Perform Pairwise t Tests of Adjusted Means from augmentedRCBD Output
Source: R/pairwise.augmentedRCBD.R
pairwise.augmentedRCBD.Rdpairwise.augmentedRCBD performs pairwise t tests of adjusted means from
an object of class augmentedRCBD.
Usage
pairwise.augmentedRCBD(aug, cl = NULL, p.adjust = c("none", "tukey", "sidak"))Arguments
- aug
An object of class
augmentedRCBD.- cl
A cluster object created by
makeClusterfor parallel evaluations.- p.adjust
The p value adjustment method. Either
"none","tukey"or"sidak".
Details
The default pairwise comparison in augmentedRCBD
employs pairs.emmGrid function from
emmeans which is very slow for large number of
comparisons. This function attempts to do the same faster with parallel
computing with the package parallel-package.
Examples
library(augmentedRCBD)
#>
#> --------------------------------------------------------------------------------
#> Welcome to augmentedRCBD version 0.1.7
#>
#>
#> # To know how to use this package type:
#> browseVignettes(package = 'augmentedRCBD')
#> for the package vignette.
#>
#> # To know whats new in this version type:
#> news(package='augmentedRCBD')
#> for the NEWS file.
#>
#> # To cite the methods in the package type:
#> citation(package='augmentedRCBD')
#>
#> # To suppress this message use:
#> suppressPackageStartupMessages(library(augmentedRCBD))
#> --------------------------------------------------------------------------------
blk <- c(rep(1,7),rep(2,6),rep(3,7))
trt <- c(1, 2, 3, 4, 7, 11, 12, 1, 2, 3, 4, 5, 9, 1, 2, 3, 4, 8, 6, 10)
y1 <- c(92, 79, 87, 81, 96, 89, 82, 79, 81, 81, 91, 79, 78, 83, 77, 78, 78,
70, 75, 74)
y2 <- c(258, 224, 238, 278, 347, 300, 289, 260, 220, 237, 227, 281, 311, 250,
240, 268, 287, 226, 395, 450)
data <- data.frame(blk, trt, y1, y2)
# Convert block and treatment to factors
data$blk <- as.factor(data$blk)
data$trt <- as.factor(data$trt)
# Results for variable y1
out1 <- augmentedRCBD(data$blk, data$trt, data$y1, method.comp = "lsd",
alpha = 0.05, group = TRUE, console = TRUE)
#>
#> Augmented Design Details
#> ========================
#>
#> Number of blocks "3"
#> Number of treatments "12"
#> Number of check treatments "4"
#> Number of test treatments "8"
#> Check treatments "1, 2, 3, 4"
#>
#>
#> ANOVA, Treatment Adjusted
#> =========================
#> Df Sum Sq Mean Sq F value Pr(>F)
#> Block (ignoring Treatments) 2 360.1 180.04 6.675 0.0298 *
#> Treatment (eliminating Blocks) 11 285.1 25.92 0.961 0.5499
#> Treatment: Check 3 52.9 17.64 0.654 0.6092
#> Treatment: Test and Test vs. Check 8 232.2 29.02 1.076 0.4779
#> Residuals 6 161.8 26.97
#> ---
#> Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1
#>
#> ANOVA, Block Adjusted
#> =====================
#> Df Sum Sq Mean Sq F value Pr(>F)
#> Treatment (ignoring Blocks) 11 575.7 52.33 1.940 0.215
#> Treatment: Check 3 52.9 17.64 0.654 0.609
#> Treatment: Test 7 505.9 72.27 2.679 0.125
#> Treatment: Test vs. Check 1 16.9 16.87 0.626 0.459
#> Block (eliminating Treatments) 2 69.5 34.75 1.288 0.342
#> Residuals 6 161.8 26.97
#>
#> Coefficient of Variation
