463e-04 5.573463e-04 5.573463e-04 5.573463e-04 5.573463e-04
## 79 80 81 82 83 84
## 5.573463e-04 5.573463e-04 5.573463e-04 5.573463e-04 5.573463e-04 5.573463e-04
## 85 86 87 88 89 90
## 5.573463e-04 5.573463e-04 5.573463e-04 5.573463e-04 5.573463e-04 5.573463e-04
## 91 92 93 94 95 96
## 5.573463e-04 5.573463e-04 5.573463e-04 5.573463e-04 5.573463e-04 5.573463e-04
## 97 98 99 100 101 102
## 5.573463e-04 5.573463e-04 5.573463e-04 5.573463e-04 5.573463e-04 5.573463e-04
## 103 104 105 106 107 108
## 5.573463e-04 5.573463e-04 5.573463e-04 5.573463e-04 5.573463e-04 5.573463e-04
## 109 110 111 112 113 114
## 9.938707e-04 9.938707e-04 9.938707e-04 9.938707e-04 9.938707e-04 9.938707e-04
## 115 116 117 118 119 120
## 9.938707e-04 9.938707e-04 9.938707e-04 9.938707e-04 9.938707e-04 9.938707e-04
## 121 122 123 124 125 126
## 9.938707e-04 9.938707e-04 9.938707e-04 9.938707e-04 9.938707e-04 9.938707e-04
## 127 128 129 130 131 132
## 9.938707e-04 9.938707e-04 9.938707e-04 9.938707e-04 9.938707e-04 9.938707e-04
## 133 134 135 136 137 138
## 9.938707e-04 9.938707e-04 9.938707e-04 9.938707e-04 9.938707e-04 9.938707e-04
## 139 140 141 142 143 144
## 9.938707e-04 9.938707e-04 9.938707e-04 9.938707e-04 9.938707e-04 9.938707e-04
## 145 146 147 148 149 150
## 9.938707e-04 9.938707e-04 9.938707e-04 8.119792e-05 8.119792e-05 8.119792e-05
## 151 152 153 154 155 156
## 8.119792e-05 8.119792e-05 8.119792e-05 8.119792e-05 8.119792e-05 8.119792e-05
## 157 158 159 160 161 162
## 8.119792e-05 8.119792e-05 8.119792e-05 8.119792e-05 8.119792e-05 8.119792e-05
## 163 164 165 166 167 168
## 8.119792e-05 8.119792e-05 8.119792e-05 8.119792e-05 8.119792e-05 8.119792e-05
## 169 170 171 172 173 174
## 8.119792e-05 8.119792e-05 8.119792e-05 8.119792e-05 8.119792e-05 8.119792e-05
## 175 176 177 178 179 180
## 8.119792e-05 8.119792e-05 8.119792e-05 8.119792e-05 8.119792e-05 8.119792e-05
## 181 182 183 184 185 186
## 8.119792e-05 8.119792e-05 8.119792e-05 8.119792e-05 8.119792e-05 8.119792e-05
## 187 188 189 190 191 192
## 8.119792e-05 8.119792e-05 8.119792e-05 8.119792e-05 8.119792e-05 8.119792e-05
## 193 194 195 196 197 198
## 8.119792e-05 8.119792e-05 8.119792e-05 8.119792e-05 8.119792e-05 8.119792e-05
## 199 200 201 202 203 204
## 8.119792e-05 8.119792e-05 8.119792e-05 8.119792e-05 8.119792e-05 8.119792e-05
## 205 206 207 208 209 210
## 8.119792e-05 8.119792e-05 8.119792e-05 8.119792e-05 8.119792e-05 8.119792e-05
## 211 212 213 214 215 216
## 8.119792e-05 8.119792e-05 8.119792e-05 8.119792e-05 8.119792e-05 8.119792e-05
## 217 218 219 220 221 222
## 8.119792e-05 8.119792e-05 8.119792e-05 8.119792e-05 8.119792e-05 8.119792e-05
## 223 224 225 226 227 228
## 8.119792e-05 8.119792e-05 8.119792e-05 8.119792e-05 8.119792e-05 8.119792e-05
## 229 230 231 232 233 234
## 8.119792e-05 8.119792e-05 8.119792e-05 1.842754e-03 1.842754e-03 1.842754e-03
## 235 236 237 238 239 240
## 1.842754e-03 1.842754e-03 1.842754e-03 1.842754e-03 1.842754e-03 1.842754e-03
## 241 242 243 244 245 246
## 1.842754e-03 1.842754e-03 1.842754e-03 1.842754e-03 1.842754e-03 1.842754e-03
## 247 248 249 250 251 252
## 1.842754e-03 1.842754e-03 1.842754e-03 1.842754e-03 1.842754e-03 8.200571e-05
## 253 254 255 256 257 258
## 8.200571e-05 8.200571e-05 8.200571e-05 8.200571e-05 8.200571e-05 8.200571e-05
## 259 260 261 262 263 264
## 8.200571e-05 8.200571e-05 8.200571e-05 8.200571e-05 8.200571e-05 8.200571e-05
## 265 266 267 268 269 270
## 8.200571e-05 8.200571e-05 8.200571e-05 8.200571e-05 8.200571e-05 8.200571e-05
## 271 272 273 274 275 276
## 8.200571e-05 8.200571e-05 8.200571e-05 8.200571e-05 8.200571e-05 8.200571e-05
## 277 278 279 280 281 282
## 8.200571e-05 8.200571e-05 8.200571e-05 8.200571e-05 8.200571e-05 8.200571e-05
## 283 284 285 286 287 288
## 8.200571e-05 8.200571e-05 8.200571e-05 8.200571e-05 8.200571e-05 8.200571e-05
## 289 290 291 292 293 294
## 8.200571e-05 8.200571e-05 8.200571e-05 8.200571e-05 8.200571e-05 8.200571e-05
## 295 296 297 298 299 300
## 8.200571e-05 8.200571e-05 8.200571e-05 8.200571e-05 8.200571e-05 8.200571e-05
## 301 302 303 304 305 306
## 8.200571e-05 8.200571e-05 8.200571e-05 8.200571e-05 8.200571e-05 8.200571e-05
## 307 308 309 310 311 312
## 8.200571e-05 8.200571e-05 8.200571e-05 8.200571e-05 8.200571e-05 8.200571e-05
## 313 314 315 316 317 318
## 8.200571e-05 8.200571e-05 8.200571e-05 8.200571e-05 8.200571e-05 8.200571e-05
## 319 320 321 322 323 324
## 8.200571e-05 8.200571e-05 8.200571e-05 8.200571e-05 8.200571e-05 8.200571e-05
## 325 326 327 328 329 330
## 6.391811e-04 6.391811e-04 6.391811e-04 6.391811e-04 6.391811e-04 6.391811e-04
## 331 332 333 334 335 336
## 6.391811e-04 6.391811e-04 6.391811e-04 6.391811e-04 6.391811e-04 6.391811e-04
## 337 338 339 340 341 342
## 6.391811e-04 6.391811e-04 6.391811e-04 6.391811e-04 6.391811e-04 6.391811e-04
## 343 344 345 346 347 348
## 6.391811e-04 6.391811e-04 6.391811e-04 6.391811e-04 6.391811e-04 6.391811e-04
## 349 350 351 352 353 354
## 6.391811e-04 6.391811e-04 6.391811e-04 6.391811e-04 6.391811e-04 6.391811e-04
## 355 356 357 358 359 360
## 6.391811e-04 6.391811e-04 6.391811e-04 6.391811e-04 6.391811e-04 6.391811e-04
## 361 362 363 364 365 366
## 6.391811e-04 6.391811e-04 6.391811e-04 6.391811e-04 6.391811e-04 6.391811e-04
## 367 368 369 370 371 372
## 6.391811e-04 6.391811e-04 6.391811e-04 6.391811e-04 6.391811e-04 6.391811e-04
## 373 374 375 376 377 378
## 6.391811e-04 5.801307e-05 5.801307e-05 5.801307e-05 5.801307e-05 5.801307e-05
## 379 380 381 382 383 384
## 5.801307e-05 5.801307e-05 5.801307e-05 5.801307e-05 5.801307e-05 5.801307e-05
## 385 386 387 388 389 390
## 5.801307e-05 5.801307e-05 5.801307e-05 5.801307e-05 5.801307e-05 5.801307e-05
## 391 392 393 394 395 396
## 5.801307e-05 5.801307e-05 5.801307e-05 5.801307e-05 5.801307e-05 5.801307e-05
## 397 398 399 400 401 402
## 5.801307e-05 5.801307e-05 5.801307e-05 5.801307e-05 5.801307e-05 5.801307e-05
## 403 404 405 406 407 408
## 5.801307e-05 5.801307e-05 5.801307e-05 5.801307e-05 5.801307e-05 5.801307e-05
## 409 410 411 412 413 414
## 5.801307e-05 5.801307e-05 5.801307e-05 5.801307e-05 5.801307e-05 5.801307e-05
## 415 416 417 418 419 420
## 5.801307e-05 5.801307e-05 5.801307e-05 5.801307e-05 5.801307e-05 5.801307e-05
## 421 422 423 424 425 426
## 5.801307e-05 5.801307e-05 5.801307e-05 5.801307e-05 5.801307e-05 5.801307e-05
## 427 428 429 430 431 432
## 5.801307e-05 5.801307e-05 5.801307e-05 5.801307e-05 5.801307e-05 5.801307e-05
## 433 434 435 436 437 438
## 5.801307e-05 5.801307e-05 5.801307e-05 5.801307e-05 5.801307e-05 5.801307e-05
## 439 440 441 442 443 444
## 5.801307e-05 5.801307e-05 5.801307e-05 5.801307e-05 5.801307e-05 5.801307e-05
## 445 446 447 448 449 450
## 5.801307e-05 5.801307e-05 5.801307e-05 5.801307e-05 5.801307e-05 5.801307e-05
## 451 452 453 454 455 456
## 5.801307e-05 5.801307e-05 5.801307e-05 5.801307e-05 5.801307e-05 5.801307e-05
## 457 458 459 460 461 462
## 5.801307e-05 5.801307e-05 5.801307e-05 5.801307e-05 5.801307e-05 5.801307e-05
## 463 464 465 466 467 468
## 5.801307e-05 5.801307e-05 5.801307e-05 5.801307e-05 5.801307e-05 5.801307e-05
## 469 470 471 472 473 474
## 5.801307e-05 5.801307e-05 5.801307e-05 5.801307e-05 5.801307e-05 5.801307e-05
## 475 476 477 478 479 480
## 5.801307e-05 5.801307e-05 5.801307e-05 5.801307e-05 5.801307e-05 5.801307e-05
## 481 482 483 484 485 486
## 5.801307e-05 5.801307e-05 5.801307e-05 5.801307e-05 5.801307e-05 5.801307e-05
## 487 488 489 490 491 492
## 5.801307e-05 5.801307e-05 5.801307e-05 5.801307e-05 5.801307e-05 5.801307e-05
## 493 494 495 496 497 498
## 5.801307e-05 5.801307e-05 5.801307e-05 5.801307e-05 5.801307e-05 5.801307e-05
## 499 500 501 502 503 504
## 5.801307e-05 5.801307e-05 5.801307e-05 5.801307e-05 5.801307e-05 1.536271e-03
## 505 506 507 508 509 510
## 1.536271e-03 1.536271e-03 1.536271e-03 1.536271e-03 1.536271e-03 1.536271e-03
## 511 512 513 514 515 516
## 1.536271e-03 1.536271e-03 1.536271e-03 1.536271e-03 1.536271e-03 1.536271e-03
## 517 518 519 520 521 522
## 1.536271e-03 1.536271e-03 1.536271e-03 1.536271e-03 1.536271e-03 1.536271e-03
## 523 524 525 526 527 528
## 1.536271e-03 3.936931e-05 3.936931e-05 3.936931e-05 3.936931e-05 3.936931e-05
## 529 530 531 532 533 534
## 3.936931e-05 3.936931e-05 3.936931e-05 3.936931e-05 3.936931e-05 3.936931e-05
## 535 536 537 538 539 540
## 3.936931e-05 3.936931e-05 3.936931e-05 3.936931e-05 3.936931e-05 3.936931e-05
## 541 542 543 544 545 546
## 3.936931e-05 3.936931e-05 3.936931e-05 3.936931e-05 3.936931e-05 3.936931e-05
## 547 548 549 550 551 552
## 3.936931e-05 3.936931e-05 3.936931e-05 3.936931e-05 3.936931e-05 3.936931e-05
## 553 554 555 556 557 558
## 3.936931e-05 3.936931e-05 3.936931e-05 3.936931e-05 3.936931e-05 3.936931e-05
## 559 560 561 562 563 564
## 3.936931e-05 3.936931e-05 3.936931e-05 3.936931e-05 3.936931e-05 3.936931e-05
## 565 566 567 568 569 570
## 3.936931e-05 3.936931e-05 3.936931e-05 3.936931e-05 3.936931e-05 3.936931e-05
## 571 572 573 574 575 576
## 3.936931e-05 3.936931e-05 3.936931e-05 3.936931e-05 3.936931e-05 3.936931e-05
## 577 578 579 580 581 582
## 3.936931e-05 3.936931e-05 3.936931e-05 3.936931e-05 3.936931e-05 3.936931e-05
## 583 584 585 586 587 588
## 3.936931e-05 3.936931e-05 3.936931e-05 3.936931e-05 3.936931e-05 3.936931e-05
## 589 590 591 592 593 594
## 3.936931e-05 3.936931e-05 3.936931e-05 3.936931e-05 3.936931e-05 3.936931e-05
## 595 596 597 598 599 600
## 3.936931e-05 3.936931e-05 3.936931e-05 3.936931e-05 3.936931e-05 3.936931e-05
## 601 602 603 604 605 606
## 3.936931e-05 3.936931e-05 3.936931e-05 3.936931e-05 3.936931e-05 3.936931e-05
## 607 608 609 610 611 612
## 2.123146e-03 2.123146e-03 2.123146e-03 2.123146e-03 2.123146e-03 2.123146e-03
## 613 614 615 616 617 618
## 7.111357e-05 7.111357e-05 7.111357e-05 7.111357e-05 7.111357e-05 7.111357e-05
## 619 620 621 622 623 624
## 7.111357e-05 7.111357e-05 7.111357e-05 7.111357e-05 7.111357e-05 7.111357e-05
## 625 626 627 628 629 630
## 7.111357e-05 7.111357e-05 7.111357e-05 7.111357e-05 7.111357e-05 7.111357e-05
## 631 632 633 634 635 636
## 7.111357e-05 7.111357e-05 7.111357e-05 7.111357e-05 7.111357e-05 7.111357e-05
## 637 638 639 640 641 642
## 7.111357e-05 7.111357e-05 7.111357e-05 7.111357e-05 7.111357e-05 7.111357e-05
## 643 644 645 646 647 648
## 7.111357e-05 7.111357e-05 7.111357e-05 7.111357e-05 7.111357e-05 7.111357e-05
## 649 650 651 652 653 654
## 7.111357e-05 7.111357e-05 7.111357e-05 7.111357e-05 7.111357e-05 7.111357e-05
## 655 656 657 658 659 660
## 7.111357e-05 7.111357e-05 7.111357e-05 7.111357e-05 7.111357e-05 7.111357e-05
## 661 662 663 664 665 666
## 7.111357e-05 7.111357e-05 7.111357e-05 7.111357e-05 7.111357e-05 7.111357e-05
## 667 668 669 670 671 672
## 7.111357e-05 7.111357e-05 7.111357e-05 7.111357e-05 7.111357e-05 