Also, analysis for the numbers reported in the paper (e.g., number of studies and entries/study)
Load Libraries
library(ezknitr)
library(knitr)
library(metafor)
## Loading required package: Matrix
## Loading 'metafor' package (version 1.9-9). For an overview
## and introduction to the package please type: help(metafor).
library(devtools)
library(doBy)
Clear environment and set seed
remove(list=ls())
set.seed(281)
raw.data <- read.csv("data/workspace.csv")
load(file="data/output_data/data_cleaned.R")
rust.data <- as.data.frame(rust.data.ROM)
yield.data <- as.data.frame(yield.data.ROM)
seedwt.data <- as.data.frame(seedwt.data.ROM)
load(file="data/output_data/results_rust.R")
load(file="data/output_data/results_yield.R")
load(file="data/output_data/results_seedwt.R")
Remove categories that shouldn't have been analyzed (not enough records)
drop.cols <- "medium"
drop.cols.seedwt <- c("medium","low")
results.rust <- results.rust[,! colnames(results.rust) %in% drop.cols]
results.yield <- results.yield[,! colnames(results.yield) %in% drop.cols]
results.seedwt <- results.seedwt[,! colnames(results.seedwt) %in% drop.cols.seedwt]
#############################################################################
Number of studies and entries of raw data
print(c("Studies in raw data", length(unique(raw.data$Reference))))
## [1] "Studies in raw data" "61"
print(c("Entries in raw data", nrow(raw.data)))
## [1] "Entries in raw data" "569"
Number of studies/category and number of entries/category
Number of studies overall
print(c("Studies in rust dataset: ",length(unique(rust.data$ReferenceNumb))))
## [1] "Studies in rust dataset: " "33"
print(c("Studies in yield dataset: ",length(unique(yield.data$ReferenceNumb))))
## [1] "Studies in yield dataset: " "31"
print(c("Studies in seedwt dataset: ",length(unique(seedwt.data$ReferenceNumb))))
## [1] "Studies in seedwt dataset: " "13"
Number of entries overall
print(c("Entries in rust dataset: ",nrow(rust.data)))
## [1] "Entries in rust dataset: " "320"
print(c("Entries in yield dataset: ",nrow(yield.data)))
## [1] "Entries in yield dataset: " "288"
print(c("Entries in seedwt dataset: ",nrow(seedwt.data)))
## [1] "Entries in seedwt dataset: " "144"
Number of entries and studies by year and location
summaryBy(FID~studyYear+Reference, data=rust.data, FUN=length)
## studyYear Reference FID.length
## 1 2005 lawrence et al ref 4 6
## 2 2005 Lawrence ref 21 6
## 3 2005 Sikora ref 42 7
## 4 2006 Harmon ref 47 12
## 5 2006 Kemerait ref 46 5
## 6 2006 Lawrence et al ref 55 10
## 7 2006 Lawrence ref 18 3
## 8 2006 Lawrence ref 41 11
## 9 2006 Mueller ref 36 23
## 10 2006 Mueller ref 37 15
## 11 2006 Mueller ref 38 6
## 12 2006 Mueller ref 39 11
## 13 2006 Mueller ref 40 4
## 14 2006 Mueller ref 43 15
## 15 2006 Mueller ref 45 17
## 16 2006 Sikora et al ref 74 12
## 17 2007 Lawrence et al ref 3 7
## 18 2007 Lawrence et al ref 53 6
## 19 2007 Lawrence et al ref 62 7
## 20 2007 Mueller et al ref 1 3
## 21 2007 Mueller et al ref 9 9
## 22 2007 Padgett et al ref 54 9
## 23 2007 Sikora ref 23 12
## 24 2008 Douglas et al ref 65 18
## 25 2008 Douglas et al ref 66 13
## 26 2008 Lawrence ref 16 13
## 27 2008 Lawrence ref 22 9
## 28 2012 Price et al ref 35 12
## 29 2013 Delaney ref 30 9
## 30 2013 Delaney ref 31 9
## 31 2013 Delaney ref 32 4
## 32 2013 Delaney ref 34 12
## 33 2013 Lawrence et al ref 68 5
table(rust.data$studyYear)