#> ========================
#> 6.372367
#>
#> Overall Adjusted Mean
#> =====================
#> 81.0625
#>
#> Standard Errors
#> ===============
#> Std. Error of Diff. CD (5%)
#> Control Treatment Means 4.240458 10.37603
#> Two Test Treatments (Same Block) 7.344688 17.97180
#> Two Test Treatments (Different Blocks) 8.211611 20.09309
#> A Test Treatment and a Control Treatment 6.704752 16.40594
#>
#> Treatment Means
#> ===============
#> Treatment Block Means SE r Min Max Adjusted Means
#> 1 84.67 3.84 3 79.00 92.00 84.67
#> 10 3 74.00 <NA> 1 74.00 74.00 77.25
#> 11 1 89.00 <NA> 1 89.00 89.00 86.50
#> 12 1 82.00 <NA> 1 82.00 82.00 79.50
#> 2 79.00 1.15 3 77.00 81.00 79.00
#> 3 82.00 2.65 3 78.00 87.00 82.00
#> 4 83.33 3.93 3 78.00 91.00 83.33
#> 5 2 79.00 <NA> 1 79.00 79.00 78.25
#> 6 3 75.00 <NA> 1 75.00 75.00 78.25
#> 7 1 96.00 <NA> 1 96.00 96.00 93.50
#> 8 3 70.00 <NA> 1 70.00 70.00 73.25
#> 9 2 78.00 <NA> 1 78.00 78.00 77.25
#>
#>
#> Comparisons
#> ===========
#>
#> Method : lsd
#>
#> contrast estimate SE df t.ratio p.value sig
#> treatment1 - treatment2 5.67 4.24 6 1.336 0.230
#> treatment1 - treatment3 2.67 4.24 6 0.629 0.553
#> treatment1 - treatment4 1.33 4.24 6 0.314 0.764
#> treatment1 - treatment5 6.42 6.36 6 1.009 0.352
#> treatment1 - treatment6 6.42 6.36 6 1.009 0.352
#> treatment1 - treatment7 -8.83 6.36 6 -1.389 0.214
#> treatment1 - treatment8 11.42 6.36 6 1.795 0.123
#> treatment1 - treatment9 7.42 6.36 6 1.166 0.288
#> treatment1 - treatment10 7.42 6.36 6 1.166 0.288
#> treatment1 - treatment11 -1.83 6.36 6 -0.288 0.783
#> treatment1 - treatment12 5.17 6.36 6 0.812 0.448
#> treatment2 - treatment3 -3.00 4.24 6 -0.707 0.506
#> treatment2 - treatment4 -4.33 4.24 6 -1.022 0.346
#> treatment2 - treatment5 0.75 6.36 6 0.118 0.910
#> treatment2 - treatment6 0.75 6.36 6 0.118 0.910
#> treatment2 - treatment7 -14.50 6.36 6 -2.280 0.063
#> treatment2 - treatment8 5.75 6.36 6 0.904 0.401
#> treatment2 - treatment9 1.75 6.36 6 0.275 0.792
#> treatment2 - treatment10 1.75 6.36 6 0.275 0.792
#> treatment2 - treatment11 -7.50 6.36 6 -1.179 0.283
#> treatment2 - treatment12 -0.50 6.36 6 -0.079 0.940
#> treatment3 - treatment4 -1.33 4.24 6 -0.314 0.764
#> treatment3 - treatment5 3.75 6.36 6 0.590 0.577
#> treatment3 - treatment6 3.75 6.36 6 0.590 0.577
#> treatment3 - treatment7 -11.50 6.36 6 -1.808 0.121
#> treatment3 - treatment8 8.75 6.36 6 1.376 0.218
#> treatment3 - treatment9 4.75 6.36 6 0.747 0.483
#> treatment3 - treatment10 4.75 6.36 6 0.747 0.483
#> treatment3 - treatment11 -4.50 6.36 6 -0.707 0.506
#> treatment3 - treatment12 2.50 6.36 6 0.393 0.708
#> treatment4 - treatment5 5.08 6.36 6 0.799 0.455
#> treatment4 - treatment6 5.08 6.36 6 0.799 0.455
#> treatment4 - treatment7 -10.17 6.36 6 -1.598 0.161
#> treatment4 - treatment8 10.08 6.36 6 1.585 0.164
#> treatment4 - treatment9 6.08 6.36 6 0.956 0.376
#> treatment4 - treatment10 6.08 6.36 6 0.956 0.376
#> treatment4 - treatment11 -3.17 6.36 6 -0.498 0.636
#> treatment4 - treatment12 3.83 6.36 6 0.603 0.569
#> treatment5 - treatment6 0.00 8.21 6 0.000 1.000
