Load Libraries
library(ezknitr)
library(knitr)
library(metafor)
Clear environment
remove(list=ls())
Document settings
opts_chunk$set(fig.width = 6, fig.height = 4)
load(file="data/output_data/data_cleaned.R")
Overall raw mean difference
overall.rust <- rma.uni(yi = yi, vi = vi, data = rust.data.MD, method="ML")
summary(overall.rust)
##
## Random-Effects Model (k = 324; tau^2 estimator: ML)
##
## logLik deviance AIC BIC AICc
## -1558.2544 808.4819 3120.5088 3128.0703 3120.5462
##
## tau^2 (estimated amount of total heterogeneity): 690.3940 (SE = 69.8629)
## tau (square root of estimated tau^2 value): 26.2753
## I^2 (total heterogeneity / total variability): 78.16%
## H^2 (total variability / sampling variability): 4.58
##
## Test for Heterogeneity:
## Q(df = 323) = 1604.9995, p-val < .0001
##
## Model Results:
##
## estimate se zval pval ci.lb ci.ub
## -35.7289 1.6571 -21.5611 <.0001 -38.9768 -32.4810 ***
##
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
Effects of Active Ingredient on SMD
rust.active <- rma.uni(yi = yi, vi = vi, data=rust.data.SMD,
method = "REML", mods = ~activeIngClean-1)
summary(rust.active)
##
## Mixed-Effects Model (k = 324; tau^2 estimator: REML)
##
## logLik deviance AIC BIC AICc
## -487.7921 975.5842 999.5842 1044.5387 1000.6242
##
## tau^2 (estimated amount of residual heterogeneity): 0.4793 (SE = 0.0911)
## tau (square root of estimated tau^2 value): 0.6923
## I^2 (residual heterogeneity / unaccounted variability): 42.31%
## H^2 (unaccounted variability / sampling variability): 1.73
##
## Test for Residual Heterogeneity:
## QE(df = 313) = 560.0651, p-val < .0001
##
## Test of Moderators (coefficient(s) 1,2,3,4,5,6,7,8,9,10,11):
## QM(df = 11) = 549.8626, p-val < .0001
##
## Model Results:
##
## estimate se zval pval ci.lb ci.ub
## activeIngCleanazo -1.1482 0.4477 -2.5648 0.0103 -2.0256 -0.2708
## activeIngCleandual -3.1655 0.2736 -11.5715 <.0001 -3.7017 -2.6293
## activeIngCleanflus -1.9558 0.4310 -4.5374 <.0001 -2.8006 -1.1110
## activeIngCleanflut -1.5930 0.1685 -9.4549 <.0001 -1.9232 -1.2627
## activeIngCleangly -0.4990 0.4053 -1.2310 0.2183 -1.2934 0.2954
## activeIngCleanmixed -1.1772 0.0936 -12.5771 <.0001 -1.3606 -0.9937
## activeIngCleanoth -0.8763 0.1588 -5.5190 <.0001 -1.1875 -0.5651
## activeIngCleanpyra -1.2766 0.2155 -5.9245 <.0001 -1.6989 -0.8543
## activeIngCleantebu -1.4328 0.1958 -7.3169 <.0001 -1.8166 -1.0490
## activeIngCleantetra -1.2249 0.3422 -3.5794 0.0003 -1.8957 -0.5542
## activeIngCleanthio -1.3532 0.4847 -2.7920 0.0052 -2.3031 -0.4033
##
## activeIngCleanazo *
## activeIngCleandual ***
## activeIngCleanflus ***
## activeIngCleanflut ***
## activeIngCleangly
## activeIngCleanmixed ***
## activeIngCleanoth ***
## activeIngCleanpyra ***
## activeIngCleantebu ***
## activeIngCleantetra ***
## activeIngCleanthio **
##
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
Effects of Class on SMD
rust.class <- rma.uni(yi = yi, vi = vi, data=rust.data.SMD,
method = "REML", mods = ~classClean-1)
summary(rust.class)
##
## Mixed-Effects Model (k = 324; tau^2 estimator: REML)
##
## logLik deviance AIC BIC AICc
## -517.2490 1034.4979 1046.4979 1069.0891 1046.7672
##
## tau^2 (estimated amount of residual heterogeneity): 0.6891 (SE = 0.1075)
## tau (square root of estimated tau^2 value): 0.8301
## I^2 (residual heterogeneity / unaccounted variability): 51.35%
## H^2 (unaccounted variability / sampling variability): 2.06
##
## Test for Residual Heterogeneity:
## QE(df = 319) = 657.1024, p-val < .0001
##
## Test of Moderators (coefficient(s) 1,2,3,4,5):
## QM(df = 5) = 438.5411, p-val < .0001
##
## Model Results:
##
## estimate se zval pval ci.lb
## classCleanherbicide -0.5089 0.4467 -1.1393 0.2546 -1.3844
## classCleanmma -1.3931 0.0967 -14.4137 <.0001 -1.5825
## classCleanother -1.0389 0.1871 -5.5526 <.0001 -1.4056
## classCleanstrobilurin -1.0313 0.2053 -5.0227 <.0001 -1.4337
## classCleantriazole -1.5641 0.1188 -13.1693 <.0001 -1.7969
## ci.ub
## classCleanherbicide 0.3666
## classCleanmma -1.2037 ***
## classCleanother -0.6722 ***
## classCleanstrobilurin -0.6288 ***
## classCleantriazole -1.3313 ***
##
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