7.111357e-05
## 673 674 675 676 677 678
## 7.111357e-05 7.111357e-05 7.111357e-05 7.111357e-05 9.605419e-04 9.605419e-04
## 679 680 681 682 683 684
## 9.605419e-04 9.605419e-04 9.605419e-04 9.605419e-04 9.605419e-04 9.605419e-04
## 685 686 687 688 689 690
## 9.605419e-04 9.605419e-04 9.605419e-04 9.605419e-04 9.605419e-04 9.605419e-04
## 691 692 693 694 695 696
## 9.605419e-04 9.605419e-04 9.605419e-04 9.605419e-04 9.605419e-04 9.605419e-04
## 697 698 699 700 701 702
## 9.605419e-04 9.605419e-04 9.605419e-04 9.605419e-04 9.605419e-04 9.605419e-04
## 703 704 705 706 707 708
## 9.605419e-04 9.605419e-04 9.605419e-04 9.605419e-04 9.605419e-04 9.605419e-04
## 709 710 711 712 713 714
## 9.605419e-04 9.605419e-04 9.605419e-04 9.605419e-04 9.605419e-04 9.605419e-04
## 715 716 717 718 719 720
## 9.605419e-04 9.605419e-04 9.605419e-04 9.605419e-04 9.605419e-04 9.605419e-04
## 721 722 723 724 725 726
## 9.605419e-04 9.605419e-04 9.605419e-04 9.605419e-04 8.291121e-05 8.291121e-05
## 727 728 729 730 731 732
## 8.291121e-05 8.291121e-05 8.291121e-05 8.291121e-05 8.291121e-05 8.291121e-05
## 733 734 735 736 737 738
## 8.291121e-05 8.291121e-05 8.291121e-05 8.291121e-05 8.291121e-05 8.291121e-05
## 739 740 741 742 743 744
## 8.291121e-05 8.291121e-05 8.291121e-05 8.291121e-05 8.291121e-05 8.291121e-05
## 745 746 747 748 749 750
## 8.291121e-05 8.291121e-05 8.291121e-05 8.291121e-05 8.291121e-05 8.291121e-05
## 751 752 753 754 755 756
## 8.291121e-05 8.291121e-05 8.291121e-05 8.291121e-05 8.291121e-05 8.291121e-05
## 757 758 759 760 761 762
## 8.291121e-05 8.291121e-05 8.291121e-05 8.291121e-05 8.291121e-05 8.291121e-05
## 763 764 765 766 767 768
## 8.291121e-05 8.291121e-05 8.291121e-05 8.291121e-05 8.291121e-05 8.291121e-05
## 769 770 771 772 773 774
## 8.291121e-05 8.291121e-05 8.291121e-05 8.291121e-05 8.291121e-05 8.291121e-05
## 775 776 777 778 779 780
## 8.291121e-05 8.291121e-05 8.291121e-05 8.291121e-05 8.291121e-05 8.291121e-05
## 781 782 783 784 785 786
## 8.291121e-05 8.291121e-05 8.291121e-05 8.291121e-05 8.291121e-05 8.291121e-05
## 787 788 789 790 791 792
## 8.291121e-05 8.291121e-05 8.291121e-05 8.291121e-05 8.291121e-05 8.291121e-05
## 793 794 795 796 797 798
## 8.291121e-05 8.291121e-05 8.291121e-05 8.291121e-05 8.291121e-05 8.291121e-05
## 799 800 801 802 803 804
## 8.291121e-05 8.291121e-05 8.291121e-05 8.291121e-05 8.291121e-05 8.291121e-05
## 805 806 807 808 809 810
## 8.291121e-05 8.291121e-05 8.291121e-05 8.291121e-05 8.291121e-05 8.291121e-05
## 811 812 813 814 815 816
## 8.291121e-05 8.291121e-05 8.291121e-05 8.291121e-05 8.291121e-05 8.291121e-05
## 817 818 819 820 821 822
## 8.291121e-05 8.291121e-05 8.291121e-05 8.291121e-05 8.291121e-05 8.291121e-05
## 823 824 825 826 827 828
## 8.291121e-05 8.291121e-05 8.291121e-05 8.291121e-05 8.291121e-05 8.291121e-05
## 829 830 831 832 833 834
## 8.291121e-05 8.291121e-05 8.291121e-05 8.291121e-05 8.291121e-05 8.291121e-05
## 835 836 837 838 839 840
## 8.291121e-05 8.291121e-05 8.291121e-05 8.291121e-05 8.291121e-05 8.291121e-05
## 841 842 843 844 845 846
## 8.291121e-05 8.291121e-05 8.291121e-05 8.291121e-05 8.291121e-05 8.291121e-05
## 847 848 849 850 851 852
## 8.291121e-05 8.291121e-05 8.291121e-05 8.291121e-05 8.291121e-05 8.291121e-05
## 853 854 855 856 857 858
## 8.291121e-05 8.291121e-05 8.291121e-05 8.291121e-05 8.291121e-05 8.291121e-05
## 859 860 861 862 863 864
## 8.291121e-05 8.291121e-05 8.291121e-05 8.291121e-05 8.291121e-05 8.291121e-05
## 865 866 867 868 869 870
## 8.291121e-05 8.291121e-05 8.291121e-05 8.291121e-05 8.291121e-05 8.291121e-05
## 871 872 873 874 875 876
## 8.291121e-05 8.291121e-05 8.291121e-05 8.291121e-05 8.291121e-05 8.291121e-05
## 877 878 879 880 881 882
## 8.291121e-05 8.291121e-05 8.291121e-05 8.291121e-05 8.291121e-05 8.291121e-05
## 883 884 885 886 887 888
## 8.291121e-05 8.291121e-05 8.291121e-05 8.291121e-05 8.291121e-05 8.291121e-05
## 889 890 891 892 893 894
## 8.291121e-05 8.291121e-05 8.291121e-05 8.291121e-05 8.291121e-05 8.291121e-05
## 895 896 897 898 899 900
## 8.291121e-05 8.291121e-05 8.291121e-05 8.291121e-05 8.291121e-05 8.291121e-05
## 901 902 903 904 905 906
## 8.291121e-05 8.291121e-05 8.291121e-05 8.291121e-05 8.291121e-05 8.291121e-05
## 907 908 909 910 911 912
## 8.291121e-05 8.291121e-05 8.291121e-05 8.291121e-05 8.291121e-05 8.291121e-05
## 913 914 915 916 917 918
## 8.291121e-05 8.291121e-05 8.291121e-05 1.314413e-03 1.314413e-03 1.314413e-03
## 919 920 921 922 923 924
## 1.314413e-03 1.314413e-03 1.314413e-03 1.314413e-03 1.314413e-03 1.314413e-03
## 925 926 927 928 929 930
## 1.314413e-03 1.314413e-03 1.314413e-03 1.314413e-03 4.310404e-05 4.310404e-05
## 931 932 933 934 935 936
## 4.310404e-05 4.310404e-05 4.310404e-05 4.310404e-05 4.310404e-05 4.310404e-05
## 937 938 939 940 941 942
## 4.310404e-05 4.310404e-05 4.310404e-05 4.310404e-05 4.310404e-05 4.310404e-05
## 943 944 945 946 947 948
## 4.310404e-05 4.310404e-05 4.310404e-05 4.310404e-05 4.310404e-05 4.310404e-05
## 949 950 951 952 953 954
## 4.310404e-05 4.310404e-05 4.310404e-05 4.310404e-05 4.310404e-05 4.310404e-05
## 955 956 957 958 959 960
## 4.310404e-05 4.310404e-05 4.310404e-05 4.310404e-05 4.310404e-05 4.310404e-05
## 961 962 963 964 965 966
## 4.310404e-05 6.407233e-04 6.407233e-04 6.407233e-04 6.407233e-04 6.407233e-04
## 967 968 969 970 971 972
## 6.407233e-04 6.407233e-04 6.407233e-04 6.407233e-04 6.407233e-04 6.407233e-04
## 973 974 975 976 977 978
## 6.407233e-04 6.407233e-04 6.407233e-04 6.407233e-04 6.407233e-04 6.407233e-04
## 979 980 981 982 983 984
## 6.407233e-04 6.407233e-04 6.407233e-04 6.407233e-04 6.407233e-04 6.407233e-04
## 985 986 987 988 989 990
## 2.153789e-05 2.153789e-05 2.153789e-05 2.153789e-05 2.153789e-05 2.153789e-05
## 991 992 993 994 995 996
## 2.153789e-05 2.153789e-05 2.153789e-05 2.153789e-05 2.153789e-05 2.153789e-05
## 997 998 999 1000 1001 1002
## 2.153789e-05 2.153789e-05 2.153789e-05 2.153789e-05 2.153789e-05 2.153789e-05
## 1003 1004 1005 1006 1007 1008
## 2.153789e-05 2.153789e-05 2.153789e-05 2.153789e-05 2.153789e-05 2.153789e-05
## 1009 1010 1011 1012 1013 1014
## 2.153789e-05 2.153789e-05 2.153789e-05 2.153789e-05 2.153789e-05 2.153789e-05
## 1015 1016 1017 1018 1019 1020
## 2.153789e-05 2.153789e-05 2.153789e-05 2.153789e-05 2.153789e-05 2.153789e-05
## 1021 1022 1023 1024 1025 1026
## 2.153789e-05 2.153789e-05 2.153789e-05 2.153789e-05 2.153789e-05 2.153789e-05
## 1027 1028 1029 1030 1031 1032
## 2.153789e-05 2.153789e-05 2.153789e-05 2.153789e-05 2.153789e-05 2.153789e-05
## 1033 1034 1035 1036 1037 1038
## 2.153789e-05 2.153789e-05 2.153789e-05 2.153789e-05 2.153789e-05 2.153789e-05
## 1039 1040 1041 1042 1043 1044
## 2.153789e-05 2.153789e-05 2.153789e-05 2.153789e-05 2.153789e-05 2.153789e-05
## 1045 1046 1047 1048 1049 1050
## 2.153789e-05 2.153789e-05 2.153789e-05 2.153789e-05 2.153789e-05 2.153789e-05
## 1051 1052 1053 1054 1055 1056
## 2.153789e-05 2.153789e-05 2.153789e-05 2.153789e-05 2.153789e-05 2.153789e-05
## 1057 1058 1059 1060 1061 1062
## 2.153789e-05 2.153789e-05 2.153789e-05 2.153789e-05 2.153789e-05 2.153789e-05
## 1063 1064 1065 1066 1067 1068
## 2.153789e-05 2.153789e-05 2.153789e-05 2.153789e-05 2.153789e-05 2.153789e-05
## 1069 1070 1071 1072 1073 1074
## 2.153789e-05 2.153789e-05 2.153789e-05 2.153789e-05 2.153789e-05 2.153789e-05
## 1075 1076 1077 1078 1079 1080
## 2.153789e-05 2.153789e-05 2.153789e-05 2.153789e-05 2.153789e-05 2.153789e-05
## 1081 1082 1083 1084 1085 1086
## 2.153789e-05 2.153789e-05 2.153789e-05 1.039653e-03 1.039653e-03 1.039653e-03
## 1087 1088 1089 1090 1091 1092
## 1.039653e-03 1.039653e-03 1.039653e-03 1.039653e-03 1.039653e-03 3.975711e-05
## 1093 1094 1095 1096 1097 1098
## 3.975711e-05 3.975711e-05 3.975711e-05 3.975711e-05 3.975711e-05 3.975711e-05
## 1099 1100 1101 1102 1103 1104
## 3.975711e-05 3.975711e-05 3.975711e-05 3.975711e-05 3.975711e-05 3.975711e-05
## 1105 1106 1107 1108 1109 1110
## 3.975711e-05 3.975711e-05 3.975711e-05 3.975711e-05 3.975711e-05 3.975711e-05
## 1111 1112 1113 1114 1115 1116
## 3.975711e-05 3.975711e-05 3.975711e-05 3.975711e-05 3.975711e-05 3.975711e-05
## 1117 1118 1119 1120 1121 1122
## 3.975711e-05 3.975711e-05 3.975711e-05 3.975711e-05 3.975711e-05 3.975711e-05
## 1123 1124 1125 1126 1127 1128
## 3.975711e-05 3.975711e-05 3.975711e-05 3.975711e-05 3.975711e-05 3.975711e-05
## 1129 1130 1131 1132 1133 1134
## 3.975711e-05 3.975711e-05 3.975711e-05 3.975711e-05 3.975711e-05 3.975711e-05
## 1135 1136 1137 1138 1139 1140
## 3.975711e-05 3.975711e-05 3.975711e-05 3.975711e-05 3.975711e-05 3.975711e-05
## 1141 1142 1143 1144 1145 1146
## 3.975711e-05 3.975711e-05 3.975711e-05 3.975711e-05 3.975711e-05 1.621098e-03
## 1147 1148 1149 1150 1151 1152
## 1.621098e-03 1.621098e-03 1.621098e-03 1.621098e-03 1.621098e-03 1.621098e-03
## 1153 1154 1155 1156 1157 1158
## 1.621098e-03 1.621098e-03 1.621098e-03 1.621098e-03 1.621098e-03 1.621098e-03
## 1159 1160 1161 1162 1163 1164
## 1.621098e-03 1.621098e-03 1.621098e-03 1.621098e-03 1.621098e-03 1.621098e-03
## 1165 1166 1167 1168 1169 1170
## 1.621098e-03 1.621098e-03 1.621098e-03 1.621098e-03 1.621098e-03 1.621098e-03
## 1171 1172 1173 1174 1175 1176
## 1.621098e-03 1.621098e-03 1.621098e-03 1.621098e-03 1.621098e-03 1.621098e-03
## 1177 1178 1179 1180 1181 1182
## 1.621098e-03 1.621098e-03 1.621098e-03 1.621098e-03 1.621098e-03 1.621098e-03
## 1183 1184 1185 1186 1187 1188
## 1.621098e-03 1.621098e-03 1.621098e-03 1.621098e-03 1.621098e-03 1.621098e-03
## 1189 1190 1191 1192 1193 1194
## 1.621098e-03 1.621098e-03 1.621098e-03 1.621098e-03 1.621098e-03 1.621098e-03
## 1195 1196 1197 1198 1199 1200
## 1.621098e-03 1.621098e-03 1.621098e-03 1.621098e-03 1.621098e-03 1.621098e-03
## 1201 1202 1203 1204 1205 1206
## 1.621098e-03 1.621098e-03 1.621098e-03 1.621098e-03 1.621098e-03 1.621098e-03
## 1207 1208 1209 1210 1211 1212
## 1.621098e-03 1.621098e-03 1.384003e-04 1.384003e-04 1.384003e-04 1.384003e-04
## 1213 1214 1215 1216 1217 1218
## 1.384003e-04 1.384003e-04 1.384003e-04 1.384003e-04 1.384003e-04 1.384003e-04
## 1219 1220 1221 1222 1223 1224
## 1.384003e-04 1.384003e-04 1.384003e-04 1.384003e-04 1.384003e-04 1.384003e-04
## 1225 1226 1227 1228 1229 1230
## 1.384003e-04 1.384003e-04 1.384003e-04 1.384003e-04 1.384003e-04 1.384003e-04
## 1231 1232 1233 1234 1235 1236
## 1.384003e-04 1.384003e-04 1.384003e-04 1.384003e-04 1.384003e-04 1.384003e-04
## 1237 1238 1239 1240 1241 1242
## 1.384003e-04 1.384003e-04 1.384003e-04 1.384003e-04 1.384003e-04 1.384003e-04
## 1243 1244 1245 1246 1247 1248
## 1.384003e-04 1.384003e-04 1.384003e-04 1.384003e-04 1.384003e-04 1.384003e-04
## 1249 1250 1251 1252 1253 1254
## 1.384003e-04 1.384003e-04 1.384003e-04 1.384003e-04 