#> treatment5 - treatment7 -15.25 8.21 6 -1.857 0.113
#> treatment5 - treatment8 5.00 8.21 6 0.609 0.565
#> treatment5 - treatment9 1.00 7.34 6 0.136 0.896
#> treatment5 - treatment10 1.00 8.21 6 0.122 0.907
#> treatment5 - treatment11 -8.25 8.21 6 -1.005 0.354
#> treatment5 - treatment12 -1.25 8.21 6 -0.152 0.884
#> treatment6 - treatment7 -15.25 8.21 6 -1.857 0.113
#> treatment6 - treatment8 5.00 7.34 6 0.681 0.521
#> treatment6 - treatment9 1.00 8.21 6 0.122 0.907
#> treatment6 - treatment10 1.00 7.34 6 0.136 0.896
#> treatment6 - treatment11 -8.25 8.21 6 -1.005 0.354
#> treatment6 - treatment12 -1.25 8.21 6 -0.152 0.884
#> treatment7 - treatment8 20.25 8.21 6 2.466 0.049 *
#> treatment7 - treatment9 16.25 8.21 6 1.979 0.095
#> treatment7 - treatment10 16.25 8.21 6 1.979 0.095
#> treatment7 - treatment11 7.00 7.34 6 0.953 0.377
#> treatment7 - treatment12 14.00 7.34 6 1.906 0.105
#> treatment8 - treatment9 -4.00 8.21 6 -0.487 0.643
#> treatment8 - treatment10 -4.00 7.34 6 -0.545 0.606
#> treatment8 - treatment11 -13.25 8.21 6 -1.614 0.158
#> treatment8 - treatment12 -6.25 8.21 6 -0.761 0.475
#> treatment9 - treatment10 -0.00 8.21 6 -0.000 1.000
#> treatment9 - treatment11 -9.25 8.21 6 -1.126 0.303
#> treatment9 - treatment12 -2.25 8.21 6 -0.274 0.793
#> treatment10 - treatment11 -9.25 8.21 6 -1.126 0.303
#> treatment10 - treatment12 -2.25 8.21 6 -0.274 0.793
#> treatment11 - treatment12 7.00 7.34 6 0.953 0.377
#>
#> Treatment Groups
#> ================
#>
#> Method : lsd
#>
#> Treatment Adjusted Means SE df lower.CL upper.CL Group
#> 8 73.25 5.61 6 59.52 86.98 1
#> 9 77.25 5.61 6 63.52 90.98 12
#> 10 77.25 5.61 6 63.52 90.98 12
#> 5 78.25 5.61 6 64.52 91.98 12
#> 6 78.25 5.61 6 64.52 91.98 12
#> 2 79.00 3.00 6 71.66 86.34 12
#> 12 79.50 5.61 6 65.77 93.23 12
#> 3 82.00 3.00 6 74.66 89.34 12
#> 4 83.33 3.00 6 76.00 90.67 12
#> 1 84.67 3.00 6 77.33 92.00 12
#> 11 86.50 5.61 6 72.77 100.23 12
#> 7 93.50 5.61 6 79.77 107.23 2
# Results for variable y2
out2 <- augmentedRCBD(data$blk, data$trt, data$y1, method.comp = "lsd",
alpha = 0.05, group = TRUE, console = TRUE)
#>
#> Augmented Design Details
#> ========================
#>
#> Number of blocks "3"
#> Number of treatments "12"
#> Number of check treatments "4"
#> Number of test treatments "8"
#> Check treatments "1, 2, 3, 4"
#>
#>
#> ANOVA, Treatment Adjusted
#> =========================
#> Df Sum Sq Mean Sq F value Pr(>F)
#> Block (ignoring Treatments) 2 360.1 180.04 6.675 0.0298 *
#> Treatment (eliminating Blocks) 11 285.1 25.92 0.961 0.5499
#> Treatment: Check 3 52.9 17.64 0.654 0.6092
#> Treatment: Test and Test vs. Check 8 232.2 29.02 1.076 0.4779
#> Residuals 6 161.8 26.97
#> ---
#> Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1
#>
#> ANOVA, Block Adjusted
#> =====================
#> Df Sum Sq Mean Sq F value Pr(>F)
#> Treatment (ignoring Blocks) 11 575.7 52.33 1.940 0.215
#> Treatment: Check 3 52.9 17.64 0.654 0.609
#> Treatment: Test 7 505.9 72.27 2.679 0.125
#> Treatment: Test vs. Check 1 16.9 16.87 0.626 0.459
#> Block (eliminating Treatments) 2 69.5 34.75 1.288 0.342