Effects of Growth stage on SMD
rust.growth.stage <- rma.uni(yi = yi, vi = vi,
data=rust.data.SMD[rust.data.SMD$growthStateClean!="unknown",],
method = "REML", mods = ~growthStateClean-1)
summary(rust.growth.stage)
##
## Mixed-Effects Model (k = 306; tau^2 estimator: REML)
##
## logLik deviance AIC BIC AICc
## -450.8324 901.6647 919.6647 952.9385 920.2897
##
## tau^2 (estimated amount of residual heterogeneity): 0.4318 (SE = 0.0890)
## tau (square root of estimated tau^2 value): 0.6571
## I^2 (residual heterogeneity / unaccounted variability): 39.90%
## H^2 (unaccounted variability / sampling variability): 1.66
##
## Test for Residual Heterogeneity:
## QE(df = 298) = 507.3941, p-val < .0001
##
## Test of Moderators (coefficient(s) 1,2,3,4,5,6,7,8):
## QM(df = 8) = 554.8558, p-val < .0001
##
## Model Results:
##
## estimate se zval pval ci.lb ci.ub
## growthStateClean1 -0.8845 0.3240 -2.7298 0.0063 -1.5195 -0.2494
## growthStateClean1+ -1.6147 0.1527 -10.5715 <.0001 -1.9141 -1.3153
## growthStateClean2 -2.4927 0.4070 -6.1241 <.0001 -3.2905 -1.6949
## growthStateClean2+ -2.1057 0.1263 -16.6721 <.0001 -2.3532 -1.8581
## growthStateClean3 -0.9136 0.0983 -9.2900 <.0001 -1.1063 -0.7209
## growthStateClean4 -1.0194 0.2496 -4.0848 <.0001 -1.5086 -0.5303
## growthStateClean5 -0.8014 0.1991 -4.0250 <.0001 -1.1916 -0.4111
## growthStateCleanV -0.3088 0.3097 -0.9972 0.3187 -0.9157 0.2981
##
## growthStateClean1 **
## growthStateClean1+ ***
## growthStateClean2 ***
## growthStateClean2+ ***
## growthStateClean3 ***
## growthStateClean4 ***
## growthStateClean5 ***
## growthStateCleanV
##
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
Effects of application number on SMD
table(rust.data.SMD$applicationsNumb)
##
## 1 2 3 5
## 133 170 15 6
rust.applications <- rma.uni(yi = yi, vi = vi,
data=rust.data.SMD,
method = "REML", mods = ~applicationsNumb)
summary(rust.applications)
##
## Mixed-Effects Model (k = 324; tau^2 estimator: REML)
##
## logLik deviance AIC BIC AICc
## -525.4152 1050.8304 1056.8304 1068.1541 1056.9059
##
## tau^2 (estimated amount of residual heterogeneity): 0.7201 (SE = 0.1095)
## tau (square root of estimated tau^2 value): 0.8486
## I^2 (residual heterogeneity / unaccounted variability): 52.48%
## H^2 (unaccounted variability / sampling variability): 2.10
## R^2 (amount of heterogeneity accounted for): 0.24%
##
## Test for Residual Heterogeneity:
## QE(df = 322) = 677.7382, p-val < .0001
##
## Test of Moderators (coefficient(s) 2):
## QM(df = 1) = 2.1017, p-val = 0.1471
##
## Model Results:
##
## estimate se zval pval ci.lb ci.ub
## intrcpt -1.1351 0.1622 -6.9990 <.0001 -1.4530 -0.8173 ***
## applicationsNumb -0.1276 0.0880 -1.4497 0.1471 -0.3000 0.0449
##
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
# Categorical
rust.data.SMD$applicationsCat <- as.character(rust.data.SMD$applicationsNumb)
rust.applications.cat <- rma.uni(yi = yi, vi = vi,
data=rust.data.SMD,
method = "REML", mods = ~applicationsCat-1)
summary(rust.applications.cat)