1.384003e-04 1.384003e-04
## 1255 1256 1257 1258 1259 1260
## 1.384003e-04 1.384003e-04 1.384003e-04 1.384003e-04 1.384003e-04 1.384003e-04
## 1261 1262 1263 1264 1265 1266
## 1.384003e-04 1.384003e-04 1.384003e-04 1.384003e-04 1.384003e-04 1.384003e-04
## 1267 1268 1269 1270 1271 1272
## 1.384003e-04 1.384003e-04 1.384003e-04 1.384003e-04 1.384003e-04 1.384003e-04
## 1273 1274 1275 1276 1277 1278
## 1.384003e-04 1.384003e-04 1.384003e-04 1.384003e-04 1.384003e-04 1.384003e-04
## 1279 1280 1281 1282 1283 1284
## 1.384003e-04 1.384003e-04 1.384003e-04 1.384003e-04 1.384003e-04 1.384003e-04
## 1285 1286 1287 1288 1289 1290
## 1.384003e-04 1.384003e-04 1.384003e-04 1.384003e-04 1.384003e-04 1.384003e-04
## 1291 1292 1293 1294 1295 1296
## 1.384003e-04 1.384003e-04 1.384003e-04 1.384003e-04 1.384003e-04 1.384003e-04
## 1297 1298 1299 1300 1301 1302
## 1.384003e-04 1.384003e-04 1.384003e-04 1.384003e-04 1.384003e-04 1.384003e-04
## 1303 1304 1305 1306 1307 1308
## 1.384003e-04 1.384003e-04 1.384003e-04 1.384003e-04 1.384003e-04 1.384003e-04
## 1309 1310 1311 1312 1313 1314
## 1.384003e-04 1.384003e-04 1.384003e-04 1.384003e-04 1.384003e-04 1.384003e-04
## 1315 1316 1317 1318 1319 1320
## 1.384003e-04 1.384003e-04 1.384003e-04 1.384003e-04 1.384003e-04 1.384003e-04
## 1321 1322 1323 1324 1325 1326
## 1.384003e-04 1.384003e-04 1.384003e-04 1.384003e-04 1.384003e-04 1.384003e-04
## 1327 1328 1329 1330 1331 1332
## 1.384003e-04 1.384003e-04 1.384003e-04 1.384003e-04 1.384003e-04 1.384003e-04
## 1333 1334 1335 1336 1337 1338
## 1.384003e-04 1.384003e-04 1.384003e-04 1.384003e-04 1.384003e-04 1.384003e-04
## 1339 1340 1341 1342 1343 1344
## 1.384003e-04 1.384003e-04 1.384003e-04 1.384003e-04 1.384003e-04 1.384003e-04
## 1345 1346 1347 1348 1349 1350
## 1.384003e-04 1.384003e-04 1.384003e-04 1.384003e-04 1.384003e-04 1.384003e-04
## 1351 1352 1353 1354 1355 1356
## 1.384003e-04 1.384003e-04 1.384003e-04 1.384003e-04 1.384003e-04 1.384003e-04
## 1357 1358 1359 1360 1361 1362
## 1.384003e-04 1.384003e-04 1.384003e-04 1.384003e-04 1.384003e-04 1.384003e-04
## 1363 1364 1365 1366 1367 1368
## 1.384003e-04 1.384003e-04 1.384003e-04 1.384003e-04 1.384003e-04 1.384003e-04
## 1369 1370 1371 1372 1373 1374
## 1.384003e-04 1.384003e-04 1.384003e-04 1.384003e-04 1.384003e-04 1.384003e-04
## 1375 1376 1377 1378 1379 1380
## 1.384003e-04 1.384003e-04 1.384003e-04 1.384003e-04 1.384003e-04 1.384003e-04
## 1381 1382 1383 1384 1385 1386
## 1.384003e-04 1.384003e-04 1.384003e-04 1.384003e-04 1.384003e-04 1.384003e-04
## 1387 1388 1389 1390 1391 1392
## 1.384003e-04 1.384003e-04 1.384003e-04 1.384003e-04 1.384003e-04 1.384003e-04
## 1393 1394 1395 1396 1397 1398
## 1.384003e-04 1.384003e-04 1.384003e-04 1.384003e-04 1.384003e-04 1.384003e-04
## 1399 1400 1401 1402 1403 1404
## 1.384003e-04 1.384003e-04 1.384003e-04 1.384003e-04 1.384003e-04 1.384003e-04
## 1405 1406 1407 1408 1409 1410
## 1.384003e-04 1.384003e-04 1.384003e-04 1.384003e-04 1.384003e-04 1.384003e-04
## 1411 1412 1413 1414 1415 1416
## 1.384003e-04 1.384003e-04 1.384003e-04 1.384003e-04 1.384003e-04 1.384003e-04
## 1417 1418 1419 1420 1421 1422
## 1.384003e-04 1.384003e-04 1.384003e-04 1.384003e-04 1.384003e-04 1.384003e-04
## 1423 1424 1425 1426 1427 1428
## 1.384003e-04 1.384003e-04 1.384003e-04 1.384003e-04 1.384003e-04 1.384003e-04
## 1429 1430 1431 1432 1433 1434
## 1.384003e-04 1.384003e-04 1.384003e-04 1.384003e-04 1.384003e-04 1.384003e-04
## 1435 1436 1437 1438 1439 1440
## 1.384003e-04 1.384003e-04 1.384003e-04 1.384003e-04 1.384003e-04 1.384003e-04
## 1441 1442 1443 1444 1445 1446
## 1.384003e-04 1.384003e-04 1.384003e-04 1.384003e-04 1.384003e-04 1.384003e-04
## 1447 1448 1449 1450 1451 1452
## 1.384003e-04 1.384003e-04 1.384003e-04 1.384003e-04 1.384003e-04 1.384003e-04
## 1453 1454 1455 1456 1457 1458
## 1.384003e-04 1.384003e-04 1.384003e-04 1.384003e-04 1.384003e-04 1.384003e-04
## 1459 1460 1461 1462 1463 1464
## 1.384003e-04 1.384003e-04 1.384003e-04 1.384003e-04 1.384003e-04 1.384003e-04
## 1465 1466 1467 1468 1469 1470
## 1.384003e-04 1.384003e-04 1.384003e-04 1.384003e-04 1.384003e-04 1.384003e-04
## 1471 1472 1473 1474 1475 1476
## 1.384003e-04 1.384003e-04 1.384003e-04 7.355081e-04 7.355081e-04 7.355081e-04
## 1477 1478 1479 1480 1481 1482
## 7.355081e-04 7.355081e-04 7.355081e-04 2.114923e-05 2.114923e-05 2.114923e-05
## 1483 1484 1485 1486 1487 1488
## 2.114923e-05 2.114923e-05 2.114923e-05 2.114923e-05 2.114923e-05 2.114923e-05
## 1489 1490 1491 1492 1493 1494
## 2.114923e-05 2.114923e-05 2.114923e-05 2.114923e-05 2.114923e-05 2.114923e-05
## 1495 1496 1497 1498 1499 1500
## 2.114923e-05 2.114923e-05 2.114923e-05 2.114923e-05 2.114923e-05 2.114923e-05
## 1501 1502 1503 1504 1505 1506
## 2.114923e-05 2.114923e-05 2.114923e-05 2.114923e-05 2.114923e-05 2.114923e-05
## 1507 1508 1509 1510 1511 1512
## 2.114923e-05 2.114923e-05 2.114923e-05 2.114923e-05 2.114923e-05 2.114923e-05
## 1513 1514 1515 1516 1517 1518
## 2.114923e-05 2.114923e-05 2.114923e-05 2.114923e-05 2.114923e-05 2.114923e-05
## 1519 1520 1521 1522 1523 1524
## 2.114923e-05 2.114923e-05 2.114923e-05 2.114923e-05 2.114923e-05 2.114923e-05
## 1525 1526 1527 1528 1529 1530
## 2.114923e-05 2.114923e-05 2.114923e-05 2.114923e-05 2.114923e-05 2.114923e-05
## 1531 1532 1533 1534 1535 1536
## 2.114923e-05 2.114923e-05 2.114923e-05 2.114923e-05 2.114923e-05 2.114923e-05
## 1537 1538 1539 1540 1541 1542
## 2.114923e-05 2.114923e-05 2.114923e-05 2.114923e-05 2.114923e-05 2.114923e-05
## 1543 1544 1545 1546 1547 1548
## 2.114923e-05 2.114923e-05 2.114923e-05 2.114923e-05 2.114923e-05 2.114923e-05
## 1549 1550 1551 1552 1553 1554
## 2.114923e-05 2.114923e-05 2.114923e-05 2.114923e-05 2.114923e-05 2.114923e-05
## 1555 1556 1557 1558 1559 1560
## 2.114923e-05 2.114923e-05 4.870886e-04 4.870886e-04 4.870886e-04 4.870886e-04
## 1561 1562 1563 1564 1565 1566
## 4.870886e-04 4.870886e-04 4.870886e-04 4.870886e-04 4.870886e-04 4.870886e-04
## 1567 1568 1569 1570 1571 1572
## 4.870886e-04 4.870886e-04 4.870886e-04 4.870886e-04 4.870886e-04 4.870886e-04
## 1573 1574 1575 1576 1577 1578
## 4.870886e-04 4.870886e-04 4.870886e-04 1.721210e-05 1.721210e-05 1.721210e-05
## 1579 1580 1581 1582 1583 1584
## 1.721210e-05 1.721210e-05 1.721210e-05 1.721210e-05 1.721210e-05 1.721210e-05
## 1585 1586 1587 1588 1589 1590
## 1.721210e-05 1.721210e-05 1.721210e-05 1.721210e-05 1.721210e-05 1.721210e-05
## 1591 1592 1593 1594 1595 1596
## 1.721210e-05 1.721210e-05 1.721210e-05 1.721210e-05 1.721210e-05 1.721210e-05
## 1597 1598 1599 1600 1601 1602
## 1.721210e-05 1.721210e-05 1.721210e-05 1.721210e-05 1.721210e-05 1.721210e-05
## 1603 1604 1605 1606 1607 1608
## 1.721210e-05 1.721210e-05 1.721210e-05 1.721210e-05 1.721210e-05 1.721210e-05
## 1609 1610 1611 1612 1613 1614
## 1.721210e-05 1.721210e-05 1.721210e-05 1.721210e-05 1.721210e-05 1.721210e-05
## 1615 1616 1617 1618 1619 1620
## 1.721210e-05 1.721210e-05 1.721210e-05 1.721210e-05 1.721210e-05 1.721210e-05
## 1621 1622 1623 1624 1625 1626
## 1.721210e-05 1.721210e-05 1.721210e-05 1.721210e-05 1.721210e-05 1.721210e-05
## 1627 1628 1629 1630 1631 1632
## 1.721210e-05 1.721210e-05 1.721210e-05 1.721210e-05 1.721210e-05 1.721210e-05
## 1633 1634 1635 1636 1637 1638
## 1.721210e-05 1.721210e-05 1.721210e-05 1.721210e-05 1.721210e-05 1.721210e-05
## 1639 1640 1641 1642 1643 1644
## 1.721210e-05 1.721210e-05 1.721210e-05 1.721210e-05 1.721210e-05 1.721210e-05
## 1645 1646 1647 1648 1649 1650
## 1.721210e-05 1.721210e-05 1.721210e-05 1.721210e-05 1.721210e-05 1.721210e-05
## 1651 1652 1653 1654 1655 1656
## 1.721210e-05 1.721210e-05 1.721210e-05 1.721210e-05 1.721210e-05 1.721210e-05
## 1657 1658 1659 1660 1661 1662
## 1.721210e-05 1.721210e-05 1.721210e-05 1.721210e-05 1.721210e-05 1.721210e-05
## 1663 1664 1665 1666 1667 1668
## 1.721210e-05 1.721210e-05 1.721210e-05 1.721210e-05 1.721210e-05 1.721210e-05
## 1669 1670 1671 1672 1673 1674
## 1.721210e-05 1.721210e-05 1.721210e-05 1.721210e-05 1.721210e-05 1.721210e-05
## 1675 1676 1677 1678 1679 1680
## 1.721210e-05 1.721210e-05 1.721210e-05 1.721210e-05 1.721210e-05 1.721210e-05
## 1681 1682 1683 1684 1685 1686
## 1.721210e-05 1.721210e-05 1.721210e-05 1.721210e-05 1.721210e-05 1.721210e-05
## 1687 1688 1689 1690 1691 1692
## 1.721210e-05 1.721210e-05 1.721210e-05 1.721210e-05 1.721210e-05 1.721210e-05
## 1693 1694 1695 1696 1697 1698
## 1.721210e-05 1.721210e-05 1.721210e-05 1.721210e-05 8.969465e-04 8.969465e-04
## 1699 1700 1701 1702 1703 1704
## 8.969465e-04 8.969465e-04 8.969465e-04 8.969465e-04 8.969465e-04 8.969465e-04
## 1705 1706 1707 1708 1709 1710
## 8.969465e-04 8.969465e-04 8.969465e-04 8.969465e-04 8.969465e-04 8.969465e-04
## 1711 1712 1713 1714 1715 1716
## 8.969465e-04 8.969465e-04 8.969465e-04 8.969465e-04 8.969465e-04 8.969465e-04
## 1717 1718 1719 1720 1721 1722
## 8.969465e-04 8.969465e-04 8.969465e-04 8.969465e-04 8.969465e-04 8.969465e-04
## 1723 1724 1725 1726 1727 1728
## 8.969465e-04 8.969465e-04 8.969465e-04 8.969465e-04 8.969465e-04 8.969465e-04
## 1729 1730 1731 1732 1733 1734
## 8.969465e-04 8.969465e-04 8.969465e-04 8.969465e-04 8.969465e-04 8.969465e-04
## 1735 1736 1737 1738 1739 1740
## 8.969465e-04 8.969465e-04 8.969465e-04 8.969465e-04 8.969465e-04 8.969465e-04
## 1741 1742 1743 1744 1745 1746
## 8.969465e-04 8.969465e-04 8.969465e-04 8.969465e-04 8.969465e-04 8.969465e-04
## 1747 1748 1749 1750 1751 1752
## 8.969465e-04 1.013239e-04 1.013239e-04 1.013239e-04 1.013239e-04 1.013239e-04
## 1753 1754 1755 1756 1757 1758
## 1.013239e-04 1.013239e-04 1.013239e-04 1.013239e-04 1.013239e-04 1.013239e-04
## 1759 1760 1761 1762 1763 1764
## 1.013239e-04 1.013239e-04 1.013239e-04 1.013239e-04 1.013239e-04 1.013239e-04
## 1765 1766 1767 1768 1769 1770
## 1.013239e-04 1.013239e-04 1.013239e-04 1.013239e-04 1.013239e-04 1.013239e-04
## 1771 1772 1773 1774 1775 1776
## 1.013239e-04 1.013239e-04 1.013239e-04 1.013239e-04 1.013239e-04 1.013239e-04
## 1777 1778 1779 1780 1781 1782
## 1.013239e-04 1.013239e-04 1.013239e-04 1.013239e-04 1.013239e-04 1.013239e-04
## 1783 1784 1785 1786 1787 1788
## 1.013239e-04 1.013239e-04 1.013239e-04 1.013239e-04 1.013239e-04 1.013239e-04
## 1789 1790 1791 1792 1793 1794
## 1.013239e-04 1.013239e-04 1.013239e-04 1.013239e-04 1.013239e-04 1.013239e-04
## 1795 1796 1797 1798 1799 1800
## 1.013239e-04 1.013239e-04 1.013239e-04 1.013239e-04 1.013239e-04 1.013239e-04
## 1801 1802 1803 1804 1805 1806
## 1.013239e-04 1.013239e-04 1.013239e-04 1.013239e-04 1.013239e-04 1.013239e-04
## 1807 1808 1809 1810 1811 1812
## 1.013239e-04 1.013239e-04 1.013239e-04 1.013239e-04 1.013239e-04 1.013239e-04
## 1813 1814 1815 1816 1817 1818
## 1.013239e-04 1.013239e-04 1.013239e-04 1.013239e-04 