#> Residuals 6 161.8 26.97
#>
#> Coefficient of Variation
#> ========================
#> 6.372367
#>
#> Overall Adjusted Mean
#> =====================
#> 81.0625
#>
#> Standard Errors
#> ===============
#> Std. Error of Diff. CD (5%)
#> Control Treatment Means 4.240458 10.37603
#> Two Test Treatments (Same Block) 7.344688 17.97180
#> Two Test Treatments (Different Blocks) 8.211611 20.09309
#> A Test Treatment and a Control Treatment 6.704752 16.40594
#>
#> Treatment Means
#> ===============
#> Treatment Block Means SE r Min Max Adjusted Means
#> 1 84.67 3.84 3 79.00 92.00 84.67
#> 10 3 74.00 <NA> 1 74.00 74.00 77.25
#> 11 1 89.00 <NA> 1 89.00 89.00 86.50
#> 12 1 82.00 <NA> 1 82.00 82.00 79.50
#> 2 79.00 1.15 3 77.00 81.00 79.00
#> 3 82.00 2.65 3 78.00 87.00 82.00
#> 4 83.33 3.93 3 78.00 91.00 83.33
#> 5 2 79.00 <NA> 1 79.00 79.00 78.25
#> 6 3 75.00 <NA> 1 75.00 75.00 78.25
#> 7 1 96.00 <NA> 1 96.00 96.00 93.50
#> 8 3 70.00 <NA> 1 70.00 70.00 73.25
#> 9 2 78.00 <NA> 1 78.00 78.00 77.25
#>
#>
#> Comparisons
#> ===========
#>
#> Method : lsd
#>
#> contrast estimate SE df t.ratio p.value sig
#> treatment1 - treatment2 5.67 4.24 6 1.336 0.230
#> treatment1 - treatment3 2.67 4.24 6 0.629 0.553
#> treatment1 - treatment4 1.33 4.24 6 0.314 0.764
#> treatment1 - treatment5 6.42 6.36 6 1.009 0.352
#> treatment1 - treatment6 6.42 6.36 6 1.009 0.352
#> treatment1 - treatment7 -8.83 6.36 6 -1.389 0.214
#> treatment1 - treatment8 11.42 6.36 6 1.795 0.123
#> treatment1 - treatment9 7.42 6.36 6 1.166 0.288
#> treatment1 - treatment10 7.42 6.36 6 1.166 0.288
#> treatment1 - treatment11 -1.83 6.36 6 -0.288 0.783
#> treatment1 - treatment12 5.17 6.36 6 0.812 0.448
#> treatment2 - treatment3 -3.00 4.24 6 -0.707 0.506
#> treatment2 - treatment4 -4.33 4.24 6 -1.022 0.346
#> treatment2 - treatment5 0.75 6.36 6 0.118 0.910
#> treatment2 - treatment6 0.75 6.36 6 0.118 0.910
#> treatment2 - treatment7 -14.50 6.36 6 -2.280 0.063
#> treatment2 - treatment8 5.75 6.36 6 0.904 0.401
#> treatment2 - treatment9 1.75 6.36 6 0.275 0.792
#> treatment2 - treatment10 1.75 6.36 6 0.275 0.792
#> treatment2 - treatment11 -7.50 6.36 6 -1.179 0.283
#> treatment2 - treatment12 -0.50 6.36 6 -0.079 0.940
#> treatment3 - treatment4 -1.33 4.24 6 -0.314 0.764
#> treatment3 - treatment5 3.75 6.36 6 0.590 0.577
#> treatment3 - treatment6 3.75 6.36 6 0.590 0.577
#> treatment3 - treatment7 -11.50 6.36 6 -1.808 0.121
#> treatment3 - treatment8 8.75 6.36 6 1.376 0.218
#> treatment3 - treatment9 4.75 6.36 6 0.747 0.483
#> treatment3 - treatment10 4.75 6.36 6 0.747 0.483
#> treatment3 - treatment11 -4.50 6.36 6 -0.707 0.506
#> treatment3 - treatment12 2.50 6.36 6 0.393 0.708
#> treatment4 - treatment5 5.08 6.36 6 0.799 0.455
#> treatment4 - treatment6 5.08 6.36 6 0.799 0.455
#> treatment4 - treatment7 -10.17 6.36 6 -1.598 0.161
#> treatment4 - treatment8 10.08 6.36 6 1.585 0.164
#> treatment4 - treatment9 6.08 6.36 6 0.956 0.376
#> treatment4 - treatment10 6.08 6.36 6 0.956 0.376
#> treatment4 - treatment11 -3.17 6.36 6 -0.498 0.636