##
## Mixed-Effects Model (k = 324; tau^2 estimator: REML)
##
## logLik deviance AIC BIC AICc
## -506.4060 1012.8121 1022.8121 1041.6537 1023.0031
##
## tau^2 (estimated amount of residual heterogeneity): 0.5800 (SE = 0.0983)
## tau (square root of estimated tau^2 value): 0.7616
## I^2 (residual heterogeneity / unaccounted variability): 47.06%
## H^2 (unaccounted variability / sampling variability): 1.89
##
## Test for Residual Heterogeneity:
## QE(df = 320) = 612.5926, p-val < .0001
##
## Test of Moderators (coefficient(s) 1,2,3,4):
## QM(df = 4) = 492.5922, p-val < .0001
##
## Model Results:
##
## estimate se zval pval ci.lb ci.ub
## applicationsCat1 -0.9808 0.0957 -10.2483 <.0001 -1.1683 -0.7932
## applicationsCat2 -1.6748 0.0877 -19.1016 <.0001 -1.8466 -1.5029
## applicationsCat3 -1.4124 0.2966 -4.7618 <.0001 -1.9937 -0.8310
## applicationsCat5 -0.0577 0.4245 -0.1359 0.8919 -0.8896 0.7742
##
## applicationsCat1 ***
## applicationsCat2 ***
## applicationsCat3 ***
## applicationsCat5
##
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
Overall mean difference
overall.yield <- rma.uni(yi = yi, vi = vi, data = yield.data.MD, method = "REML")
summary(overall.yield)
##
## Random-Effects Model (k = 513; tau^2 estimator: REML)
##
## logLik deviance AIC BIC AICc
## -3963.2069 7926.4138 7930.4138 7938.8904 7930.4374
##
## tau^2 (estimated amount of total heterogeneity): 119824.4635 (SE = 18147.5135)
## tau (square root of estimated tau^2 value): 346.1567
## I^2 (total heterogeneity / total variability): 43.49%
## H^2 (total variability / sampling variability): 1.77
##
## Test for Heterogeneity:
## Q(df = 512) = 887.3935, p-val < .0001
##
## Model Results:
##
## estimate se zval pval ci.lb ci.ub
## 525.0076 24.6017 21.3403 <.0001 476.7892 573.2260 ***
##
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
Effects of Active Ingredient on SMD
yield.active <- rma.uni(yi = yi, vi = vi, data=yield.data.SMD,
method = "REML", mods = ~activeIngClean-1)
summary(yield.active)
##
## Mixed-Effects Model (k = 513; tau^2 estimator: REML)
##
## logLik deviance AIC BIC AICc
## -526.1748 1052.3496 1078.3496 1133.1654 1079.0970
##
## tau^2 (estimated amount of residual heterogeneity): 0 (SE = 0.0342)
## tau (square root of estimated tau^2 value): 0
## I^2 (residual heterogeneity / unaccounted variability): 0.00%
## H^2 (unaccounted variability / sampling variability): 1.00
##
## Test for Residual Heterogeneity:
## QE(df = 501) = 419.8387, p-val = 0.9965
##
## Test of Moderators (coefficient(s) 1,2,3,4,5,6,7,8,9,10,11,12):
## QM(df = 12) = 467.2884, p-val < .0001
##
## Model Results:
##
## estimate se zval pval ci.lb ci.ub
## activeIngCleanazo 0.2080 0.1412 1.4732 0.1407 -0.0687 0.4847
## activeIngCleancypr 0.3131 0.2966 1.0559 0.2910 -0.2681 0.8944
## activeIngCleandual 1.1559 0.1049 11.0143 <.0001 0.9502 1.3616
## activeIngCleanflus 0.7149 0.2806 2.5478 0.0108 0.1650 1.2649
## activeIngCleanflut 0.7107 0.0985 7.2124 <.0001 0.5176 0.9038
## activeIngCleanmixed 0.6782 0.0583 11.6359 <.0001 0.5639 0.7924
## activeIngCleanmyc 0.7688 0.2268 3.3902 0.0007 0.3243 1.2133
## activeIngCleanoth 0.5584 0.0990 5.6415 <.0001 0.3644 0.7524
## activeIngCleanpyra 0.6662 0.1104 6.0324 <.0001 0.4497 0.8826
## activeIngCleantebu 0.7252 0.1098 6.6039 <.0001 0.5100 0.9405
## activeIngCleantetra 0.5792 0.1244 4.6544 <.0001 0.3353 0.8231
## activeIngCleanthio 0.4634 0.2377 1.9494 0.0513 -0.0025 0.9292
##
## activeIngCleanazo
## activeIngCleancypr
## activeIngCleandual ***
## activeIngCleanflus *
## activeIngCleanflut ***
## activeIngCleanmixed ***
## activeIngCleanmyc ***
## activeIngCleanoth ***
## activeIngCleanpyra ***
## activeIngCleantebu ***
## activeIngCleantetra ***
## activeIngCleanthio .
##
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
Effects of Class on SMD
yield.class <- rma.uni(yi = yi, vi = vi, data=yield.data.SMD,
method = "REML", mods = ~classClean-1)
summary(yield.class)