1.013239e-04 1.013239e-04
## 1819 1820 1821 1822 1823 1824
## 1.013239e-04 1.013239e-04 1.013239e-04 1.013239e-04 1.013239e-04 1.013239e-04
## 1825 1826 1827 1828 1829 1830
## 1.013239e-04 1.013239e-04 1.013239e-04 1.013239e-04 1.013239e-04 1.013239e-04
## 1831 1832 1833 1834 1835 1836
## 1.013239e-04 1.013239e-04 1.013239e-04 1.013239e-04 1.013239e-04 1.013239e-04
## 1837 1838 1839 1840 1841 1842
## 1.013239e-04 1.013239e-04 1.013239e-04 1.013239e-04 1.013239e-04 1.013239e-04
## 1843 1844 1845 1846 1847 1848
## 1.013239e-04 1.013239e-04 1.013239e-04 1.013239e-04 1.013239e-04 1.013239e-04
## 1849 1850 1851 1852 1853 1854
## 1.013239e-04 1.013239e-04 1.013239e-04 1.013239e-04 1.013239e-04 1.013239e-04
## 1855 1856 1857 1858 1859 1860
## 1.013239e-04 1.013239e-04 1.013239e-04 1.013239e-04 1.013239e-04 1.013239e-04
## 1861 1862 1863 1864 1865 1866
## 1.013239e-04 1.013239e-04 1.013239e-04 1.013239e-04 1.013239e-04 1.013239e-04
## 1867 1868 1869 1870 1871 1872
## 1.013239e-04 1.013239e-04 1.013239e-04 1.013239e-04 1.013239e-04 1.013239e-04
## 1873 1874 1875 1876 1877 1878
## 1.013239e-04 1.013239e-04 1.013239e-04 7.074313e-04 7.074313e-04 7.074313e-04
## 1879 1880 1881 1882 1883 1884
## 7.074313e-04 7.074313e-04 7.074313e-04 7.074313e-04 7.074313e-04 7.074313e-04
## 1885 1886 1887 1888 1889 1890
## 7.074313e-04 7.074313e-04 7.074313e-04 7.074313e-04 7.074313e-04 7.074313e-04
## 1891 1892 1893 1894 1895 1896
## 7.074313e-04 3.206654e-05 3.206654e-05 3.206654e-05 3.206654e-05 3.206654e-05
## 1897 1898 1899 1900 1901 1902
## 3.206654e-05 3.206654e-05 3.206654e-05 3.206654e-05 3.206654e-05 3.206654e-05
## 1903 1904 1905 1906 1907 1908
## 3.206654e-05 3.206654e-05 3.206654e-05 3.206654e-05 3.206654e-05 3.206654e-05
## 1909 1910 1911 1912 1913 1914
## 3.206654e-05 3.206654e-05 3.206654e-05 3.206654e-05 3.206654e-05 3.206654e-05
## 1915 1916 1917 1918 1919 1920
## 3.206654e-05 3.206654e-05 3.206654e-05 3.206654e-05 3.206654e-05 3.206654e-05
## 1921 1922 1923 1924 1925 1926
## 3.206654e-05 3.206654e-05 3.206654e-05 3.206654e-05 3.206654e-05 3.206654e-05
## 1927 1928 1929 1930 1931 1932
## 3.206654e-05 3.206654e-05 3.206654e-05 3.206654e-05 3.206654e-05 3.206654e-05
## 1933 1934 1935 1936 1937 1938
## 3.206654e-05 3.206654e-05 3.206654e-05 3.206654e-05 3.206654e-05 3.206654e-05
## 1939 1940 1941 1942 1943 1944
## 3.206654e-05 3.206654e-05 3.206654e-05 3.206654e-05 3.206654e-05 3.206654e-05
## 1945 1946 1947 1948 1949 1950
## 3.206654e-05 3.206654e-05 3.206654e-05 3.206654e-05 3.206654e-05 3.206654e-05
## 1951 1952 1953 1954 1955 1956
## 3.206654e-05 3.206654e-05 3.206654e-05 3.206654e-05 3.206654e-05 3.206654e-05
## 1957 1958 1959 1960 1961 1962
## 3.206654e-05 3.206654e-05 3.206654e-05 3.206654e-05 3.206654e-05 3.206654e-05
## 1963 1964 1965 1966 1967 1968
## 3.206654e-05 3.206654e-05 3.206654e-05 3.206654e-05 3.206654e-05 3.206654e-05
## 1969 1970 1971 1972 1973 1974
## 3.206654e-05 3.206654e-05 3.206654e-05 3.206654e-05 3.206654e-05 3.206654e-05
## 1975 1976 1977 1978 1979 1980
## 3.206654e-05 3.206654e-05 3.206654e-05 3.206654e-05 3.206654e-05 3.206654e-05
## 1981 1982 1983 1984 1985 1986
## 3.206654e-05 3.206654e-05 3.206654e-05 3.206654e-05 3.206654e-05 3.206654e-05
## 1987 1988 1989 1990 1991 1992
## 3.206654e-05 3.206654e-05 3.206654e-05 3.206654e-05 3.206654e-05 3.206654e-05
## 1993 1994 1995 1996 1997 1998
## 3.206654e-05 3.206654e-05 3.206654e-05 3.206654e-05 3.206654e-05 3.206654e-05
## 1999 2000 2001 2002 2003 2004
## 3.206654e-05 3.206654e-05 3.206654e-05 3.206654e-05 3.206654e-05 3.206654e-05
## 2005 2006 2007 2008 2009 2010
## 3.206654e-05 3.206654e-05 3.206654e-05 3.206654e-05 3.206654e-05 3.206654e-05
## 2011 2012 2013 2014 2015 2016
## 3.206654e-05 3.206654e-05 3.206654e-05 3.206654e-05 3.206654e-05 3.206654e-05
## 2017 2018 2019 2020 2021 2022
## 3.206654e-05 3.206654e-05 3.206654e-05 3.206654e-05 3.206654e-05 3.206654e-05
## 2023 2024 2025 2026 2027 2028
## 3.206654e-05 3.206654e-05 3.206654e-05 3.206654e-05 3.206654e-05 3.206654e-05
## 2029 2030 2031 2032 2033 2034
## 3.206654e-05 5.737297e-04 5.737297e-04 5.737297e-04 5.737297e-04 5.737297e-04
## 2035 2036 2037 2038 2039 2040
## 5.737297e-04 5.737297e-04 5.737297e-04 5.737297e-04 5.737297e-04 5.737297e-04
## 2041 2042 2043 2044 2045 2046
## 5.737297e-04 5.737297e-04 5.737297e-04 5.737297e-04 5.737297e-04 5.737297e-04
## 2047 2048 2049 2050 2051 2052
## 5.737297e-04 5.737297e-04 5.737297e-04 5.737297e-04 5.737297e-04 5.737297e-04
## 2053 2054 2055 2056 2057 2058
## 5.737297e-04 5.737297e-04 5.737297e-04 1.686300e-05 1.686300e-05 1.686300e-05
## 2059 2060 2061 2062 2063 2064
## 1.686300e-05 1.686300e-05 1.686300e-05 1.686300e-05 1.686300e-05 1.686300e-05
## 2065 2066 2067 2068 2069 2070
## 1.686300e-05 1.686300e-05 1.686300e-05 1.686300e-05 1.686300e-05 1.686300e-05
## 2071 2072 2073 2074 2075 2076
## 1.686300e-05 1.686300e-05 1.686300e-05 1.686300e-05 1.686300e-05 1.686300e-05
## 2077 2078 2079 2080 2081 2082
## 1.686300e-05 1.686300e-05 1.686300e-05 1.686300e-05 1.686300e-05 1.686300e-05
## 2083 2084 2085 2086 2087 2088
## 1.686300e-05 1.686300e-05 1.686300e-05 1.686300e-05 1.686300e-05 1.686300e-05
## 2089 2090 2091 2092 2093 2094
## 1.686300e-05 1.686300e-05 1.686300e-05 1.686300e-05 1.686300e-05 1.686300e-05
## 2095 2096 2097 2098 2099 2100
## 1.686300e-05 1.686300e-05 1.686300e-05 1.686300e-05 1.686300e-05 1.686300e-05
## 2101 2102 2103 2104 2105 2106
## 1.686300e-05 1.686300e-05 1.686300e-05 1.686300e-05 1.686300e-05 1.686300e-05
## 2107 2108 2109 2110 2111 2112
## 1.686300e-05 1.686300e-05 1.686300e-05 1.686300e-05 1.686300e-05 1.686300e-05
## 2113 2114 2115 2116 2117 2118
## 1.686300e-05 1.686300e-05 1.686300e-05 1.686300e-05 1.686300e-05 1.686300e-05
## 2119 2120 2121 2122 2123 2124
## 1.686300e-05 1.686300e-05 1.686300e-05 1.686300e-05 1.686300e-05 1.686300e-05
## 2125 2126 2127 2128 2129 2130
## 1.686300e-05 1.686300e-05 1.686300e-05 1.686300e-05 1.686300e-05 1.686300e-05
## 2131 2132 2133 2134 2135 2136
## 1.686300e-05 1.686300e-05 1.686300e-05 1.686300e-05 1.686300e-05 1.686300e-05
## 2137 2138 2139 2140 2141 2142
## 1.686300e-05 1.686300e-05 1.686300e-05 1.686300e-05 1.686300e-05 1.686300e-05
## 2143 2144 2145 2146 2147 2148
## 1.686300e-05 1.686300e-05 1.686300e-05 1.686300e-05 1.686300e-05 1.686300e-05
## 2149 2150 2151 2152 2153 2154
## 1.686300e-05 1.686300e-05 1.686300e-05 1.686300e-05 1.686300e-05 1.686300e-05
## 2155 2156 2157 2158 2159 2160
## 1.686300e-05 1.686300e-05 1.686300e-05 1.686300e-05 1.686300e-05 1.686300e-05
## 2161 2162 2163 2164 2165 2166
## 1.686300e-05 1.686300e-05 1.686300e-05 1.686300e-05 1.686300e-05 1.686300e-05
## 2167 2168 2169 2170 2171 2172
## 1.686300e-05 1.686300e-05 1.686300e-05 1.686300e-05 1.686300e-05 1.686300e-05
## 2173 2174 2175 2176 2177 2178
## 1.686300e-05 1.686300e-05 1.686300e-05 1.686300e-05 1.686300e-05 1.686300e-05
## 2179 2180 2181 2182 2183 2184
## 1.686300e-05 1.686300e-05 1.686300e-05 1.686300e-05 8.981920e-04 8.981920e-04
## 2185 2186 2187 2188 2189 2190
## 8.981920e-04 8.981920e-04 8.981920e-04 8.981920e-04 8.981920e-04 8.981920e-04
## 2191 2192 2193 2194 2195 2196
## 8.981920e-04 8.981920e-04 8.981920e-04 8.981920e-04 8.981920e-04 8.981920e-04
## 2197 2198 2199 2200 2201 2202
## 8.981920e-04 8.981920e-04 2.077009e-05 2.077009e-05 2.077009e-05 2.077009e-05
## 2203 2204 2205 2206 2207 2208
## 2.077009e-05 2.077009e-05 2.077009e-05 2.077009e-05 2.077009e-05 2.077009e-05
## 2209 2210 2211 2212 2213 2214
## 2.077009e-05 2.077009e-05 2.077009e-05 2.077009e-05 2.077009e-05 2.077009e-05
## 2215 2216 2217 2218 2219 2220
## 2.077009e-05 2.077009e-05 2.077009e-05 2.077009e-05 2.077009e-05 2.077009e-05
## 2221 2222 2223 2224 2225 2226
## 2.077009e-05 2.077009e-05 2.077009e-05 2.077009e-05 2.077009e-05 2.077009e-05
## 2227 2228 2229 2230 2231 2232
## 2.077009e-05 2.077009e-05 2.077009e-05 2.077009e-05 2.077009e-05 2.077009e-05
## 2233 2234 2235 2236 2237 2238
## 2.077009e-05 2.077009e-05 2.077009e-05 2.077009e-05 2.077009e-05 2.077009e-05
## 2239 2240 2241 2242 2243 2244
## 2.077009e-05 2.077009e-05 2.077009e-05 2.077009e-05 2.077009e-05 2.077009e-05
## 2245 2246 2247 2248 2249 2250
## 2.077009e-05 2.077009e-05 2.077009e-05 2.077009e-05 2.077009e-05 2.077009e-05
## 2251 2252 2253 2254 2255 2256
## 2.077009e-05 2.077009e-05 2.077009e-05 2.077009e-05 2.077009e-05 2.077009e-05
## 2257 2258 2259 2260 2261 2262
## 2.077009e-05 2.077009e-05 2.077009e-05 2.077009e-05 2.077009e-05 2.077009e-05
## 2263 2264 2265 2266 2267 2268
## 2.077009e-05 2.077009e-05 2.077009e-05 2.077009e-05 2.077009e-05 2.077009e-05
## 2269 2270 2271 2272 2273 2274
## 2.077009e-05 2.077009e-05 2.077009e-05 2.077009e-05 2.077009e-05 2.077009e-05
## 2275 2276 2277 2278 2279 2280
## 2.077009e-05 2.077009e-05 2.077009e-05 2.077009e-05 2.077009e-05 2.077009e-05
## 2281 2282 2283 2284 2285 2286
## 2.077009e-05 2.077009e-05 2.077009e-05 2.077009e-05 2.077009e-05 2.077009e-05
## 2287 2288 2289 2290 2291 2292
## 2.077009e-05 2.077009e-05 2.077009e-05 2.077009e-05 2.077009e-05 2.077009e-05
## 2293 2294 2295 2296 2297 2298
## 2.077009e-05 2.077009e-05 2.077009e-05 2.077009e-05 2.077009e-05 2.077009e-05
## 2299 2300 2301 2302 2303 2304
## 2.077009e-05 1.697308e-03 1.697308e-03 3.490541e-05 3.490541e-05 3.490541e-05
## 2305 2306 2307 2308 2309 2310
## 3.490541e-05 3.490541e-05 3.490541e-05 3.490541e-05 3.490541e-05 3.490541e-05
## 2311 2312 2313 2314 2315 2316
## 3.490541e-05 3.490541e-05 3.490541e-05 3.490541e-05 3.490541e-05 3.490541e-05
## 2317 2318 2319 2320 2321 2322
## 3.490541e-05 3.490541e-05 3.490541e-05 3.490541e-05 3.490541e-05 3.490541e-05
## 2323 2324 2325 2326 2327 2328
## 3.490541e-05 3.490541e-05 3.490541e-05 3.490541e-05 3.490541e-05 3.490541e-05
## 2329 2330 2331 2332 2333 2334
## 3.490541e-05 3.490541e-05 3.490541e-05 3.490541e-05 3.490541e-05 3.490541e-05
## 2335 2336 2337 2338 2339 2340
## 3.490541e-05 3.490541e-05 3.490541e-05 3.490541e-05 3.490541e-05 3.490541e-05
## 2341 2342 2343 2344 2345 2346
## 3.490541e-05 3.490541e-05 3.490541e-05 3.490541e-05 3.490541e-05 3.490541e-05
## 2347 2348 2349 2350 2351 2352
## 3.356422e-05 3.356422e-05 3.356422e-05 3.356422e-05 3.356422e-05 3.356422e-05
## 2353 2354 2355 2356 2357 2358
## 3.356422e-05 3.356422e-05 3.356422e-05 3.356422e-05 3.356422e-05 3.356422e-05
## 2359 2360 2361 2362 2363 2364
## 3.356422e-05 3.356422e-05 3.356422e-05 3.356422e-05 3.356422e-05 3.356422e-05
## 2365 2366 2367 2368 2369 2370
## 3.356422e-05 3.356422e-05 3.356422e-05 3.356422e-05 3.356422e-05 3.356422e-05
## 2371 2372 2373 2374 2375 2376
## 3.356422e-05 3.356422e-05 3.356422e-05 3.356422e-05 3.356422e-05 3.356422e-05
## 2377 2378 2379 2380 2381 2382
## 3.356422e-05 3.356422e-05 8.964212e-04 8.964212e-04 