##
## 2005 2006 2007 2008 2012 2013
## 19 144 53 53 12 39
summaryBy(FID~State+Reference, data=rust.data, FUN=length)
## State Reference FID.length
## 1 AL Delaney ref 30 9
## 2 AL Delaney ref 31 9
## 3 AL Delaney ref 32 4
## 4 AL Delaney ref 34 12
## 5 AL Lawrence et al ref 3 7
## 6 AL lawrence et al ref 4 6
## 7 AL Lawrence et al ref 53 6
## 8 AL Lawrence et al ref 55 10
## 9 AL Lawrence et al ref 62 7
## 10 AL Lawrence et al ref 68 5
## 11 AL Lawrence ref 16 13
## 12 AL Lawrence ref 18 3
## 13 AL Lawrence ref 21 6
## 14 AL Lawrence ref 22 9
## 15 AL Sikora et al ref 74 12
## 16 AL Sikora ref 23 12
## 17 AL Sikora ref 42 7
## 18 AL Lawrence ref 41 11
## 19 FL Douglas et al ref 65 18
## 20 FL Douglas et al ref 66 13
## 21 FL Harmon ref 47 12
## 22 FL Mueller et al ref 1 3
## 23 FL Mueller et al ref 9 9
## 24 FL Mueller ref 36 23
## 25 FL Mueller ref 37 15
## 26 FL Mueller ref 38 6
## 27 FL Mueller ref 39 11
## 28 FL Mueller ref 40 4
## 29 FL Mueller ref 45 17
## 30 GA Kemerait ref 46 5
## 31 GA Mueller ref 43 15
## 32 LA Padgett et al ref 54 9
## 33 LA Price et al ref 35 12
table(rust.data$State)
##
## AL AL FL GA LA MS
## 137 11 131 20 21 0
Mean and std of dependent vars
print(c("mean, SD of rust severity in treated plants",
mean(rust.data$m1i), sd(rust.data$m1i)))
## [1] "mean, SD of rust severity in treated plants"
## [2] "24.4085020833333"
## [3] "28.9950610143505"
print(c("mean, SD of rust severity in control plants",
mean(rust.data$m2i), sd(rust.data$m2i)))
## [1] "mean, SD of rust severity in control plants"
## [2] "60.1640625"
## [3] "33.4041850999308"
print(c("mean, SD of rust severity response ratio in control plants",
mean(exp(rust.data$yi)), sd(exp(rust.data$yi))))
## [1] "mean, SD of rust severity response ratio in control plants"
## [2] "0.377096200042875"
## [3] "0.334528920445451"
print(c("mean, SD of yield in treated plants",
mean(yield.data$m1i), sd(yield.data$m1i)))
## [1] "mean, SD of yield in treated plants"
## [2] "3731.41201736111"
## [3] "1166.65952730187"
print(c("mean, SD of yield in control plants",
mean(yield.data$m2i), sd(yield.data$m2i)))
## [1] "mean, SD of yield in control plants"
## [2] "3064.02441840278"
## [3] "1000.89112658311"
print(c("mean, SD of yield response ratio in control plants",
mean(exp(yield.data$yi)), sd(exp(yield.data$yi))))
## [1] "mean, SD of yield response ratio in control plants"
## [2] "1.25188232429643"
## [3] "0.249848371042816"
print(c("mean, SD of seedwt in treated plants",
mean(seedwt.data$m1i), sd(seedwt.data$m1i)))
## [1] "mean, SD of seedwt in treated plants"
## [2] "11.9063722569444"
## [3] "6.22630903932661"
print(c("mean, SD of seedwt in control plants",
mean(seedwt.data$m2i), sd(seedwt.data$m2i)))