##
## Mixed-Effects Model (k = 513; tau^2 estimator: REML)
##
## logLik deviance AIC BIC AICc
## -541.6601 1083.3202 1095.3202 1120.7030 1095.4878
##
## tau^2 (estimated amount of residual heterogeneity): 0.0000 (SE = 0.0339)
## tau (square root of estimated tau^2 value): 0.0029
## I^2 (residual heterogeneity / unaccounted variability): 0.00%
## H^2 (unaccounted variability / sampling variability): 1.00
##
## Test for Residual Heterogeneity:
## QE(df = 508) = 442.1905, p-val = 0.9838
##
## Test of Moderators (coefficient(s) 1,2,3,4,5):
## QM(df = 5) = 444.9295, p-val < .0001
##
## Model Results:
##
## estimate se zval pval ci.lb ci.ub
## classCleanmma 0.7923 0.0511 15.5101 <.0001 0.6922 0.8925
## classCleanother 0.6390 0.1092 5.8520 <.0001 0.4250 0.8530
## classCleanstrobilurin 0.4391 0.0831 5.2835 <.0001 0.2762 0.6020
## classCleanthiophanate 0.4310 0.2514 1.7144 0.0864 -0.0617 0.9236
## classCleantriazole 0.6775 0.0574 11.8011 <.0001 0.5650 0.7900
##
## classCleanmma ***
## classCleanother ***
## classCleanstrobilurin ***
## classCleanthiophanate .
## classCleantriazole ***
##
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
Effects of Growth stage on SMD
yield.growth.stage <- rma.uni(yi = yi, vi = vi,
data=yield.data.SMD[yield.data.SMD$growthStateClean!="unknown",],
method = "REML", mods = ~growthStateClean-1)
summary(yield.growth.stage)
##
## Mixed-Effects Model (k = 490; tau^2 estimator: REML)
##
## logLik deviance AIC BIC AICc
## -511.1338 1022.2675 1038.2675 1071.7077 1038.5713
##
## tau^2 (estimated amount of residual heterogeneity): 0 (SE = 0.0347)
## tau (square root of estimated tau^2 value): 0
## I^2 (residual heterogeneity / unaccounted variability): 0.00%
## H^2 (unaccounted variability / sampling variability): 1.00
##
## Test for Residual Heterogeneity:
## QE(df = 483) = 414.1355, p-val = 0.9895
##
## Test of Moderators (coefficient(s) 1,2,3,4,5,6,7):
## QM(df = 7) = 443.9602, p-val < .0001
##
## Model Results:
##
## estimate se zval pval ci.lb ci.ub
## growthStateClean1 0.4066 0.1863 2.1828 0.0291 0.0415 0.7717 *
## growthStateClean1+ 0.7522 0.0992 7.5814 <.0001 0.5577 0.9466 ***
## growthStateClean2 0.5839 0.1554 3.7587 0.0002 0.2794 0.8884 ***
## growthStateClean2+ 0.7995 0.0643 12.4296 <.0001 0.6734 0.9256 ***
## growthStateClean3 0.6083 0.0540 11.2615 <.0001 0.5024 0.7142 ***
## growthStateClean4 1.6593 0.2212 7.5005 <.0001 1.2257 2.0929 ***
## growthStateClean5 0.5119 0.0934 5.4787 <.0001 0.3288 0.6950 ***
##
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
Effects of application number on SMD
table(yield.data.SMD$applicationsNumb)
##
## 1 2 3
## 254 238 21
yield.applications <- rma.uni(yi = yi, vi = vi,
data=yield.data.SMD,
method = "REML", mods = ~applicationsNumb)
summary(yield.applications)