8.964212e-04 8.964212e-04
## 2383 2384 2385 2386 2387 2388
## 8.964212e-04 8.964212e-04 8.964212e-04 8.964212e-04 1.548715e-05 1.548715e-05
## 2389 2390 2391 2392 2393 2394
## 1.548715e-05 1.548715e-05 1.548715e-05 1.548715e-05 1.548715e-05 1.548715e-05
## 2395 2396 2397 2398 2399 2400
## 1.548715e-05 1.548715e-05 1.548715e-05 1.548715e-05 1.548715e-05 1.548715e-05
## 2401 2402 2403 2404 2405 2406
## 1.548715e-05 1.548715e-05 1.548715e-05 1.548715e-05 1.548715e-05 1.548715e-05
## 2407 2408 2409 2410 2411 2412
## 1.548715e-05 1.548715e-05 1.548715e-05 1.548715e-05 1.548715e-05 1.548715e-05
## 2413 2414 2415 2416 2417 2418
## 1.548715e-05 1.548715e-05 1.548715e-05 1.548715e-05 1.548715e-05 1.548715e-05
## 2419 2420 2421 2422 2423 2424
## 1.548715e-05 1.548715e-05 1.548715e-05 1.548715e-05 1.548715e-05 1.548715e-05
## 2425 2426 2427 2428 2429 2430
## 1.548715e-05 1.548715e-05 1.548715e-05 1.548715e-05 1.548715e-05 1.548715e-05
## 2431 2432 2433 2434 2435 2436
## 1.548715e-05 1.548715e-05 1.548715e-05 1.548715e-05 1.548715e-05 1.548715e-05
## 2437 2438 2439 2440 2441 2442
## 1.548715e-05 1.548715e-05 1.548715e-05 1.548715e-05 1.548715e-05 1.548715e-05
## 2443 2444 2445 2446 2447 2448
## 1.548715e-05 1.548715e-05 1.548715e-05 1.548715e-05 1.548715e-05 1.548715e-05
## 2449 2450 2451 2452 2453 2454
## 1.548715e-05 1.548715e-05 1.548715e-05 1.548715e-05 9.110192e-04 9.110192e-04
## 2455 2456 2457 2458 2459 2460
## 9.110192e-04 2.101352e-05 2.101352e-05 2.101352e-05 2.101352e-05 2.101352e-05
## 2461 2462 2463 2464 2465 2466
## 2.101352e-05 2.101352e-05 2.101352e-05 2.101352e-05 2.101352e-05 2.101352e-05
## 2467 2468 2469 2470 2471 2472
## 2.101352e-05 2.101352e-05 2.101352e-05 2.101352e-05 2.101352e-05 2.101352e-05
## 2473 2474 2475 2476 2477 2478
## 2.101352e-05 2.101352e-05 2.101352e-05 2.101352e-05 2.101352e-05 2.101352e-05
## 2479 2480 2481 2482 2483 2484
## 2.101352e-05 2.101352e-05 2.101352e-05 2.101352e-05 2.101352e-05 2.101352e-05
## 2485 2486 2487 2488 2489 2490
## 2.101352e-05 2.101352e-05 2.101352e-05 2.101352e-05 2.101352e-05 2.101352e-05
## 2491 2492 2493 2494 2495 2496
## 2.101352e-05 2.101352e-05 2.101352e-05 2.101352e-05 2.101352e-05 2.101352e-05
## 2497 2498 2499 2500 2501 2502
## 2.101352e-05 3.964510e-04 3.964510e-04 3.964510e-04 3.964510e-04 3.964510e-04
## 2503 2504 2505 2506 2507 2508
## 3.964510e-04 3.964510e-04 3.964510e-04 3.964510e-04 3.964510e-04 3.964510e-04
## 2509 2510 2511 2512 2513 2514
## 3.964510e-04 3.964510e-04 3.964510e-04 3.964510e-04 3.964510e-04 1.260825e-05
## 2515 2516 2517 2518 2519 2520
## 1.260825e-05 1.260825e-05 1.260825e-05 1.260825e-05 1.260825e-05 1.260825e-05
## 2521 2522 2523 2524 2525 2526
## 1.260825e-05 1.260825e-05 1.260825e-05 1.260825e-05 1.260825e-05 1.260825e-05
## 2527 2528 2529 2530 2531 2532
## 1.260825e-05 1.260825e-05 1.260825e-05 1.260825e-05 1.260825e-05 1.260825e-05
## 2533 2534 2535 2536 2537 2538
## 1.260825e-05 1.260825e-05 1.260825e-05 1.260825e-05 1.260825e-05 1.260825e-05
## 2539 2540 2541 2542 2543 2544
## 1.260825e-05 1.260825e-05 1.260825e-05 1.260825e-05 1.260825e-05 1.260825e-05
## 2545 2546 2547 2548 2549 2550
## 1.260825e-05 1.260825e-05 1.260825e-05 1.260825e-05 1.260825e-05 1.260825e-05
## 2551 2552 2553 2554 2555 2556
## 1.260825e-05 1.260825e-05 1.260825e-05 1.260825e-05 1.260825e-05 1.260825e-05
## 2557 2558 2559 2560 2561 2562
## 1.260825e-05 1.260825e-05 1.260825e-05 1.260825e-05 1.260825e-05 1.260825e-05
## 2563 2564 2565 2566 2567 2568
## 1.260825e-05 1.260825e-05 1.260825e-05 1.260825e-05 1.260825e-05 1.260825e-05
## 2569 2570 2571 2572 2573 2574
## 1.260825e-05 1.260825e-05 1.260825e-05 1.260825e-05 1.260825e-05 1.260825e-05
## 2575 2576 2577 2578 2579 2580
## 1.260825e-05 1.260825e-05 1.260825e-05 1.260825e-05 1.260825e-05 1.260825e-05
## 2581 2582 2583 2584 2585 2586
## 1.260825e-05 1.260825e-05 1.260825e-05 1.260825e-05 1.260825e-05 1.260825e-05
## 2587 2588 2589 2590 2591 2592
## 1.260825e-05 1.260825e-05 1.260825e-05 1.260825e-05 1.260825e-05 1.260825e-05
## 2593 2594 2595 2596 2597 2598
## 1.260825e-05 1.260825e-05 1.260825e-05 1.260825e-05 1.260825e-05 1.260825e-05
## 2599 2600 2601 2602 2603 2604
## 1.260825e-05 1.260825e-05 1.260825e-05 1.260825e-05 1.260825e-05 1.260825e-05
## 2605 2606 2607 2608 2609 2610
## 1.260825e-05 1.260825e-05 1.260825e-05 1.260825e-05 1.260825e-05 1.260825e-05
## 2611 2612 2613 2614 2615 2616
## 1.260825e-05 1.260825e-05 1.260825e-05 1.260825e-05 1.260825e-05 1.260825e-05
## 2617 2618 2619 2620 2621 2622
## 1.260825e-05 1.260825e-05 1.260825e-05 1.260825e-05 1.260825e-05 1.260825e-05
## 2623 2624 2625 2626 2627 2628
## 1.260825e-05 1.260825e-05 1.260825e-05 1.260825e-05 1.260825e-05 1.260825e-05
## 2629 2630 2631 2632 2633 2634
## 1.260825e-05 1.260825e-05 1.260825e-05 1.260825e-05 1.260825e-05 1.260825e-05
## 2635 2636 2637 2638 2639 2640
## 1.260825e-05 4.806892e-04 4.806892e-04 4.806892e-04 4.806892e-04 4.806892e-04
## 2641 2642 2643 2644 2645 2646
## 4.806892e-04 4.806892e-04 4.806892e-04 4.806892e-04 4.806892e-04 4.806892e-04
## 2647 2648 2649 2650 2651 2652
## 4.806892e-04 4.806892e-04 4.806892e-04 4.806892e-04 4.806892e-04 4.806892e-04
## 2653 2654 2655 2656 2657 2658
## 4.806892e-04 4.806892e-04 4.806892e-04 4.806892e-04 4.806892e-04 4.806892e-04
## 2659 2660 2661 2662 2663 2664
## 4.806892e-04 4.806892e-04 4.806892e-04 4.806892e-04 4.806892e-04 4.806892e-04
## 2665 2666 2667 2668 2669 2670
## 4.806892e-04 4.806892e-04 4.806892e-04 4.806892e-04 4.806892e-04 4.806892e-04
## 2671 2672 2673 2674 2675 2676
## 2.608066e-05 2.608066e-05 2.608066e-05 2.608066e-05 2.608066e-05 2.608066e-05
## 2677 2678 2679 2680 2681 2682
## 2.608066e-05 2.608066e-05 2.608066e-05 2.608066e-05 2.608066e-05 2.608066e-05
## 2683 2684 2685 2686 2687 2688
## 2.608066e-05 2.608066e-05 2.608066e-05 2.608066e-05 2.608066e-05 2.608066e-05
## 2689 2690 2691 2692 2693 2694
## 2.608066e-05 2.608066e-05 2.608066e-05 2.608066e-05 2.608066e-05 2.608066e-05
## 2695 2696 2697 2698 2699 2700
## 2.608066e-05 2.608066e-05 2.608066e-05 2.608066e-05 2.608066e-05 2.608066e-05
## 2701 2702 2703 2704 2705 2706
## 2.608066e-05 2.608066e-05 2.608066e-05 2.608066e-05 2.608066e-05 2.608066e-05
## 2707 2708 2709 2710 2711 2712
## 2.608066e-05 2.608066e-05 2.608066e-05 2.608066e-05 2.608066e-05 2.608066e-05
## 2713 2714 2715 2716 2717 2718
## 2.608066e-05 2.608066e-05 2.608066e-05 2.608066e-05 2.608066e-05 2.608066e-05
## 2719 2720 2721 2722 2723 2724
## 2.608066e-05 2.608066e-05 2.608066e-05 2.608066e-05 2.608066e-05 2.608066e-05
## 2725 2726 2727 2728 2729 2730
## 2.608066e-05 2.608066e-05 2.608066e-05 2.608066e-05 2.608066e-05 2.608066e-05
## 2731 2732 2733 2734 2735 2736
## 2.608066e-05 2.608066e-05 2.608066e-05 2.608066e-05 2.608066e-05 2.608066e-05
## 2737 2738 2739 2740 2741 2742
## 2.608066e-05 2.608066e-05 2.608066e-05 2.608066e-05 2.608066e-05 2.608066e-05
## 2743 2744 2745 2746 2747 2748
## 2.608066e-05 2.608066e-05 2.608066e-05 2.608066e-05 2.608066e-05 2.608066e-05
## 2749 2750 2751 2752 2753 2754
## 2.608066e-05 2.608066e-05 2.608066e-05 2.608066e-05 2.608066e-05 2.608066e-05
## 2755 2756 2757 2758 2759 2760
## 2.608066e-05 2.608066e-05 2.608066e-05 2.608066e-05 2.608066e-05 2.608066e-05
## 2761 2762 2763 2764 2765 2766
## 2.608066e-05 2.608066e-05 2.608066e-05 2.608066e-05 2.608066e-05 2.608066e-05
## 2767 2768 2769 2770 2771 2772
## 2.608066e-05 2.608066e-05 2.608066e-05 2.608066e-05 2.608066e-05 2.608066e-05
## 2773 2774 2775 2776 2777 2778
## 2.608066e-05 2.608066e-05 2.608066e-05 2.608066e-05 2.608066e-05 2.608066e-05
## 2779 2780 2781 2782 2783 2784
## 2.608066e-05 2.608066e-05 2.608066e-05 2.608066e-05 2.608066e-05 2.608066e-05
## 2785 2786 2787 2788 2789 2790
## 2.608066e-05 2.608066e-05 2.608066e-05 2.608066e-05 2.608066e-05 2.608066e-05
## 2791 2792 2793 2794 2795 2796
## 1.045916e-03 1.045916e-03 1.045916e-03 1.045916e-03 1.045916e-03 1.045916e-03
## 2797 2798 2799 2800 2801 2802
## 1.045916e-03 1.045916e-03 1.045916e-03 1.045916e-03 1.045916e-03 1.045916e-03
## 2803 2804 2805 2806 2807 2808
## 1.045916e-03 1.045916e-03 1.045916e-03 1.045916e-03 1.045916e-03 1.045916e-03
## 2809 2810 2811 2812 2813 2814
## 1.045916e-03 1.045916e-03 1.045916e-03 1.045916e-03 1.045916e-03 1.045916e-03
## 2815 2816 2817 2818 2819 2820
## 1.045916e-03 1.045916e-03 1.045916e-03 1.045916e-03 1.045916e-03 1.045916e-03
## 2821 2822 2823 2824 2825 2826
## 1.045916e-03 1.045916e-03 1.045916e-03 1.045916e-03 1.045916e-03 1.045916e-03
## 2827 2828 2829 2830 2831 2832
## 1.045916e-03 3.872971e-05 3.872971e-05 3.872971e-05 3.872971e-05 3.872971e-05
## 2833 2834 2835 2836 2837 2838
## 3.872971e-05 3.872971e-05 3.872971e-05 3.872971e-05 3.872971e-05 3.872971e-05
## 2839 2840 2841 2842 2843 2844
## 3.872971e-05 3.872971e-05 3.872971e-05 3.872971e-05 3.872971e-05 3.872971e-05
## 2845 2846 2847 2848 2849 2850
## 3.872971e-05 3.872971e-05 3.872971e-05 3.872971e-05 3.872971e-05 3.872971e-05
## 2851 2852 2853 2854 2855 2856
## 3.872971e-05 3.872971e-05 3.872971e-05 3.872971e-05 3.872971e-05 3.872971e-05
## 2857 2858 2859 2860 2861 2862
## 3.872971e-05 3.872971e-05 3.872971e-05 3.872971e-05 3.872971e-05 3.872971e-05
## 2863 2864 2865 2866 2867 2868
## 3.872971e-05 3.872971e-05 3.872971e-05 3.872971e-05 3.872971e-05 3.872971e-05
## 2869 2870 2871 2872 2873 2874
## 3.872971e-05 3.872971e-05 3.872971e-05 3.872971e-05 3.872971e-05 3.872971e-05
## 2875 2876 2877 2878 2879 2880
## 3.872971e-05 3.872971e-05 3.872971e-05 3.872971e-05 3.872971e-05 3.872971e-05
## 2881 2882 2883 2884 2885 2886
## 3.872971e-05 3.872971e-05 3.872971e-05 3.872971e-05 3.872971e-05 3.872971e-05
## 2887 2888 2889 2890 2891 2892
## 3.872971e-05 3.872971e-05 3.872971e-05 3.872971e-05 3.872971e-05 3.872971e-05
## 2893 2894 2895 2896 2897 2898
## 3.872971e-05 3.872971e-05 3.872971e-05 3.872971e-05 3.872971e-05 3.872971e-05
## 2899 2900 2901 2902 2903 2904
## 3.872971e-05 3.872971e-05 3.872971e-05 3.872971e-05 3.872971e-05 3.872971e-05
## 2905 2906 2907 2908 2909 2910
## 3.872971e-05 3.872971e-05 3.872971e-05 3.872971e-05 3.872971e-05 3.872971e-05
## 2911 2912 2913 2914 2915 2916
## 3.872971e-05 3.872971e-05 3.872971e-05 3.872971e-05 3.872971e-05 3.872971e-05
## 2917 2918 2919 2920 2921 2922
## 3.872971e-05 3.872971e-05 3.872971e-05 3.872971e-05 3.872971e-05 3.872971e-05
## 2923 2924 2925 2926 2927 2928
## 3.872971e-05 3.872971e-05 3.872971e-05 3.872971e-05 3.872971e-05 3.872971e-05
## 2929 2930 2931 2932 2933 2934
## 3.872971e-05 3.872971e-05 3.872971e-05 3.872971e-05 3.872971e-05 3.872971e-05
## 2935 2936 2937 2938 2939 2940
## 3.872971e-05 3.872971e-05 3.872971e-05 3.872971e-05 3.872971e-05 3.872971e-05
## 2941 2942 2943 2944 2945 2946
## 3.872971e-05 3.872971e-05 3.872971e-05 