## [1] "mean, SD of seedwt in control plants"
## [2] "10.4764800347222"
## [3] "5.62464013581265"
print(c("mean, SD of seedwt response ratio in control plants",
mean(exp(seedwt.data$yi)), sd(exp(seedwt.data$yi))))
## [1] "mean, SD of seedwt response ratio in control plants"
## [2] "1.18847764847298"
## [3] "0.157947343409103"
#############################################################################
summaryBy(FID~ReferenceNumb+Reference, data=raw.data, FUN=length)
## ReferenceNumb Reference FID.length
## 1 1 Mueller et al ref 1 3
## 2 3 Lawrence et al ref 3 7
## 3 4 lawrence et al ref 4 6
## 4 6 Delaney et al ref 6 8
## 5 7 Sikora et al ref 7 4
## 6 8 Delaney et al ref 8 12
## 7 9 Mueller et al ref 9 9
## 8 11 Allen ref 11 7
## 9 12 Allen ref 12 8
## 10 13 Allen ref 13 5
## 11 14 Allen ref 14 5
## 12 16 Lawrence ref 16 13
## 13 17 Delaney ref 17 9
## 14 18 Lawrence ref 18 3
## 15 21 Lawrence ref 21 6
## 16 22 Lawrence ref 22 9
## 17 23 Sikora ref 23 12
## 18 24 Delaney ref 24 8
## 19 25 O'Brien ref 25 7
## 20 26 Delaney ref 26 4
## 21 27 Delaney ref 27 2
## 22 28 Delanely ref 28 2
## 23 29 Delaney ref 29 2
## 24 30 Delaney ref 30 9
## 25 31 Delaney ref 31 9
## 26 32 Delaney ref 32 4
## 27 33 Delaney ref 33 4
## 28 34 Delaney ref 34 12
## 29 35 Price et al ref 35 13
## 30 36 Mueller ref 36 23
## 31 37 Mueller ref 37 15
## 32 38 Mueller ref 38 6
## 33 39 Mueller ref 39 11
## 34 40 Mueller ref 40 4
## 35 41 Lawrence ref 41 11
## 36 42 Sikora ref 42 7
## 37 43 Mueller ref 43 15
## 38 44 Delaney ref 44 13
## 39 45 Mueller ref 45 17
## 40 46 Kemerait ref 46 5
## 41 47 Harmon ref 47 12
## 42 49 Kemerait ref 49 12
## 43 50 Kemerait ref 50 10
## 44 51 Kemerait ref 51 7
## 45 53 Lawrence et al ref 53 6
## 46 54 Padgett et al ref 54 9
## 47 55 Lawrence et al ref 55 10
## 48 59 Kemerait et al ref 59 15
## 49 60 Schneider et al ref 60 23
## 50 62 Lawrence et al ref 62 7
## 51 63 Mueller et al ref 63 6
## 52 65 Douglas et al ref 65 18
## 53 66 Douglas et al ref 66 13
## 54 67 Douglas et al ref 67 11
## 55 68 Lawrence et al ref 68 5
## 56 69 O'Brien et al ref 69 13
## 57 70 Sikora et al ref 70 13
## 58 71 Kemerait et al ref 71 8
## 59 72 Padgett et al ref 72 15
## 60 73 Padgett et al ref 73 15
## 61 74 Sikora et al ref 74 12
# For Rust analysis
summaryBy(FID~ReferenceNumb+Reference, data=rust.data, FUN=length)
## ReferenceNumb Reference FID.length
## 1 1 Mueller et al ref 1 3
## 2 3 Lawrence et al ref 3 7
## 3 4 lawrence et al ref 4 6
## 4 9 Mueller et al ref 9 9
## 5 16 Lawrence ref 16 13
## 6 18 Lawrence ref 18 3
## 7 21 Lawrence ref 21 6
## 8 22 Lawrence ref 22 9
## 9 23 Sikora ref 23 12
## 10 30 Delaney ref 30 9
## 11 31 Delaney ref 31 9
## 12 32 Delaney ref 32 4
## 13 34 Delaney ref 34 12
## 14 35 Price et al ref 35 12
## 15 36 Mueller ref 36 23
## 16 37 Mueller ref 37 15
## 17 38 Mueller ref 38 6
## 18 39 Mueller ref 39 11
## 19 40 Mueller ref 40 4
## 20 41 Lawrence ref 41 11
## 21 42 Sikora ref 42 7
## 22 43 Mueller ref 43 15
## 23 45 Mueller ref 45 17
## 24 46 Kemerait ref 46 5
## 25 47 Harmon ref 47 12
## 26 53 Lawrence et al ref 53 6
## 27 54 Padgett et al ref 54 9
## 28 55 Lawrence et al ref 55 10
## 29 62 Lawrence et al ref 62 7
## 30 65 Douglas et al ref 65 18
## 31 66 Douglas et al ref 66 13
## 32 68 Lawrence et al ref 68 5
## 33 74 Sikora et al ref 74 12
#############################################################################