##
## Mixed-Effects Model (k = 513; tau^2 estimator: REML)
##
## logLik deviance AIC BIC AICc
## -546.4379 1092.8757 1098.8757 1111.5848 1098.9230
##
## tau^2 (estimated amount of residual heterogeneity): 0.0000 (SE = 0.0338)
## tau (square root of estimated tau^2 value): 0.0025
## I^2 (residual heterogeneity / unaccounted variability): 0.00%
## H^2 (unaccounted variability / sampling variability): 1.00
## R^2 (amount of heterogeneity accounted for): 0.00%
##
## Test for Residual Heterogeneity:
## QE(df = 511) = 448.0084, p-val = 0.9792
##
## Test of Moderators (coefficient(s) 2):
## QM(df = 1) = 8.5465, p-val = 0.0035
##
## Model Results:
##
## estimate se zval pval ci.lb ci.ub
## intrcpt 0.4211 0.0944 4.4582 <.0001 0.2360 0.6062 ***
## applicationsNumb 0.1667 0.0570 2.9234 0.0035 0.0550 0.2785 **
##
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
# Categorical
yield.data.SMD$applicationsCat <- as.character(yield.data.SMD$applicationsNumb)
yield.applications.cat <- rma.uni(yi = yi, vi = vi,
data=yield.data.SMD,
method = "REML", mods = ~applicationsCat-1)
summary(yield.applications.cat)
##
## Mixed-Effects Model (k = 513; tau^2 estimator: REML)
##
## logLik deviance AIC BIC AICc
## -544.2503 1088.5007 1096.5007 1113.4383 1096.5799
##
## tau^2 (estimated amount of residual heterogeneity): 0.0000 (SE = 0.0338)
## tau (square root of estimated tau^2 value): 0.0027
## I^2 (residual heterogeneity / unaccounted variability): 0.00%
## H^2 (unaccounted variability / sampling variability): 1.00
##
## Test for Residual Heterogeneity:
## QE(df = 510) = 444.8633, p-val = 0.9827
##
## Test of Moderators (coefficient(s) 1,2,3):
## QM(df = 3) = 442.2577, p-val < .0001
##
## Model Results:
##
## estimate se zval pval ci.lb ci.ub
## applicationsCat1 0.5676 0.0469 12.1043 <.0001 0.4757 0.6595 ***
## applicationsCat2 0.7965 0.0478 16.6646 <.0001 0.7029 0.8902 ***
## applicationsCat3 0.6833 0.1609 4.2466 <.0001 0.3679 0.9987 ***
##
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
Overall mean difference
overall.seedwt <- rma.uni(yi = yi, vi = vi, data = seedwt.data.MD, method = "REML")
summary(overall.seedwt)
##
## Random-Effects Model (k = 207; tau^2 estimator: REML)
##
## logLik deviance AIC BIC AICc
## -275.9736 551.9472 555.9472 562.6030 556.0063
##
## tau^2 (estimated amount of total heterogeneity): 0.0028 (SE = 0.0014)
## tau (square root of estimated tau^2 value): 0.0529
## I^2 (total heterogeneity / total variability): 14.28%
## H^2 (total variability / sampling variability): 1.17
##
## Test for Heterogeneity:
## Q(df = 206) = 221.5038, p-val = 0.2183
##
## Model Results:
##
## estimate se zval pval ci.lb ci.ub
## 0.1302 0.0119 10.9485 <.0001 0.1069 0.1535 ***
##
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
Effects of Active Ingredient on SMD
seedwt.active <- rma.uni(yi = yi, vi = vi, data=seedwt.data.SMD,
method = "REML", mods = ~activeIngClean-1)
summary(seedwt.active)