3.872971e-05 3.872971e-05 3.872971e-05
## 2947 2948 2949 2950 2951 2952
## 3.872971e-05 3.872971e-05 3.872971e-05 3.872971e-05 3.872971e-05 3.872971e-05
## 2953 2954 2955 2956 2957 2958
## 3.872971e-05 3.872971e-05 3.872971e-05 3.872971e-05 3.872971e-05 3.872971e-05
## 2959 2960 2961 2962 2963 2964
## 3.872971e-05 3.872971e-05 3.872971e-05 3.872971e-05 3.872971e-05 3.872971e-05
## 2965 2966 2967 2968 2969 2970
## 3.872971e-05 3.872971e-05 3.872971e-05 3.872971e-05 3.872971e-05 3.872971e-05
## 2971 2972 2973 2974 2975 2976
## 3.872971e-05 3.872971e-05 3.872971e-05 3.872971e-05 3.872971e-05 3.872971e-05
## 2977 2978 2979 2980 2981 2982
## 3.872971e-05 3.872971e-05 3.872971e-05 3.872971e-05 3.872971e-05 3.872971e-05
## 2983 2984 2985 2986 2987 2988
## 3.872971e-05 3.872971e-05 3.872971e-05 3.872971e-05 3.872971e-05 3.872971e-05
## 2989 2990 2991 2992 2993 2994
## 3.872971e-05 3.872971e-05 3.872971e-05 3.872971e-05 3.872971e-05 3.872971e-05
## 2995 2996 2997 2998 2999 3000
## 3.872971e-05 3.872971e-05 3.872971e-05 3.872971e-05 3.872971e-05 3.872971e-05
## 3001 3002 3003 3004 3005 3006
## 3.872971e-05 3.872971e-05 3.872971e-05 3.872971e-05 3.872971e-05 3.872971e-05
## 3007 3008 3009 3010 3011 3012
## 3.872971e-05 3.872971e-05 3.872971e-05 3.872971e-05 3.872971e-05 3.872971e-05
## 3013 3014 3015 3016 3017 3018
## 3.872971e-05 3.872971e-05 3.872971e-05 3.872971e-05 3.872971e-05 3.872971e-05
## 3019 3020 3021 3022 3023 3024
## 3.872971e-05 3.872971e-05 1.159374e-03 1.159374e-03 1.159374e-03 1.159374e-03
## 3025 3026 3027 3028 3029 3030
## 1.159374e-03 1.159374e-03 1.159374e-03 1.159374e-03 1.159374e-03 1.159374e-03
## 3031 3032 3033 3034 3035 3036
## 1.159374e-03 1.159374e-03 1.159374e-03 1.159374e-03 1.159374e-03 1.159374e-03
## 3037 3038 3039 3040 3041 3042
## 1.159374e-03 1.159374e-03 1.159374e-03 1.159374e-03 1.159374e-03 1.159374e-03
## 3043 3044 3045 3046 3047 3048
## 1.159374e-03 1.159374e-03 1.159374e-03 1.159374e-03 1.159374e-03 1.159374e-03
## 3049 3050 3051 3052 3053 3054
## 1.159374e-03 1.159374e-03 1.159374e-03 1.159374e-03 2.259925e-05 2.259925e-05
## 3055 3056 3057 3058 3059 3060
## 2.259925e-05 2.259925e-05 2.259925e-05 2.259925e-05 2.259925e-05 2.259925e-05
## 3061 3062 3063 3064 3065 3066
## 2.259925e-05 2.259925e-05 2.259925e-05 2.259925e-05 2.259925e-05 2.259925e-05
## 3067 3068 3069 3070 3071 3072
## 2.259925e-05 2.259925e-05 2.259925e-05 2.259925e-05 2.259925e-05 2.259925e-05
## 3073 3074 3075 3076 3077 3078
## 2.259925e-05 2.259925e-05 2.259925e-05 2.259925e-05 2.259925e-05 2.259925e-05
## 3079 3080 3081 3082 3083 3084
## 2.259925e-05 2.259925e-05 2.259925e-05 2.259925e-05 2.259925e-05 2.259925e-05
## 3085 3086 3087 3088 3089 3090
## 2.259925e-05 2.259925e-05 2.259925e-05 2.259925e-05 2.259925e-05 2.259925e-05
## 3091 3092 3093 3094 3095 3096
## 2.259925e-05 2.259925e-05 2.259925e-05 2.259925e-05 2.259925e-05 2.259925e-05
## 3097 3098 3099 3100 3101 3102
## 2.259925e-05 2.259925e-05 2.259925e-05 2.259925e-05 2.259925e-05 2.259925e-05
## 3103 3104 3105 3106 3107 3108
## 2.259925e-05 2.259925e-05 2.259925e-05 2.259925e-05 2.259925e-05 2.259925e-05
## 3109 3110 3111 3112 3113 3114
## 2.259925e-05 2.259925e-05 2.259925e-05 2.259925e-05 2.259925e-05 2.259925e-05
## 3115 3116 3117 3118 3119 3120
## 2.259925e-05 2.259925e-05 2.259925e-05 2.259925e-05 2.259925e-05 2.259925e-05
## 3121 3122 3123 3124 3125 3126
## 2.259925e-05 2.259925e-05 2.259925e-05 2.259925e-05 2.259925e-05 2.259925e-05
## 3127 3128 3129 3130 3131 3132
## 2.259925e-05 2.259925e-05 2.259925e-05 2.259925e-05 2.259925e-05 2.259925e-05
## 3133 3134 3135 3136 3137 3138
## 2.259925e-05 2.259925e-05 2.259925e-05 2.259925e-05 2.259925e-05 2.259925e-05
## 3139 3140 3141 3142 3143 3144
## 2.259925e-05 2.259925e-05 2.259925e-05 2.259925e-05 2.259925e-05 2.259925e-05
## 3145 3146 3147 3148 3149 3150
## 2.259925e-05 2.259925e-05 2.259925e-05 2.259925e-05 2.259925e-05 2.259925e-05
## 3151 3152 3153 3154 3155 3156
## 2.259925e-05 2.259925e-05 2.259925e-05 2.259925e-05 2.259925e-05 2.259925e-05
## 3157 3158 3159 3160 3161 3162
## 2.259925e-05 2.259925e-05 2.259925e-05 2.259925e-05 2.259925e-05 2.259925e-05
## 3163 3164 3165 3166 3167 3168
## 2.259925e-05 2.259925e-05 2.259925e-05 2.259925e-05 2.259925e-05 2.259925e-05
## 3169 3170 3171 3172 3173 3174
## 2.259925e-05 2.259925e-05 2.259925e-05 2.259925e-05 2.259925e-05 2.259925e-05
## 3175 3176 3177 3178 3179 3180
## 2.259925e-05 2.259925e-05 2.259925e-05 2.259925e-05 2.259925e-05 2.259925e-05
## 3181 3182 3183 3184 3185 3186
## 2.259925e-05 2.259925e-05 2.259925e-05 2.259925e-05 2.259925e-05 1.102277e-03
## 3187 3188 3189 3190 3191 3192
## 1.102277e-03 1.102277e-03 1.102277e-03 1.102277e-03 1.102277e-03 1.102277e-03
## 3193 3194 3195 3196 3197 3198
## 1.102277e-03 1.102277e-03 1.102277e-03 1.102277e-03 1.102277e-03 1.102277e-03
## 3199 3200 3201 3202 3203 3204
## 1.102277e-03 1.102277e-03 1.102277e-03 1.102277e-03 1.102277e-03 1.102277e-03
## 3205 3206 3207 3208 3209 3210
## 1.102277e-03 1.639544e-05 1.639544e-05 1.639544e-05 1.639544e-05 1.639544e-05
## 3211 3212 3213 3214 3215 3216
## 1.639544e-05 1.639544e-05 1.639544e-05 1.639544e-05 1.639544e-05 1.639544e-05
## 3217 3218 3219 3220 3221 3222
## 1.639544e-05 1.639544e-05 1.639544e-05 1.639544e-05 1.639544e-05 1.639544e-05
## 3223 3224 3225 3226 3227 3228
## 1.639544e-05 1.639544e-05 1.639544e-05 1.639544e-05 1.639544e-05 1.639544e-05
## 3229 3230 3231 3232 3233 3234
## 1.639544e-05 1.639544e-05 1.639544e-05 1.639544e-05 1.639544e-05 1.639544e-05
## 3235 3236 3237 3238 3239 3240
## 1.639544e-05 1.639544e-05 1.639544e-05 1.639544e-05 1.639544e-05 1.639544e-05
## 3241 3242 3243 3244 3245 3246
## 1.639544e-05 1.639544e-05 1.639544e-05 1.639544e-05 1.639544e-05 1.639544e-05
## 3247 3248 3249 3250 3251 3252
## 1.639544e-05 1.639544e-05 1.639544e-05 1.639544e-05 1.639544e-05 1.639544e-05
## 3253 3254 3255 3256 3257 3258
## 1.639544e-05 1.639544e-05 1.639544e-05 1.639544e-05 1.639544e-05 1.639544e-05
## 3259 3260 3261 3262 3263 3264
## 1.639544e-05 1.639544e-05 1.639544e-05 1.639544e-05 1.639544e-05 1.639544e-05
## 3265 3266 3267 3268 3269 3270
## 1.639544e-05 1.639544e-05 1.639544e-05 1.639544e-05 1.639544e-05 1.639544e-05
## 3271 3272 3273 3274 3275 3276
## 1.639544e-05 1.639544e-05 1.639544e-05 1.639544e-05 1.639544e-05 1.639544e-05
## 3277 3278 3279 3280 3281 3282
## 1.639544e-05 1.639544e-05 1.639544e-05 1.639544e-05 1.639544e-05 1.639544e-05
## 3283 3284 3285 3286 3287 3288
## 1.639544e-05 1.639544e-05 1.639544e-05 1.639544e-05 1.639544e-05 1.639544e-05
## 3289 3290 3291 3292 3293 3294
## 1.639544e-05 1.639544e-05 1.639544e-05 1.639544e-05 1.639544e-05 1.639544e-05
## 3295 3296 3297 3298 3299 3300
## 1.639544e-05 1.639544e-05 1.639544e-05 1.639544e-05 1.639544e-05 1.077350e-03
## 3301 3302 3303 3304 3305 3306
## 1.077350e-03 1.077350e-03 1.077350e-03 1.077350e-03 1.077350e-03 1.077350e-03
## 3307 3308 3309 3310 3311 3312
## 1.524107e-05 1.524107e-05 1.524107e-05 1.524107e-05 1.524107e-05 1.524107e-05
## 3313 3314 3315 3316 3317 3318
## 1.524107e-05 1.524107e-05 1.524107e-05 1.524107e-05 1.524107e-05 1.524107e-05
## 3319 3320 3321 3322 3323 3324
## 1.524107e-05 1.524107e-05 1.524107e-05 1.524107e-05 1.524107e-05 1.524107e-05
## 3325 3326 3327 3328 3329 3330
## 1.524107e-05 1.524107e-05 1.524107e-05 1.524107e-05 1.524107e-05 1.524107e-05
## 3331 3332 3333 3334 3335 3336
## 1.524107e-05 1.524107e-05 1.524107e-05 1.524107e-05 1.524107e-05 1.524107e-05
## 3337 3338 3339 3340 3341 3342
## 1.524107e-05 1.524107e-05 1.524107e-05 1.524107e-05 1.524107e-05 1.524107e-05
## 3343 3344 3345 3346 3347 3348
## 1.524107e-05 1.524107e-05 1.524107e-05 1.524107e-05 1.524107e-05 1.524107e-05
## 3349 3350 3351 3352 3353 3354
## 1.524107e-05 1.524107e-05 1.524107e-05 1.524107e-05 1.524107e-05 1.524107e-05
## 3355 3356 3357 3358 3359 3360
## 1.524107e-05 1.524107e-05 1.524107e-05 1.524107e-05 1.524107e-05 1.524107e-05
## 3361 3362 3363 3364 3365 3366
## 1.524107e-05 1.524107e-05 1.524107e-05 1.524107e-05 1.524107e-05 1.524107e-05
## 3367 3368 3369 3370 3371 3372
## 1.524107e-05 1.524107e-05 1.524107e-05 1.524107e-05 1.524107e-05 1.524107e-05
## 3373 3374 3375 3376 3377 3378
## 1.524107e-05 1.524107e-05 1.524107e-05 1.524107e-05 1.524107e-05 1.524107e-05
## 3379 3380 3381 3382 3383 3384
## 1.524107e-05 1.524107e-05 1.524107e-05 1.524107e-05 1.524107e-05 1.524107e-05
## 3385 3386 3387 3388 3389 3390
## 1.524107e-05 1.524107e-05 1.524107e-05 1.524107e-05 1.524107e-05 1.524107e-05
## 3391 3392 3393 3394 3395 3396
## 1.524107e-05 1.524107e-05 1.524107e-05 1.524107e-05 1.524107e-05 1.524107e-05
## 3397 3398 3399 3400 3401 3402
## 1.524107e-05 2.409341e-03 2.409341e-03 2.409341e-03 2.409341e-03 2.409341e-03
## 3403 3404 3405 3406 3407 3408
## 2.409341e-03 2.409341e-03 2.409341e-03 2.409341e-03 2.409341e-03 2.409341e-03
## 3409 3410 3411 3412 3413 3414
## 2.409341e-03 2.409341e-03 6.005158e-05 6.005158e-05 6.005158e-05 6.005158e-05
## 3415 3416 3417 3418 3419 3420
## 6.005158e-05 6.005158e-05 6.005158e-05 6.005158e-05 6.005158e-05 6.005158e-05
## 3421 3422 3423 3424 3425 3426
## 6.005158e-05 6.005158e-05 6.005158e-05 6.005158e-05 6.005158e-05 6.005158e-05
## 3427 3428 3429 3430 3431 3432
## 6.005158e-05 6.005158e-05 6.005158e-05 6.005158e-05 6.005158e-05 6.005158e-05
## 3433 3434 3435 3436 3437 3438
## 6.005158e-05 6.005158e-05 6.005158e-05 6.005158e-05 6.005158e-05 6.005158e-05
## 3439 3440 3441 3442 3443 3444
## 6.005158e-05 6.005158e-05 6.005158e-05 6.005158e-05 6.005158e-05 6.005158e-05
## 3445 3446 3447 3448 3449 3450
## 6.005158e-05 6.005158e-05 6.005158e-05 6.005158e-05 6.005158e-05 6.005158e-05
## 3451 3452 3453 3454 3455 3456
## 6.005158e-05 6.005158e-05 6.005158e-05 6.005158e-05 6.005158e-05 6.005158e-05
## 3457 3458 3459 3460 3461 3462
## 6.005158e-05 6.005158e-05 6.005158e-05 6.005158e-05 6.005158e-05 6.005158e-05
## 3463 3464 3465 3466 3467 3468
## 6.005158e-05 6.005158e-05 6.005158e-05 6.005158e-05 6.005158e-05 6.005158e-05
## 3469 3470 3471 3472 3473 3474
## 6.005158e-05 6.005158e-05 6.005158e-05 6.005158e-05 6.005158e-05 6.005158e-05
## 3475 3476 3477 3478 3479 3480
## 6.005158e-05 6.005158e-05 6.005158e-05 6.005158e-05 6.005158e-05 6.005158e-05
## 3481 3482 3483 3484 3485 3486
## 6.005158e-05 6.005158e-05 6.005158e-05 6.005158e-05 3.363874e-03 3.363874e-03
## 3487 3488 3489 3490 3491 3492
## 3.363874e-03 3.363874e-03 3.363874e-03 3.363874e-03 3.363874e-03 3.363874e-03
## 3493 3494 3495 3496 3497 3498
## 9.428234e-05 9.428234e-05 9.428234e-05 9.428234e-05 9.428234e-05 9.428234e-05
## 3499 3500 3501 3502 3503 3504
## 9.428234e-05 9.428234e-05 9.428234e-05 9.428234e-05 9.428234e-05 9.428234e-05
## 3505 3506 3507 3508 3509 3510
## 9.428234e-05 9.428234e-05 9.428234e-05 