Overall mean, 95% CI and Tau2
print(c("Mean e.s. of rust severity", mean(exp(results.rust$OVERALL))))
## [1] "Mean e.s. of rust severity" "0.0867322664501827"
print(c("SD e.s. of rust severity", sd(exp(results.rust$OVERALL))))
## [1] "SD e.s. of rust severity" "0.0478620915892458"
print(c("CI of mean e.s. of rust severity",
quantile(exp(results.rust$OVERALL), probs = c(0.025,0.975))))
## 2.5%
## "CI of mean e.s. of rust severity" "0.0241790593032055"
## 97.5%
## "0.207018518105641"
print(c("Mean tau^2 for rust", mean(results.rust$tau2)))
## [1] "Mean tau^2 for rust" "14.5693271830589"
print(c("Mean e.s. of yield", mean(exp(results.yield$OVERALL))))
## [1] "Mean e.s. of yield" "1.27652556332507"
print(c("SD es of yield", sd(exp(results.yield$OVERALL))))
## [1] "SD es of yield" "0.0368943911886343"
print(c("CI of mean e.s. of yield",
quantile(exp(results.yield$OVERALL), probs = c(0.025,0.975))))
## 2.5%
## "CI of mean e.s. of yield" "1.20894190057049"
## 97.5%
## "1.35256618307631"
print(c("Mean tau^2 for yield", mean(results.yield$tau2)))
## [1] "Mean tau^2 for yield" "0.0166130238687126"
print(c("Mean e.s. of seedwt severity", mean(exp(results.seedwt$OVERALL))))
## [1] "Mean e.s. of seedwt severity" "1.21159967140728"
print(c("SD es of yield", sd(exp(results.seedwt$OVERALL))))
## [1] "SD es of yield" "0.0301736021468863"
print(c("CI of mean e.s. of seedwt severity",
quantile(exp(results.seedwt$OVERALL), probs = c(0.025,0.975))))
## 2.5%
## "CI of mean e.s. of seedwt severity" "1.15830431403257"
## 97.5%
## "1.27935002811906"
print(c("Mean tau^2 for seedwt", mean(results.seedwt$tau2)))
## [1] "Mean tau^2 for seedwt" "0.00168248886301628"
#############################################################################
summary.means <- as.data.frame(matrix(NA, nrow = 64, ncol = 6))
colnames(summary.means) <- c("Category", "Moderator" , "Mean", "LL", "UL", "SD")
Transform model results
transform.rust <- exp(results.rust)
colnames(transform.rust) <- colnames(results.rust)
transform.rust$tau2 <- results.rust$tau2
transform.yield <- exp(results.yield)
colnames(transform.yield) <- colnames(results.yield)
transform.yield$tau2 <- results.yield$tau2
transform.seedwt <- exp(results.seedwt)
colnames(transform.seedwt) <- colnames(results.seedwt)
transform.seedwt$tau2 <- results.seedwt$tau2
Rust means
summary.means$Moderator[1:25] <- colnames(results.rust)
summary.means$Category[1:25] <- "Rust"
summary.means$Mean[1:25] <- apply(X = transform.rust, MARGIN = 2,
FUN = mean, na.rm=T)
summary.means$LL[1:25] <- apply(X = transform.rust, MARGIN = 2,
FUN = function(x){quantile(x, probs = c(0.025), na.rm=T)})
summary.means$UL[1:25] <- apply(X = transform.rust, MARGIN = 2,