##
## Mixed-Effects Model (k = 207; tau^2 estimator: REML)
##
## logLik deviance AIC BIC AICc
## -178.4013 356.8026 372.8026 399.1891 373.5565
##
## tau^2 (estimated amount of residual heterogeneity): 0 (SE = 0.0534)
## tau (square root of estimated tau^2 value): 0
## I^2 (residual heterogeneity / unaccounted variability): 0.00%
## H^2 (unaccounted variability / sampling variability): 1.00
##
## Test for Residual Heterogeneity:
## QE(df = 200) = 112.2592, p-val = 1.0000
##
## Test of Moderators (coefficient(s) 1,2,3,4,5,6,7):
## QM(df = 7) = 125.1656, p-val < .0001
##
## Model Results:
##
## estimate se zval pval ci.lb ci.ub
## activeIngCleanazo 0.1393 0.2361 0.5899 0.5553 -0.3235 0.6020
## activeIngCleanflut 0.7203 0.1159 6.2129 <.0001 0.4931 0.9475
## activeIngCleanmixed 0.5333 0.0853 6.2502 <.0001 0.3661 0.7005
## activeIngCleanoth 0.4773 0.1372 3.4792 0.0005 0.2084 0.7461
## activeIngCleanpyra 0.5750 0.1668 3.4481 0.0006 0.2482 0.9018
## activeIngCleantebu 0.7533 0.1704 4.4197 <.0001 0.4192 1.0873
## activeIngCleantetra 0.3335 0.1751 1.9042 0.0569 -0.0098 0.6767
##
## activeIngCleanazo
## activeIngCleanflut ***
## activeIngCleanmixed ***
## activeIngCleanoth ***
## activeIngCleanpyra ***
## activeIngCleantebu ***
## activeIngCleantetra .
##
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
Effects of Class on SMD
seedwt.class <- rma.uni(yi = yi, vi = vi, data=seedwt.data.SMD,
method = "REML", mods = ~classClean-1)
summary(seedwt.class)
##
## Mixed-Effects Model (k = 207; tau^2 estimator: REML)
##
## logLik deviance AIC BIC AICc
## -181.2380 362.4760 372.4760 389.0421 372.7806
##
## tau^2 (estimated amount of residual heterogeneity): 0 (SE = 0.0530)
## tau (square root of estimated tau^2 value): 0
## I^2 (residual heterogeneity / unaccounted variability): 0.00%
## H^2 (unaccounted variability / sampling variability): 1.00
##
## Test for Residual Heterogeneity:
## QE(df = 203) = 114.2499, p-val = 1.0000
##
## Test of Moderators (coefficient(s) 1,2,3,4):
## QM(df = 4) = 123.1749, p-val < .0001
##
## Model Results:
##
## estimate se zval pval ci.lb ci.ub
## classCleanmma 0.5990 0.0832 7.1965 <.0001 0.4358 0.7621
## classCleanother 0.2338 0.1761 1.3275 0.1844 -0.1114 0.5789
## classCleanstrobilurin 0.3470 0.1460 2.3757 0.0175 0.0607 0.6332
## classCleantriazole 0.6278 0.0785 7.9987 <.0001 0.4740 0.7816
##
## classCleanmma ***
## classCleanother
## classCleanstrobilurin *
## classCleantriazole ***
##
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
Effects of Growth stage on SMD
seedwt.growth.stage <- rma.uni(yi = yi, vi = vi,
data=seedwt.data.SMD[seedwt.data.SMD$growthStateClean!="unknown",],
method = "REML", mods = ~growthStateClean-1)
summary(seedwt.growth.stage)