9.428234e-05 9.428234e-05 9.428234e-05
## 3511 3512 3513 3514 3515 3516
## 9.428234e-05 9.428234e-05 9.428234e-05 9.428234e-05 9.428234e-05 9.428234e-05
## 3517 3518 3519 3520 3521 3522
## 9.428234e-05 9.428234e-05 9.428234e-05 9.428234e-05 9.428234e-05 9.428234e-05
## 3523 3524 3525 3526 3527 3528
## 9.428234e-05 9.428234e-05 9.428234e-05 9.428234e-05 9.428234e-05 9.428234e-05
## 3529 3530 3531 3532 3533 3534
## 9.428234e-05 9.428234e-05 9.428234e-05 9.428234e-05 9.428234e-05 9.428234e-05
## 3535 3536 3537 3538 3539 3540
## 9.428234e-05 9.428234e-05 9.428234e-05 9.428234e-05 9.428234e-05 9.428234e-05
## 3541 3542 3543 3544 3545 3546
## 9.428234e-05 9.428234e-05 9.428234e-05 9.428234e-05 9.428234e-05 9.428234e-05
## 3547 3548 3549 3550 3551 3552
## 9.428234e-05 9.428234e-05 9.428234e-05 9.428234e-05 9.428234e-05 9.428234e-05
## 3553 3554 3555 3556 3557 3558
## 9.428234e-05 9.428234e-05 9.428234e-05 9.428234e-05 9.428234e-05 9.428234e-05
## 3559 3560 3561 3562 3563 3564
## 9.428234e-05 9.428234e-05 9.428234e-05 9.428234e-05 9.428234e-05 9.428234e-05
## 3565 3566 3567 3568 3569 3570
## 9.428234e-05 9.428234e-05 2.517676e-03 2.517676e-03 2.517676e-03 2.517676e-03
## 3571 3572 3573 3574 3575 3576
## 2.517676e-03 3.009438e-05 3.009438e-05 3.009438e-05 3.009438e-05 3.009438e-05
## 3577 3578 3579 3580 3581 3582
## 3.009438e-05 3.009438e-05 3.009438e-05 3.009438e-05 3.009438e-05 3.009438e-05
## 3583 3584 3585 3586 3587 3588
## 3.009438e-05 3.009438e-05 3.009438e-05 3.009438e-05 3.009438e-05 3.009438e-05
## 3589 3590 3591 3592 3593 3594
## 3.009438e-05 3.009438e-05 3.009438e-05 3.009438e-05 3.009438e-05 3.009438e-05
## 3595 3596 3597 3598 3599 3600
## 3.009438e-05 3.009438e-05 3.009438e-05 3.009438e-05 3.009438e-05 3.009438e-05
## 3601 3602 3603 3604 3605 3606
## 3.009438e-05 3.009438e-05 3.009438e-05 3.009438e-05 3.009438e-05 3.009438e-05
## 3607 3608 3609 3610 3611 3612
## 3.009438e-05 3.009438e-05 3.009438e-05 3.009438e-05 3.009438e-05 3.009438e-05
## 3613 3614 3615 3616 3617 3618
## 6.573824e-04 6.573824e-04 6.573824e-04 6.573824e-04 6.573824e-04 6.573824e-04
## 3619 3620 3621 3622 3623 3624
## 6.573824e-04 6.573824e-04 6.573824e-04 6.573824e-04 6.573824e-04 6.573824e-04
## 3625 3626 3627 3628 3629 3630
## 6.573824e-04 6.573824e-04 6.573824e-04 6.573824e-04 6.573824e-04 6.573824e-04
## 3631 3632 3633 3634 3635 3636
## 6.573824e-04 6.573824e-04 6.573824e-04 6.573824e-04 6.573824e-04 6.573824e-04
## 3637 3638 3639 3640 3641 3642
## 6.573824e-04 6.573824e-04 6.573824e-04 6.573824e-04 1.834469e-05 1.834469e-05
## 3643 3644 3645 3646 3647 3648
## 1.834469e-05 1.834469e-05 1.834469e-05 1.834469e-05 1.834469e-05 1.834469e-05
## 3649 3650 3651 3652 3653 3654
## 1.834469e-05 1.834469e-05 1.834469e-05 1.834469e-05 1.834469e-05 1.834469e-05
## 3655 3656 3657 3658 3659 3660
## 1.834469e-05 1.834469e-05 1.834469e-05 1.834469e-05 1.834469e-05 1.834469e-05
## 3661 3662 3663 3664 3665 3666
## 1.834469e-05 1.834469e-05 1.834469e-05 1.834469e-05 1.834469e-05 1.834469e-05
## 3667 3668 3669 3670 3671 3672
## 1.834469e-05 1.834469e-05 1.834469e-05 1.834469e-05 1.834469e-05 1.834469e-05
## 3673 3674 3675 3676 3677 3678
## 1.834469e-05 1.834469e-05 1.834469e-05 1.834469e-05 1.834469e-05 1.834469e-05
## 3679 3680 3681 3682 3683 3684
## 1.834469e-05 1.834469e-05 1.834469e-05 1.834469e-05 1.834469e-05 1.834469e-05
## 3685 3686 3687 3688 3689 3690
## 1.834469e-05 1.834469e-05 1.834469e-05 1.834469e-05 1.834469e-05 1.834469e-05
## 3691 3692 3693 3694 3695 3696
## 1.834469e-05 1.834469e-05 1.834469e-05 1.834469e-05 1.834469e-05 1.834469e-05
## 3697 3698 3699 3700 3701 3702
## 1.834469e-05 1.834469e-05 1.834469e-05 1.834469e-05 1.834469e-05 1.834469e-05
## 3703 3704 3705 3706 3707 3708
## 1.834469e-05 1.834469e-05 1.834469e-05 1.834469e-05 1.834469e-05 1.834469e-05
## 3709 3710 3711 3712 3713 3714
## 1.834469e-05 1.834469e-05 1.834469e-05 1.834469e-05 1.834469e-05 1.834469e-05
## 3715 3716 3717 3718 3719 3720
## 1.834469e-05 1.834469e-05 1.834469e-05 1.834469e-05 1.834469e-05 1.834469e-05
## 3721 3722 3723 3724 3725 3726
## 1.834469e-05 1.834469e-05 1.834469e-05 1.834469e-05 1.834469e-05 1.834469e-05
## 3727 3728 3729 3730 3731 3732
## 1.834469e-05 1.834469e-05 1.834469e-05 1.834469e-05 1.834469e-05 1.834469e-05
## 3733 3734 3735 3736 3737 3738
## 1.834469e-05 1.834469e-05 1.834469e-05 1.834469e-05 1.834469e-05 1.834469e-05
## 3739 3740 3741 3742 3743 3744
## 1.834469e-05 1.834469e-05 1.834469e-05 1.834469e-05 1.834469e-05 1.834469e-05
## 3745 3746 3747 3748 3749 3750
## 1.406138e-03 2.292371e-05 2.292371e-05 2.292371e-05 2.292371e-05 2.292371e-05
## 3751 3752 3753 3754 3755 3756
## 2.292371e-05 2.292371e-05 2.292371e-05 2.292371e-05 2.292371e-05 2.292371e-05
## 3757 3758 3759 3760 3761 3762
## 2.292371e-05 2.292371e-05 2.292371e-05 2.292371e-05 2.292371e-05 2.292371e-05
## 3763 3764 3765 3766 3767 3768
## 2.292371e-05 2.292371e-05 2.292371e-05 2.292371e-05 2.292371e-05 2.292371e-05
## 3769 3770 3771 3772 3773 3774
## 2.292371e-05 2.292371e-05 2.292371e-05 2.292371e-05 2.292371e-05 2.292371e-05
## 3775 3776 3777 3778 3779 3780
## 2.292371e-05 2.292371e-05 2.292371e-05 2.292371e-05 2.292371e-05 2.292371e-05
## 3781 3782 3783 3784 3785 3786
## 2.292371e-05 2.292371e-05 2.292371e-05 2.292371e-05 2.292371e-05 2.292371e-05
## 3787 3788 3789 3790 3791 3792
## 2.292371e-05 2.292371e-05 2.292371e-05 2.292371e-05 2.316694e-03 2.316694e-03
## 3793 3794 3795 3796 3797 3798
## 3.645888e-05 3.645888e-05 3.645888e-05 3.645888e-05 3.645888e-05 3.645888e-05
## 3799 3800 3801 3802 3803 3804
## 3.645888e-05 3.645888e-05 3.645888e-05 3.645888e-05 3.645888e-05 3.645888e-05
## 3805 3806 3807 3808 3809 3810
## 3.645888e-05 3.645888e-05 3.645888e-05 3.645888e-05 3.645888e-05 3.645888e-05
## 3811 3812 3813 3814 3815 3816
## 3.645888e-05 3.645888e-05 3.645888e-05 3.645888e-05 3.645888e-05 3.645888e-05
## 3817 3818 3819 3820 3821 3822
## 3.645888e-05 3.645888e-05 3.645888e-05 3.645888e-05 3.645888e-05 3.645888e-05
## 3823 3824 3825 3826 3827 3828
## 3.645888e-05 3.645888e-05 3.645888e-05 3.645888e-05 3.645888e-05 3.645888e-05
## 3829 3830 3831 3832 3833 3834
## 3.645888e-05 3.645888e-05 3.645888e-05 3.645888e-05 3.645888e-05 3.645888e-05
## 3835 3836 3837 3838 3839 3840
## 3.645888e-05 3.645888e-05 3.645888e-05 3.645888e-05 3.645888e-05 3.645888e-05
## 3841 3842 3843 3844 3845 3846
## 3.645888e-05 3.645888e-05 3.645888e-05 3.645888e-05 3.645888e-05 3.645888e-05
## 3847 3848 3849 3850 3851 3852
## 3.645888e-05 3.645888e-05 3.645888e-05 1.300807e-03 1.300807e-03 1.300807e-03
## 3853 3854 3855 3856 3857 3858
## 1.300807e-03 1.300807e-03 1.300807e-03 1.300807e-03 1.300807e-03 1.300807e-03
## 3859 3860 3861 3862 3863 3864
## 1.300807e-03 1.300807e-03 1.300807e-03 1.300807e-03 1.300807e-03 1.300807e-03
## 3865 3866 3867 3868 3869 3870
## 1.300807e-03 1.300807e-03 3.063623e-05 3.063623e-05 3.063623e-05 3.063623e-05
## 3871 3872 3873 3874 3875 3876
## 3.063623e-05 3.063623e-05 3.063623e-05 3.063623e-05 3.063623e-05 3.063623e-05
## 3877 3878 3879 3880 3881 3882
## 3.063623e-05 3.063623e-05 3.063623e-05 3.063623e-05 3.063623e-05 3.063623e-05
## 3883 3884 3885 3886 3887 3888
## 3.063623e-05 3.063623e-05 3.063623e-05 3.063623e-05 3.063623e-05 3.063623e-05
## 3889 3890 3891 3892 3893 3894
## 3.063623e-05 3.063623e-05 3.063623e-05 3.063623e-05 3.063623e-05 3.063623e-05
## 3895 3896 3897 3898 3899 3900
## 3.063623e-05 3.063623e-05 3.063623e-05 3.063623e-05 3.063623e-05 3.063623e-05
## 3901 3902 3903 3904 3905 3906
## 3.063623e-05 3.063623e-05 3.063623e-05 3.063623e-05 3.063623e-05 3.063623e-05
## 3907 3908 3909 3910 3911 3912
## 3.063623e-05 3.063623e-05 3.063623e-05 3.063623e-05 3.063623e-05 3.063623e-05
## 3913 3914 3915 3916 3917 3918
## 3.063623e-05 3.063623e-05 3.063623e-05 3.063623e-05 3.063623e-05 3.063623e-05
## 3919 3920 3921 3922 3923 3924
## 3.063623e-05 3.063623e-05 3.063623e-05 3.063623e-05 3.063623e-05 3.063623e-05
## 3925 3926 3927 3928 3929 3930
## 3.063623e-05 3.063623e-05 3.063623e-05 3.063623e-05 3.063623e-05 3.063623e-05
## 3931 3932 3933 3934 3935 3936
## 3.063623e-05 3.063623e-05 3.063623e-05 1.365161e-03 1.365161e-03 1.365161e-03
## 3937 3938 3939 3940 3941 3942
## 1.365161e-03 1.966385e-05 1.966385e-05 1.966385e-05 1.966385e-05 1.966385e-05
## 3943 3944 3945 3946 3947 3948
## 1.966385e-05 1.966385e-05 1.966385e-05 1.966385e-05 1.966385e-05 1.966385e-05
## 3949 3950 3951 3952 3953 3954
## 1.966385e-05 1.966385e-05 1.966385e-05 1.966385e-05 1.966385e-05 1.966385e-05
## 3955 3956 3957 3958 3959 3960
## 1.966385e-05 1.966385e-05 1.966385e-05 1.966385e-05 1.966385e-05 1.966385e-05
## 3961 3962 3963 3964 3965 3966
## 1.966385e-05 1.966385e-05 1.966385e-05 1.966385e-05 1.966385e-05 1.966385e-05
## 3967 3968 3969 3970 3971 3972
## 1.966385e-05 1.966385e-05 1.966385e-05 1.966385e-05 1.966385e-05 1.966385e-05
## 3973 3974 3975 3976 3977 3978
## 1.966385e-05 1.966385e-05 1.966385e-05 1.966385e-05 1.966385e-05 1.966385e-05
## 3979 3980 3981 3982 3983 3984
## 1.966385e-05 1.966385e-05 1.966385e-05 1.840032e-03 2.701603e-05 2.701603e-05
## 3985 3986 3987 3988 3989 3990
## 2.701603e-05 2.701603e-05 2.701603e-05 2.701603e-05 2.701603e-05 2.701603e-05
## 3991 3992 3993 3994 3995 3996
## 2.701603e-05 2.701603e-05 2.701603e-05 2.701603e-05 2.701603e-05 2.701603e-05
## 3997 3998 3999 4000 4001 4002
## 2.701603e-05 2.701603e-05 2.701603e-05 2.701603e-05 2.701603e-05 2.701603e-05
## 4003 4004 4005 4006 4007 4008
## 2.701603e-05 2.701603e-05 2.701603e-05 2.701603e-05 2.701603e-05 2.701603e-05
## 4009 4010 4011 4012 4013 4014
## 2.701603e-05 2.701603e-05 2.701603e-05 2.701603e-05 2.701603e-05 2.701603e-05
## 4015 4016 4017 4018 4019 4020
## 2.701603e-05 2.701603e-05 2.701603e-05 2.701603e-05 2.701603e-05 2.701603e-05
## 4021 4022 4023 4024 4025 4026
## 2.701603e-05 2.701603e-05 2.701603e-05 2.701603e-05 2.701603e-05 2.701603e-05
## 4027 4028 4029 4030 4031 4032
## 2.701603e-05 2.701603e-05 2.701603e-05 2.701603e-05 2.701603e-05 2.701603e-05
## 4033 4034 4035 4036 4037 4038
## 2.701603e-05 2.701603e-05 2.701603e-05 2.701603e-05 2.701603e-05 2.701603e-05
## 4039 4040 4041 4042 4043 4044
## 2.701603e-05 2.701603e-05 2.701603e-05 2.701603e-05 2.701603e-05 2.701603e-05
## 4045 4046 4047 4048 4049
## 2.701603e-05 2.701603e-05 2.701603e-05 2.701603e-05 2.701603e-05which(cooks.distance(mod15) == max(cooks.distance(mod15))) # These could be leveraging points, but it seems like a weak case## 3485 3486 3487 3488 3489 3490 3491 3492
## 3485 3486 3487 3488 3489 3490 3491 3492mod15.1 = glm(survive ~ TotalObs + Temp + specCond + poly(CuStart, 2), family = "binomial", tbl_jarData_long[-c(3567, 3568, 3569, 3570, 3571),]) # try model without these points, AND try with a poly... did separately, neither works