FUN = function(x){quantile(x, probs = c(0.975), na.rm=T)})
summary.means$SD[1:25] <- apply(X = transform.rust, MARGIN = 2,
FUN = sd, na.rm=T)
Yield means
summary.means$Moderator[26:49] <- colnames(results.yield)
summary.means$Category[26:49] <- "Yield"
summary.means$Mean[26:49] <- apply(X = transform.yield, MARGIN = 2, FUN = mean, na.rm=T)
summary.means$LL[26:49] <- apply(X = transform.yield, MARGIN = 2,
FUN = function(x){quantile(x, probs = c(0.025), na.rm=T)})
summary.means$UL[26:49] <- apply(X = transform.yield, MARGIN = 2,
FUN = function(x){quantile(x, probs = c(0.975), na.rm=T)})
summary.means$SD[26:49] <- apply(X = transform.yield, MARGIN = 2,
FUN = sd, na.rm=T)
Seed weight means
summary.means$Moderator[50:64] <- colnames(results.seedwt)
summary.means$Category[50:64] <- "Seed Weight"
summary.means$Mean[50:64] <-
apply(X = transform.seedwt, MARGIN = 2, FUN = mean, na.rm=T)
summary.means$LL[50:64] <-
apply(X = transform.seedwt, MARGIN = 2,
FUN = function(x){quantile(x, probs = c(0.025), na.rm=T)})
summary.means$UL[50:64] <-
apply(X = transform.seedwt, MARGIN = 2,
FUN = function(x){quantile(x, probs = c(0.975), na.rm=T)})
summary.means$SD[50:64] <-
apply(X = transform.seedwt, MARGIN = 2,
FUN = sd, na.rm=T)
Specify analysis for graphing
summary.means$Analysis[summary.means$Moderator=="1 Application" |
summary.means$Moderator=="2 Applications"] <- "Applications"
summary.means$Analysis[summary.means$Moderator=="2006" |
summary.means$Moderator=="2007"|
summary.means$Moderator=="2013"] <- "Study Year"
summary.means$Analysis[summary.means$Moderator=="AZO_PROP"|
summary.means$Moderator=="FLUT"|
summary.means$Moderator=="PYR"|
summary.means$Moderator=="MIXED"|
summary.means$Moderator=="TEBU"] <- "Active Ingredient"
summary.means$Analysis[summary.means$Moderator=="low"|
summary.means$Moderator=="high"] <- "Disease Pressure"
summary.means$Analysis[summary.means$Moderator=="R1+"|
summary.means$Moderator=="R2+"|
summary.means$Moderator=="R3"|
summary.means$Moderator=="R5"] <- "Growth Stage"
summary.means$Analysis[summary.means$Moderator=="Strobilurin"|
summary.means$Moderator=="Triaz_Strob"|
summary.means$Moderator=="Triazole"] <- "Fungicide Class"
summary.means$Analysis[summary.means$Moderator=="OVERALL"] <- "Overall Mean"
Table of meta-analysis results
summary.means[summary.means$Analysis=="Overall Mean"&
!is.na(summary.means$Analysis),]
## Category Moderator Mean LL UL SD
## 1 Rust OVERALL 0.08673227 0.02417906 0.2070185 0.04786209
## 26 Yield OVERALL 1.27652556 1.20894190 1.3525662 0.03689439
## 50 Seed Weight OVERALL 1.21159967 1.15830431 1.2793500 0.03017360
## Analysis
## 1 Overall Mean
## 26 Overall Mean
## 50 Overall Mean
summary.means[summary.means$Moderator=="tau2",]
## Category Moderator Mean LL UL SD
## 2 Rust tau2 14.569327183 6.421193568 22.439328653 4.198315249
## 27 Yield tau2 0.016613024 0.001629016 0.036616434 0.009197851
## 51 Seed Weight tau2 0.001682489 0.000000000 0.007539147 0.002240498
## Analysis
## 2 <NA>
## 27 <NA>
## 51 <NA>
Save summary.means
save(summary.means, file="data/output_data/summary_results.R")