##
## Mixed-Effects Model (k = 198; tau^2 estimator: REML)
##
## logLik deviance AIC BIC AICc
## -161.0261 322.0522 336.0522 358.8546 336.6609
##
## tau^2 (estimated amount of residual heterogeneity): 0 (SE = 0.0546)
## tau (square root of estimated tau^2 value): 0
## I^2 (residual heterogeneity / unaccounted variability): 0.00%
## H^2 (unaccounted variability / sampling variability): 1.00
##
## Test for Residual Heterogeneity:
## QE(df = 192) = 86.8329, p-val = 1.0000
##
## Test of Moderators (coefficient(s) 1,2,3,4,5,6):
## QM(df = 6) = 149.4960, p-val < .0001
##
## Model Results:
##
## estimate se zval pval ci.lb ci.ub
## growthStateClean1+ 0.8785 0.1589 5.5275 <.0001 0.5670 1.1900 ***
## growthStateClean2 0.2754 0.1561 1.7640 0.0777 -0.0306 0.5813 .
## growthStateClean2+ 0.3292 0.1077 3.0565 0.0022 0.1181 0.5402 **
## growthStateClean3 0.5470 0.0823 6.6499 <.0001 0.3858 0.7082 ***
## growthStateClean4 1.4361 0.1977 7.2647 <.0001 1.0486 1.8235 ***
## growthStateClean5 0.5853 0.1900 3.0811 0.0021 0.2130 0.9576 **
##
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
Effects of application number on SMD
table(seedwt.data.SMD$applicationsNumb)
##
## 1 2
## 116 91
seedwt.applications <- rma.uni(yi = yi, vi = vi,
data=seedwt.data.SMD,
method = "REML", mods = ~applicationsNumb)
summary(seedwt.applications)
##
## Mixed-Effects Model (k = 207; tau^2 estimator: REML)
##
## logLik deviance AIC BIC AICc
## -183.4827 366.9653 372.9653 382.9344 373.0847
##
## tau^2 (estimated amount of residual heterogeneity): 0 (SE = 0.0527)
## tau (square root of estimated tau^2 value): 0
## I^2 (residual heterogeneity / unaccounted variability): 0.00%
## H^2 (unaccounted variability / sampling variability): 1.00
## R^2 (amount of heterogeneity accounted for): NA%
##
## Test for Residual Heterogeneity:
## QE(df = 205) = 116.4111, p-val = 1.0000
##
## Test of Moderators (coefficient(s) 2):
## QM(df = 1) = 4.3252, p-val = 0.0376
##
## Model Results:
##
## estimate se zval pval ci.lb ci.ub
## intrcpt 0.2443 0.1555 1.5708 0.1162 -0.0605 0.5491
## applicationsNumb 0.2140 0.1029 2.0797 0.0376 0.0123 0.4156 *
##
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
# Categorical
seedwt.data.SMD$applicationsCat <- as.character(seedwt.data.SMD$applicationsNumb)
seedwt.applications.cat <- rma.uni(yi = yi, vi = vi,
data=seedwt.data.SMD,
method = "REML", mods = ~applicationsCat-1)
summary(seedwt.applications.cat)
##
## Mixed-Effects Model (k = 207; tau^2 estimator: REML)
##
## logLik deviance AIC BIC AICc
## -183.4827 366.9653 372.9653 382.9344 373.0847
##
## tau^2 (estimated amount of residual heterogeneity): 0 (SE = 0.0527)
## tau (square root of estimated tau^2 value): 0
## I^2 (residual heterogeneity / unaccounted variability): 0.00%
## H^2 (unaccounted variability / sampling variability): 1.00
##
## Test for Residual Heterogeneity:
## QE(df = 205) = 116.4111, p-val = 1.0000
##
## Test of Moderators (coefficient(s) 1,2):
## QM(df = 2) = 121.0136, p-val < .0001
##
## Model Results:
##
## estimate se zval pval ci.lb ci.ub
## applicationsCat1 0.4583 0.0673 6.8045 <.0001 0.3263 0.5903 ***
## applicationsCat2 0.6722 0.0778 8.6436 <.0001 0.5198 0.8246 ***
##
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