hist(residuals(mod15.1)) # it doesn't seem to fix the bimodality
# Can we figure out why there is bimodality? What happens when we remove CuStart?
mod15 = glm(survive ~ TotalObs + Temp + specCond + CuStart, family = "binomial", tbl_jarData_long) # Return to the original model
lows = which(residuals(mod15) < 0) # Break it apart into low and not-low residuals
tmp4 = tbl_jarData_long[lows,]
tmp5 = tbl_jarData_long[-lows,]
summary(tmp4) # compare when it's just low and not-low## jar_id CuStart CuEnd pH
## Min. : 1.00 Min. : 2.50 Min. : 2.50 Min. :8.510
## 1st Qu.:10.00 1st Qu.: 14.64 1st Qu.: 22.67 1st Qu.:8.585
## Median :16.00 Median : 51.06 Median : 67.06 Median :8.600
## Mean :17.27 Mean : 72.71 Mean : 73.19 Mean :8.598
## 3rd Qu.:25.00 3rd Qu.:124.10 3rd Qu.:114.61 3rd Qu.:8.620
## Max. :36.00 Max. :190.68 Max. :163.14 Max. :8.645
## Temp DO specCond cuGroup
## Min. :25.55 Min. :7.330 Min. :445.5 Length:3357
## 1st Qu.:26.10 1st Qu.:7.410 1st Qu.:448.5 Class :character
## Median :26.45 Median :7.465 Median :450.5 Mode :character
## Mean :26.55 Mean :7.464 Mean :452.1
## 3rd Qu.:27.05 3rd Qu.:7.520 3rd Qu.:453.5
## Max. :27.65 Max. :7.575 Max. :471.5
## TotalObs survive
## Min. : 32.0 Min. :0
## 1st Qu.: 87.0 1st Qu.:0
## Median :132.0 Median :0
## Mean :145.1 Mean :0
## 3rd Qu.:179.0 3rd Qu.:0
## Max. :328.0 Max. :0summary(tmp5)## jar_id CuStart CuEnd pH
## Min. : 1.00 Min. : 2.50 Min. : 2.50 Min. :8.510
## 1st Qu.: 7.00 1st Qu.: 9.11 1st Qu.: 22.67 1st Qu.:8.590
## Median :14.00 Median : 51.06 Median : 67.06 Median :8.600
## Mean :14.68 Mean : 71.32 Mean : 72.63 Mean :8.599
## 3rd Qu.:24.00 3rd Qu.:124.10 3rd Qu.:114.61 3rd Qu.:8.620
## Max. :36.00 Max. :190.68 Max. :163.14 Max. :8.645
## Temp DO specCond cuGroup
## Min. :25.55 Min. :7.330 Min. :445.5 Length:692
## 1st Qu.:26.05 1st Qu.:7.420 1st Qu.:449.0 Class :character
## Median :26.20 Median :7.480 Median :450.5 Mode :character
## Mean :26.40 Mean :7.468 Mean :452.9
## 3rd Qu.:27.00 3rd Qu.:7.520 3rd Qu.:454.0
## Max. :27.65 Max. :7.575 Max. :471.5
## TotalObs survive
## Min. : 45.0 Min. :1
## 1st Qu.:108.0 1st Qu.:1
## Median :153.0 Median :1
## Mean :159.8 Mean :1
## 3rd Qu.:179.0 3rd Qu.:1
## Max. :328.0 Max. :1summary(mod15) # recall the original##
## Call:
## glm(formula = survive ~ TotalObs + Temp + specCond + CuStart,
## family = "binomial", data = tbl_jarData_long)
##
## Deviance Residuals:
## Min 1Q Median 3Q Max
## -0.9002 -0.6213 -0.5597 -0.4797 2.1527
##
## Coefficients:
## Estimate Std. Error z value Pr(>|z|)
## (Intercept) -6.5989492 4.7465340 -1.390 0.164448
## TotalObs 0.0023005 0.0006053 3.800 0.000145 ***
## Temp -0.3978054 0.0839989 -4.736 2.18e-06 ***
## specCond 0.0338187 0.0081391 4.155 3.25e-05 ***
## CuStart -0.0013770 0.0006895 -1.997 0.045810 *
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## (Dispersion parameter for binomial family taken to be 1)
##
## Null deviance: 3703.4 on 4048 degrees of freedom
## Residual deviance: 3632.2 on 4044 degrees of freedom
## AIC: 3642.2
##
## Number of Fisher Scoring iterations: 4mod15.1 = glm(survive ~ TotalObs + Temp + specCond, family = "binomial", tbl_jarData_long)
AIC(mod15) # now determine how AIC compares## [1] 3642.187AIC(mod15.1)## [1] 3644.2AIC(mod15.1)-AIC(mod15) # It's better to include CuStart## [1] 2.012637# Are the data actually two groups, or is this just the data, and a challenge of binary data?
blah = numeric(0)
for(i in 1:1000) {
tmpmod = glm(sample(tbl_jarData_long$survive) ~ TotalObs + Temp + specCond +CuStart, family = "binomial", tbl_jarData_long)
blah = c(blah, coefficients(tmpmod)[5])
}
hist(blah) # And these meet the central limit theorem, so it comes down to being a challenge of modelling binary data
# Let's reconsider removing specCond+Temp and TotalObs+survival - do they make sense?
cor(tbl_jarData_long$Temp, tbl_jarData_long$specCond)## [1] -0.03733425plot(tbl_jarData_long$Temp, tbl_jarData_long$specCond) # Even though these are mechanically related, they are not correlated in our data; KEEP!
plot(tbl_jarData_long$TotalObs, tbl_jarData_long$survive)
abline(lm(survive~TotalObs, data=tbl_jarData_long))
# Include because: accounts for "noise" (we tried to put the same number of veligers in each jar, but inevitably there was variation) and bias (dead veligers were easier to spot than live, and we had multiple observers)# Now... is there pseudoreplication by tank?
mod15.2 = glm(survive ~ scale(TotalObs) + scale(Temp) + scale(specCond) + scale(CuStart), family = "binomial", tbl_jarData_long) # Return to the original model, but this time scale everything so we can compare it to the next model
summary(mod15.2)##
## Call:
## glm(formula = survive ~ scale(TotalObs) + scale(Temp) + scale(specCond) +
## scale(CuStart), family = "binomial", data = tbl_jarData_long)
##
## Deviance Residuals:
## Min 1Q Median 3Q Max
## -0.9002 -0.6213 -0.5597 -0.4797 2.1527
##
## Coefficients:
## Estimate Std. Error z value Pr(>|z|)
## (Intercept) -1.61829 0.04314 -37.514 < 2e-16 ***
## scale(TotalObs) 0.17265 0.04543 3.800 0.000145 ***
## scale(Temp) -0.21913 0.04627 -4.736 2.18e-06 ***
## scale(specCond) 0.17035 0.04100 4.155 3.25e-05 ***
## scale(CuStart) -0.08382 0.04197 -1.997 0.045810 *
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## (Dispersion parameter for binomial family taken to be 1)
##
## Null deviance: 3703.4 on 4048 degrees of freedom
## Residual deviance: 3632.2 on 4044 degrees of freedom
## AIC: 3642.2
##
## Number of Fisher Scoring iterations: 4mod16 = glmer(survive ~ scale(TotalObs) + scale(Temp) + scale(specCond) + scale(CuStart) + (1|jar_id), family = "binomial", tbl_jarData_long) # include jar ID as a random effect
summary(mod16)## Generalized linear mixed model fit by maximum likelihood (Laplace
## Approximation) [glmerMod]
## Family: binomial ( logit )
## Formula:
## survive ~ scale(TotalObs) + scale(Temp) + scale(specCond) + scale(CuStart) +
## (1 | jar_id)
## Data: tbl_jarData_long
##
## AIC BIC logLik deviance df.resid
## 3625.1 3663.0 -1806.6 3613.1 4043
##
## Scaled residuals:
## Min 1Q Median 3Q Max
## -0.6465 -0.5032 -0.4033 -0.3027 4.0136
##
## Random effects:
## Groups Name Variance Std.Dev.
## jar_id (Intercept) 0.1275 0.357
## Number of obs: 4049, groups: jar_id, 36
##
## Fixed effects:
## Estimate Std. Error z value Pr(>|z|)
## (Intercept) -1.65811 0.08196 -20.231 < 2e-16 ***
## scale(TotalObs) 0.28546 0.09808 2.910 0.00361 **
## scale(Temp) -0.25496 0.08011 -3.183 0.00146 **
## scale(specCond) 0.16362 0.07534 2.172 0.02987 *
## scale(CuStart) -0.01788 0.08240 -0.217 0.82825
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Correlation of Fixed Effects:
## (Intr) sc(TO) scl(T) scl(C)
## scl(TtlObs) 0.213
## scale(Temp) 0.065 0.247
## scl(spcCnd) -0.025 0.193 0.102
## scal(CStrt) -0.019 0.069 0.040 -0.166AIC(mod16) # the random effect model is a better fit## [1] 3625.118AIC(mod15.2)## [1] 3642.187hist(residuals(mod16)) # and the residual distribution hasn't changed
tbl_jarData_long %>%
group_by(cuGroup) %>%
summarize(mean(CuStart), mean(survive)) # content to discuss in the manuscript discussion!## # A tibble: 7 × 3
## cuGroup `mean(CuStart)` `mean(survive)`
## <chr> <dbl> <dbl>
## 1 C1 14.8 0.148
## 2 C2 47.5 0.134
## 3 C3 52.5 0.167
## 4 C4 106. 0.191
## 5 C5 135. 0.161
## 6 C6 164. 0.159
## 7 Control 2.5 0.208mod15##
## Call: glm(formula = survive ~ TotalObs + Temp + specCond + CuStart,
## family = "binomial", data = tbl_jarData_long)
##
## Coefficients:
## (Intercept) TotalObs Temp specCond CuStart
## -6.598949 0.002300 -0.397805 0.033819 -0.001377
##
## Degrees of Freedom: 4048 Total (i.e. Null); 4044 Residual
## Null Deviance: 3703
## Residual Deviance: 3632 AIC: 36422.6.7 Figure
intcpt = -6.599
velnum = 0.002
speccond = 0.034
temp = -0.398
copconc = -0.001
velnum_avg = mean(tbl_jarData$TotalObs)
speccond_avg = mean(tbl_jarData$specCond)
temp_avg = mean(tbl_jarData$Temp)
jarFakeCus = seq(from = 0, to = 191, length = 1000) # Create many copper concentration values
vel_y = boot::inv.logit(intcpt + velnum*velnum_avg + speccond*speccond_avg + temp*temp_avg + copconc*jarFakeCus) # Calculate species response at those copper values
Figure6df = data.frame(jarFakeCus, vel_y) # bundle it all as a dataframe
tbl_jarSurvival$propAlive <- tbl_jarSurvival$percentAlive/100 # add a column that converts to proportion alive
(plot_velSurvival = ggplot() +
geom_line(data = Figure6df, aes(x = jarFakeCus, y = vel_y), color = "black", size = 1) +
geom_point(data = tbl_jarSurvival, aes(x=CuStart, y=propAlive), color="black", size = 3) +
theme_classic() +
theme(axis.text = element_text(size = 14, color = "black"),
axis.title = element_text(size = 16, color = "black", face = "bold"),
axis.line = element_line(size = 1.5, color = "black"),
axis.ticks = element_line(size = 1.2, color = "black"),
axis.ticks.length = unit(0.25, "cm")) +
xlim(0,195) +
xlab('\nCopper concentration (µg Cu/L)') +
ylab("Survival\n"))# Final figure 
ggsave(filename="Figure6.jpeg", plot=plot_velSurvival, width=4.5, height=3, dpi=500, units="in")2.8 Copper concentration (part 2)
# Compare variability of different groups to determine if appropriate for ANOVA
leveneTest(tbl_jarData$CuStart~tbl_jarData$cuGroup)## Warning in leveneTest.default(y = y, group = group, ...): group coerced to
## factor.## Levene's Test for Homogeneity of Variance (center = median)
## Df F value Pr(>F)
## group 6 1.7582 0.1431
## 29# Visualize variability differences
boxplot(tbl_jarData$CuStart~tbl_jarData$cuGroup)
# Consider outliers (all together to get big picture)
hist(tbl_jarData$CuStart)
# Compare concentrations among treatment groups
(aov_cu <- oneway.test(CuStart~cuGroup, data=tbl_jarData, var.equal = TRUE)) # Temperatures do not vary between the jar categories##
## One-way analysis of means
##
## data: CuStart and cuGroup
## F = 50.569, num df = 6, denom df = 29, p-value = 4.712e-14#What are the concentrations by group?
group_by(tbl_jarData, cuGroup) %>%
summarise(
mean = mean(CuStart, na.rm=T),
sd = sd(CuStart, na.rm=T)
)## # A tibble: 7 × 3
## cuGroup mean sd
## <chr> <dbl> <dbl>
## 1 C1 16.0 4.67
## 2 C2 47.7 5.99
## 3 C3 60.1 26.2
## 4 C4 104. 11.8
## 5 C5 137. 28.6
## 6 C6 163. 32.7
## 7 Control 2.5 0group_by(tbl_jarData, cuGroup) %>%
summarise(
mean = mean(CuEnd, na.rm=T),
sd = sd(CuEnd, na.rm=T)
)## # A tibble: 7 × 3
## cuGroup mean sd
## <chr> <dbl> <dbl>
## 1 C1 19.1 2.26
## 2 C2 46.6 5.50
## 3 C3 68.7 11.2
## 4 C4 91.6 6.54
## 5 C5 125. 10.6
## 6 C6 150. 12.5
## 7 Control 15.8 25.22.9 Conclude veliger analysis
## Close and remove channels
close(cnxn_vel)
rm(cnxn_vel)Session info
sessionInfo()## R version 4.2.2 (2022-10-31 ucrt)
## Platform: x86_64-w64-mingw32/x64 (64-bit)
## Running under: Windows 10 x64 (build 19045)
##
## Matrix products: default
##
## locale:
## [1] LC_COLLATE=English_United States.utf8
## [2] LC_CTYPE=English_United States.utf8
## [3] LC_MONETARY=English_United States.utf8
## [4] LC_NUMERIC=C
## [5] LC_TIME=English_United States.utf8
##
## attached base packages:
## [1] stats graphics grDevices utils datasets methods base
##
## other attached packages:
## [1] rstatix_0.7.2 viridis_0.6.2 viridisLite_0.4.1 broom_1.0.3
## [5] RColorBrewer_1.1-3 nlme_3.1-160 lme4_1.1-31 Matrix_1.5-1
## [9] FSA_0.9.4 car_3.1-1 carData_3.0-5 RODBC_1.3-20
## [13] ggpubr_0.6.0 kableExtra_1.3.4 data.table_1.14.8 forcats_1.0.0
## [17] stringr_1.5.0 dplyr_1.1.0 purrr_1.0.1 readr_2.1.4
## [21] tidyr_1.3.0 tibble_3.1.8 ggplot2_3.4.1 tidyverse_1.3.2
## [25] knitr_1.42
##
## loaded via a namespace (and not attached):
## [1] fs_1.6.1 lubridate_1.9.2 webshot_0.5.4
## [4] httr_1.4.4 ggsci_2.9 tools_4.2.2
## [7] backports_1.4.1 bslib_0.4.2 utf8_1.2.3
## [10] R6_2.5.1 mgcv_1.8-41 DBI_1.1.3
## [13] colorspace_2.1-0 withr_2.5.0 tidyselect_1.2.0
## [16] gridExtra_2.3 compiler_4.2.2 cli_3.6.0
## [19] rvest_1.0.3 xml2_1.3.3 labeling_0.4.2
## [22] sass_0.4.5 scales_1.2.1 systemfonts_1.0.4
## [25] digest_0.6.31 minqa_1.2.5 rmarkdown_2.20
## [28] svglite_2.1.1 pkgconfig_2.0.3 htmltools_0.5.4
## [31] dbplyr_2.3.0 fastmap_1.1.0 highr_0.10
## [34] rlang_1.0.6 readxl_1.4.2 rstudioapi_0.14
## [37] farver_2.1.1 jquerylib_0.1.4 generics_0.1.3
## [40] jsonlite_1.8.4 googlesheets4_1.0.1 magrittr_2.0.3
## [43] Rcpp_1.0.10 munsell_0.5.0 fansi_1.0.4
## [46] abind_1.4-5 lifecycle_1.0.3 stringi_1.7.12
## [49] yaml_2.3.7 MASS_7.3-58.1 grid_4.2.2
## [52] crayon_1.5.2 lattice_0.20-45 haven_2.5.1
## [55] splines_4.2.2 hms_1.1.2 pillar_1.8.1
## [58] boot_1.3-28 ggsignif_0.6.4 reprex_2.0.2
## [61] glue_1.6.2 evaluate_0.20 modelr_0.1.10
## [64] vctrs_0.5.2 nloptr_2.0.3 tzdb_0.3.0
## [67] cellranger_1.1.0 gtable_0.3.1 assertthat_0.2.1
## [70] cachem_1.0.6 xfun_0.37 googledrive_2.0.0
## [73] gargle_1.3.0 timechange_0.2.0 ellipsis_0.3.2