#############################################################################
Rust
table(rust.data$category_ai)
##
## FLUT MIXED PYR TEBU
## 40 129 25 31
tapply(rust.data$Reference, rust.data$category_ai,
FUN=function(x){length(unique(x))})
## FLUT MIXED PYR TEBU
## 10 30 17 20
table(rust.data$category_class)
##
## strobilurin triaz + strob triazole
## 40 98 98
tapply(rust.data$Reference, rust.data$category_class,
FUN=function(x){length(unique(x))})
## strobilurin triaz + strob triazole
## 20 26 25
table(rust.data$alphaIngred[rust.data$alphaIngred=="AZO + PROP"])
##
## AZO + PROP
## 16
tapply(rust.data$Reference[rust.data$alphaIngred=="AZO + PROP"],
rust.data$category_class[rust.data$alphaIngred=="AZO + PROP"],
FUN=function(x){length(unique(x))})
## triaz + strob
## 8
Yield
table(yield.data$category_ai)
##
## FLUT MIXED PYR TEBU
## 39 119 20 28
tapply(yield.data$Reference, yield.data$category_ai,
FUN=function(x){length(unique(x))})
## FLUT MIXED PYR TEBU
## 9 28 15 19
table(yield.data$category_class)
##
## strobilurin triaz + strob triazole
## 34 89 91
tapply(yield.data$Reference, yield.data$category_class,
FUN=function(x){length(unique(x))})
## strobilurin triaz + strob triazole
## 18 24 23
Seedwt
table(seedwt.data$category_ai)
##
## FLUT MIXED TEBU
## 30 59 18
tapply(seedwt.data$Reference, seedwt.data$category_ai,
FUN=function(x){length(unique(x))})
## FLUT MIXED TEBU
## 6 12 9
table(seedwt.data$category_class)
##
## strobilurin triaz + strob triazole
## 18 42 58
tapply(seedwt.data$Reference, seedwt.data$category_class,
FUN=function(x){length(unique(x))})
## strobilurin triaz + strob triazole
## 8 11 10
#############################################################################
Number of applications
table(rust.data$number_applications)
##
## 1 2
## 129 170
tapply(rust.data$Reference, rust.data$number_applications,
FUN=function(x){length(unique(x))})
## 1 2
## 17 23
table(yield.data$number_applications)
##
## 1 2
## 111 160
tapply(yield.data$Reference, yield.data$number_applications,
FUN=function(x){length(unique(x))})
## 1 2
## 15 22
table(seedwt.data$number_applications)
##
## 1 2
## 79 64
tapply(seedwt.data$Reference, seedwt.data$number_applications,
FUN=function(x){length(unique(x))})
## 1 2
## 9 8
Growth stage
table(rust.data$category_rstage)
##
## 1+ 2+ 3 5
## 51 75 106 25
tapply(rust.data$Reference, rust.data$category_rstage,
FUN=function(x){length(unique(x))})
## 1+ 2+ 3 5
## 8 9 14 7
table(yield.data$category_rstage)
##
## 1+ 2+ 3 5
## 47 73 100 13
tapply(yield.data$Reference, yield.data$category_rstage,
FUN=function(x){length(unique(x))})
## 1+ 2+ 3 5
## 7 9 13 5
table(seedwt.data$category_rstage)
##
## 3
## 44
tapply(seedwt.data$Reference, seedwt.data$category_rstage,
FUN=function(x){length(unique(x))})
## 3
## 5
Disease Pressure
table(rust.data$category_pressure)
##
## high low medium
## 167 109 44
tapply(rust.data$Reference, rust.data$category_pressure,
FUN=function(x){length(unique(x))})
## high low medium
## 16 13 4
table(yield.data$category_pressure)