#> treatment4 - treatment12 3.83 6.36 6 0.603 0.569
#> treatment5 - treatment6 0.00 8.21 6 0.000 1.000
#> treatment5 - treatment7 -15.25 8.21 6 -1.857 0.113
#> treatment5 - treatment8 5.00 8.21 6 0.609 0.565
#> treatment5 - treatment9 1.00 7.34 6 0.136 0.896
#> treatment5 - treatment10 1.00 8.21 6 0.122 0.907
#> treatment5 - treatment11 -8.25 8.21 6 -1.005 0.354
#> treatment5 - treatment12 -1.25 8.21 6 -0.152 0.884
#> treatment6 - treatment7 -15.25 8.21 6 -1.857 0.113
#> treatment6 - treatment8 5.00 7.34 6 0.681 0.521
#> treatment6 - treatment9 1.00 8.21 6 0.122 0.907
#> treatment6 - treatment10 1.00 7.34 6 0.136 0.896
#> treatment6 - treatment11 -8.25 8.21 6 -1.005 0.354
#> treatment6 - treatment12 -1.25 8.21 6 -0.152 0.884
#> treatment7 - treatment8 20.25 8.21 6 2.466 0.049 *
#> treatment7 - treatment9 16.25 8.21 6 1.979 0.095
#> treatment7 - treatment10 16.25 8.21 6 1.979 0.095
#> treatment7 - treatment11 7.00 7.34 6 0.953 0.377
#> treatment7 - treatment12 14.00 7.34 6 1.906 0.105
#> treatment8 - treatment9 -4.00 8.21 6 -0.487 0.643
#> treatment8 - treatment10 -4.00 7.34 6 -0.545 0.606
#> treatment8 - treatment11 -13.25 8.21 6 -1.614 0.158
#> treatment8 - treatment12 -6.25 8.21 6 -0.761 0.475
#> treatment9 - treatment10 -0.00 8.21 6 -0.000 1.000
#> treatment9 - treatment11 -9.25 8.21 6 -1.126 0.303
#> treatment9 - treatment12 -2.25 8.21 6 -0.274 0.793
#> treatment10 - treatment11 -9.25 8.21 6 -1.126 0.303
#> treatment10 - treatment12 -2.25 8.21 6 -0.274 0.793
#> treatment11 - treatment12 7.00 7.34 6 0.953 0.377
#>
#> Treatment Groups
#> ================
#>
#> Method : lsd
#>
#> Treatment Adjusted Means SE df lower.CL upper.CL Group
#> 8 73.25 5.61 6 59.52 86.98 1
#> 9 77.25 5.61 6 63.52 90.98 12
#> 10 77.25 5.61 6 63.52 90.98 12
#> 5 78.25 5.61 6 64.52 91.98 12
#> 6 78.25 5.61 6 64.52 91.98 12
#> 2 79.00 3.00 6 71.66 86.34 12
#> 12 79.50 5.61 6 65.77 93.23 12
#> 3 82.00 3.00 6 74.66 89.34 12
#> 4 83.33 3.00 6 76.00 90.67 12
#> 1 84.67 3.00 6 77.33 92.00 12
#> 11 86.50 5.61 6 72.77 100.23 12
#> 7 93.50 5.61 6 79.77 107.23 2
# Make cluster
library(parallel)
ncores <- max(2, parallel::detectCores() - 2)
# Pairwise t test without p value adjustment
cl <- makeCluster(getOption("cl.cores", ncores))
pout1 <- pairwise.augmentedRCBD(out1, cl = cl,
p.adjust = "none")
stopCluster(cl)
cl <- makeCluster(getOption("cl.cores", ncores))
pout2 <- pairwise.augmentedRCBD(out1, cl = cl,
p.adjust = "none")
stopCluster(cl)
# Pairwise t test with tukey adjustment
cl <- makeCluster(getOption("cl.cores", ncores))
pout1_tukey <- pairwise.augmentedRCBD(out1, cl = cl,
p.adjust = "tukey")
stopCluster(cl)
cl <- makeCluster(getOption("cl.cores", ncores))
pout2_tukey <- pairwise.augmentedRCBD(out1, cl = cl,
p.adjust = "tukey")
stopCluster(cl)
# Pairwise t test with sidak p value adjustment
cl <- makeCluster(getOption("cl.cores", ncores))
pout1_sidak <- pairwise.augmentedRCBD(out1, cl = cl,
p.adjust = "sidak")
stopCluster(cl)
cl <- makeCluster(getOption("cl.cores", ncores))
pout2_sidak <- pairwise.augmentedRCBD(out1, cl = cl,
p.adjust = "sidak")
stopCluster(cl)