Overall mean difference
overall.cerco <- rma.uni(yi = yi, vi = vi, data = cerco.data.MD, method = "REML")
summary(overall.cerco)
##
## Random-Effects Model (k = 40; tau^2 estimator: REML)
##
## logLik deviance AIC BIC AICc
## -151.1431 302.2862 306.2862 309.6134 306.6196
##
## tau^2 (estimated amount of total heterogeneity): 0 (SE = 57.1784)
## tau (square root of estimated tau^2 value): 0
## I^2 (total heterogeneity / total variability): 0.00%
## H^2 (total variability / sampling variability): 1.00
##
## Test for Heterogeneity:
## Q(df = 39) = 14.4324, p-val = 0.9999
##
## Model Results:
##
## estimate se zval pval ci.lb ci.ub
## -9.6508 2.5198 -3.8300 0.0001 -14.5894 -4.7121 ***
##
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
Overall mean difference
overall.target.spot <- rma.uni(yi = yi, vi = vi, data = target.spot.data.MD, method = "REML")
summary(overall.target.spot)
##
## Random-Effects Model (k = 31; tau^2 estimator: REML)
##
## logLik deviance AIC BIC AICc
## -135.3497 270.6994 274.6994 277.5018 275.1439
##
## tau^2 (estimated amount of total heterogeneity): 285.6250 (SE = 125.3878)
## tau (square root of estimated tau^2 value): 16.9004
## I^2 (total heterogeneity / total variability): 58.82%
## H^2 (total variability / sampling variability): 2.43
##
## Test for Heterogeneity:
## Q(df = 30) = 72.8437, p-val < .0001
##
## Model Results:
##
## estimate se zval pval ci.lb ci.ub
## -18.9129 3.9579 -4.7785 <.0001 -26.6703 -11.1555 ***
##
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
save(overall.rust, overall.yield, overall.seedwt, overall.cerco, overall.target.spot,
rust.active, rust.class, rust.growth.stage, rust.applications, rust.applications.cat,
yield.active, yield.class, yield.growth.stage, yield.applications, yield.applications.cat,
seedwt.active, seedwt.class, seedwt.growth.stage, seedwt.applications, seedwt.applications.cat,
file="data/output_data/analysis_results.R")
Spun with ezspin(“programs/data_analysis.R”, out_dir=“output”, fig_dir=“figures”, keep_md=FALSE)
Session Info:
sessionInfo()
## R version 3.3.0 (2016-05-03)
## Platform: x86_64-apple-darwin13.4.0 (64-bit)
## Running under: OS X 10.10.5 (Yosemite)
##
## locale:
## [1] en_US.UTF-8/en_US.UTF-8/en_US.UTF-8/C/en_US.UTF-8/en_US.UTF-8
##
## attached base packages:
## [1] stats graphics grDevices utils datasets methods base
##
## other attached packages:
## [1] doBy_4.5-15 metafor_1.9-8 Matrix_1.2-6 knitr_1.13 ezknitr_0.4
##
## loaded via a namespace (and not attached):
## [1] lattice_0.20-33 R.methodsS3_1.7.1 MASS_7.3-45
## [4] grid_3.3.0 formatR_1.4 magrittr_1.5
## [7] evaluate_0.9 stringi_1.1.1 R.oo_1.20.0
## [10] R.utils_2.3.0 tools_3.3.0 stringr_1.0.0
## [13] markdown_0.7.7