##
## high low medium
## 152 94 42
tapply(yield.data$Reference, yield.data$category_pressure,
FUN=function(x){length(unique(x))})
## high low medium
## 15 12 4
table(seedwt.data$category_pressure)
##
## high low medium
## 98 29 17
tapply(seedwt.data$Reference, seedwt.data$category_pressure,
FUN=function(x){length(unique(x))})
## high low medium
## 8 4 1
Study Year
table(rust.data$category_year)
##
## 2006 2007 2013
## 144 53 39
tapply(rust.data$Reference, rust.data$category_year,
FUN=function(x){length(unique(x))})
## 2006 2007 2013
## 13 7 5
table(yield.data$category_year)
##
## 2006 2007 2013
## 121 44 39
tapply(yield.data$Reference, yield.data$category_year,
FUN=function(x){length(unique(x))})
## 2006 2007 2013
## 12 6 5
table(seedwt.data$category_year)
##
## 2006
## 76
tapply(seedwt.data$Reference, seedwt.data$category_year,
FUN=function(x){length(unique(x))})
## 2006
## 6
#############################################################################
Year analysis
year.regression <- summary.means[summary.means$Moderator=="Year Slope",]
year.regression.rust <- results.rust[,c("Year Intrcpt|2004", "Year Slope")]
year.regression.rust$Category <- "Rust"
year.regression.yield <- results.yield[,c("Year Intrcpt|2004", "Year Slope")]
year.regression.yield$Category <- "Yield"
year.regression.sims <- rbind(year.regression.rust, year.regression.yield)
# combine
year.regression <- merge(year.regression, year.regression.sims, by = "Category")
Applications analysis
applic.regression <- summary.means[summary.means$Moderator=="Application Slope",]
applic.regression.rust <- results.rust[,c("Application Intrcpt", "Application Slope")]
applic.regression.rust$Category <- "Rust"
applic.regression.yield <- results.yield[,c("Application Intrcpt", "Application Slope")]
applic.regression.yield$Category <- "Yield"
applic.regression.seedwt <- results.yield[,c("Application Intrcpt", "Application Slope")]
applic.regression.seedwt$Category <- "Seed Weight"
applic.regression.sims <- rbind(applic.regression.rust,
applic.regression.yield,
applic.regression.seedwt)
# combine
applic.regression <- merge(applic.regression, applic.regression.sims, by = "Category")
#############################################################################
rust.data[rust.data$category_ai=="PYR"&!is.na(rust.data$category_ai),c("amount","category_ai", "activeIngClean")]
## amount category_ai activeIngClean
## 3 7.8 PYR PYR
## 48 5.5 PYR PYR
## 114 4.5 PYR PYR
## 115 7.8 PYR PYR
## 203 9.2 PYR PYR
## 204 9.2 PYR PYR
## 205 9.2 PYR PYR
## 222 9.2 + 4 PYR PYR
## 223 9.2 + 4 PYR PYR
## 224 4 + 9.2 PYR PYR
## 229 7.8 PYR PYR
## 257 7.8 PYR PYR
## 332 7.8 PYR PYR
## 338 7.8 PYR PYR
## 376 4 + 6 PYR PYR
## 461 6 PYR PYR
## 8 7.8 PYR PYR
## 75 6 PYR PYR
## 155 6 PYR PYR
## 164 6 PYR PYR
## 272 6 PYR PYR
## 280 9.2 PYR PYR
## 281 9.2 PYR PYR
## 282 9.2 PYR PYR
## 389 6 PYR PYR
rust.data[rust.data$category_ai=="FLUT"&!is.na(rust.data$category_ai),c("amount","category_ai", "activeIngClean")]
## amount category_ai activeIngClean
## 190 7 FLUT FLUT
## 191 10 FLUT FLUT
## 194 7 FLUT FLUT
## 195 10 FLUT FLUT
## 198 7 FLUT FLUT
## 199 10 FLUT FLUT
## 226 7 FLUT FLUT
## 308 7 FLUT FLUT
## 309 11 FLUT FLUT
## 310 14 FLUT FLUT
## 311 7 FLUT FLUT
## 312 7.0 + 11.0 FLUT FLUT
## 313 7.0 + 14.0 FLUT FLUT
## 314 7 FLUT FLUT
## 315 7.0 + 11.0 FLUT FLUT
## 316 7.0 + 14.0 FLUT FLUT
## 318 3.5 FLUT FLUT
## 319 5 FLUT FLUT
## 320 7 FLUT FLUT
## 321 11 FLUT FLUT
## 322 5.0 + 0.25% FLUT FLUT
## 380 7 FLUT FLUT
## 447 7 FLUT FLUT
## 455 7 FLUT FLUT
## 465 7 FLUT FLUT
## 466 11 FLUT FLUT
## 467 14 FLUT FLUT
## 468 7,7 FLUT FLUT
## 469 7,11 FLUT FLUT
## 470 7,14 FLUT FLUT
## 471 7,7 FLUT FLUT
## 472 7,11 FLUT FLUT
## 473 7,14 FLUT FLUT
## 474 7+.25v/v FLUT FLUT
## 78 7 FLUT FLUT
## 85 7 FLUT FLUT
## 152 7 FLUT FLUT
## 161 7 FLUT FLUT
## 178 7 FLUT FLUT
## 179 7 FLUT FLUT
rust.data[rust.data$category_ai=="TEBU"&!is.na(rust.data$category_ai),c("amount","category_ai", "activeIngClean")]
## amount category_ai activeIngClean
## 43 5.1 TEBU TEBU
## 46 6.8 TEBU TEBU
## 105 4 TEBU TEBU
## 206 3.6 TEBU TEBU
## 207 3.6 TEBU TEBU
## 208 3.6 TEBU TEBU
## 209 3.6 TEBU TEBU
## 210 3.6 TEBU TEBU
## 211 3.6 TEBU TEBU
## 227 3.6 TEBU TEBU
## 247 4 TEBU TEBU
## 248 4 TEBU TEBU
## 317 3.6 TEBU TEBU
## 453 4 TEBU TEBU
## 454 4 TEBU TEBU
## 477 4,4 TEBU TEBU
## 4 4 TEBU TEBU
## 16 4 TEBU TEBU
## 81 4 TEBU TEBU
## 97 4 TEBU TEBU
## 154 4 TEBU TEBU
## 163 4 TEBU TEBU
## 182 4 TEBU TEBU
## 183 4 TEBU TEBU
## 271 4 TEBU TEBU
## 283 4 TEBU TEBU
## 284 4 TEBU TEBU
## 285 4 TEBU TEBU
## 325 4 TEBU TEBU
## 391 4 TEBU TEBU
## 434 4 TEBU TEBU
rust.data[rust.data$alphaIngred=="AZO + PYR"&!is.na(rust.data$alphaIngred),c("amount","category_ai", "activeIngClean")]
## amount category_ai activeIngClean
## 239 6.2 + 7.8 MIXED MIXED
Spun with ezspin(“programs/results_processing.R”, out_dir=“output”, fig_dir=“figures”, keep_md=FALSE)
Session Info:
devtools::session_info()
## Session info --------------------------------------------------------------
## setting value
## version R version 3.3.2 (2016-10-31)
## system x86_64, darwin13.4.0
## ui RStudio (0.99.902)
## language (EN)
## collate en_US.UTF-8
## tz America/Chicago
## date 2017-01-25
## Packages ------------------------------------------------------------------
## package * version date source
## devtools * 1.12.0 2016-06-24 CRAN (R 3.3.0)
## digest 0.6.10 2016-08-02 CRAN (R 3.3.0)
## doBy * 4.5-15 2016-03-31 CRAN (R 3.3.0)
## evaluate 0.10 2016-10-11 CRAN (R 3.3.0)
## ezknitr * 0.6 2016-09-16 CRAN (R 3.3.0)
## knitr * 1.15.1 2016-11-22 CRAN (R 3.3.2)
## lattice 0.20-34 2016-09-06 CRAN (R 3.3.2)
## magrittr 1.5 2014-11-22 CRAN (R 3.3.0)
## MASS 7.3-45 2016-04-21 CRAN (R 3.3.2)
## Matrix * 1.2-7.1 2016-09-01 CRAN (R 3.3.2)
## memoise 1.0.0 2016-01-29 CRAN (R 3.3.0)
## metafor * 1.9-9 2016-09-25 CRAN (R 3.3.0)
## R.methodsS3 1.7.1 2016-02-16 CRAN (R 3.3.0)
## R.oo 1.21.0 2016-11-01 CRAN (R 3.3.0)
## R.utils 2.5.0 2016-11-07 CRAN (R 3.3.0)
## stringi 1.1.2 2016-10-01 CRAN (R 3.3.0)
## stringr 1.1.0 2016-08-19 CRAN (R 3.3.0)
## withr 1.0.2 2016-06-20 CRAN (R 3.3.0)