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,
alpha = NULL,
p.adjust = c("none", "tukey", "sidak")
)Arguments
- aug
An object of class
augmentedRCBD.- cl
A cluster object created by
makeClusterfor parallel evaluations.- alpha
Type I error probability (Significance level).
- 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)
# Check if running under R CMD check and adjust cores accordingly
if (nzchar(Sys.getenv("_R_CHECK_LIMIT_CORES_"))) {
ncores <- 2
} else {
ncores <- max(2L, parallel::detectCores() - 4)
}
cl <- makeCluster(getOption("cl.cores", ncores))
# Pairwise t test without p value adjustment ----
pout1 <- pairwise.augmentedRCBD(out1, cl = cl,
p.adjust = "none")
pout1
#> contrast estimate SE df t.ratio p.value sig
#> 1 1 - (10) 7.416667 6.704752 6 1.10618056 1.68898284
#> 2 1 - (11) -1.833333 6.704752 6 -0.27343789 0.79368531
#> 3 1 - (12) 5.166667 6.704752 6 0.77059769 1.52980915
#> 4 1 - 2 5.666667 4.240458 6 1.33633373 1.77011499
#> 5 1 - 3 2.666667 4.240458 6 0.62886293 1.44738825
#> 6 1 - 4 1.333333 4.240458 6 0.31443147 1.23616021
#> 7 1 - (5) 6.416667 6.704752 6 0.95703262 1.62449310
#> 8 1 - (6) 6.416667 6.704752 6 0.95703262 1.62449310
#> 9 1 - (7) -8.833333 6.704752 6 -1.31747347 0.23575127
#> 10 1 - (8) 11.416667 6.704752 6 1.70277232 1.86049532
#> 11 1 - (9) 7.416667 6.704752 6 1.10618056 1.68898284
#> 12 (10) - (11) -9.250000 8.211611 6 -1.12645375 0.30300033
#> 13 (10) - (12) -2.250000 8.211611 6 -0.27400226 0.79327167
#> 14 (10) - 2 -1.750000 6.704752 6 -0.26100890 0.80281330
#> 15 (10) - 3 -4.750000 6.704752 6 -0.70845272 0.50524328
#> 16 (10) - 4 -6.083333 6.704752 6 -0.90731664 0.39921196
#> 17 (10) - (5) -1.000000 8.211611 6 -0.12177878 0.90705045
#> 18 (10) - (6) -1.000000 7.344688 6 -0.13615282 0.89615381
#> 19 (10) - (7) -16.250000 8.211611 6 -1.97890523 0.09516823
#> 20 (10) - (8) 4.000000 7.344688 6 0.54461128 1.39434533
#> 21 (10) - (9) 0.000000 8.211611 6 0.00000000 1.00000000
#> 22 (11) - (12) 7.000000 7.344688 6 0.95306973 1.62264457
#> 23 (11) - 2 7.500000 6.704752 6 1.11860955 1.69391854
#> 24 (11) - 3 4.500000 6.704752 6 0.67116573 1.47290592
#> 25 (11) - 4 3.166667 6.704752 6 0.47230181 1.34659999
#> 26 (11) - (5) 8.250000 8.211611 6 1.00467496 1.64616381
#> 27 (11) - (6) 8.250000 8.211611 6 1.00467496 1.64616381
#> 28 (11) - (7) -7.000000 7.344688 6 -0.95306973 0.37735543
#> 29 (11) - (8) 13.250000 8.211611 6 1.61356888 1.84225214
#> 30 (11) - (9) 9.250000 8.211611 6 1.12645375 1.69699967
#> 31 (12) - 2 0.500000 6.704752 6 0.07457397 1.05702215
#> 32 (12) - 3 -2.500000 6.704752 6 -0.37286985 0.72206273
#> 33 (12) - 4 -3.833333 6.704752 6 -0.57173377 0.58826470
#> 34 (12) - (5) 1.250000 8.211611 6 0.15222348 1.11599954
#> 35 (12) - (6) 1.250000 8.211611 6 0.15222348 1.11599954
#> 36 (12) - (7) -14.000000 7.344688 6 -1.90613947 0.10526990
#> 37 (12) - (8) 6.250000 8.211611 6 0.76111740 1.52457343
#> 38 (12) - (9) 2.250000 8.211611 6 0.27400226 1.20672833
#> 39 2 - 3 -3.000000 4.240458 6 -0.70747080 0.50581089
#> 40 2 - 4 -4.333333 4.240458 6 -1.02190227 0.34624985
#> 41 2 - (5) 0.750000 6.704752 6 0.11186096 1.08541796
#> 42 2 - (6) 0.750000 6.704752 6 0.11186096 1.08541796
#> 43 2 - (7) -14.500000 6.704752 6 -2.16264514 0.07380701
#> 44 2 - (8) 5.750000 6.704752 6 0.85760066 1.57596055
#> 45 2 - (9) 1.750000 6.704752 6 0.26100890 1.19718670
#> 46 3 - 4 -1.333333 4.240458 6 -0.31443147 0.76383979
#> 47 3 - (5) 3.750000 6.704752 6 0.55930478 1.40380253
#> 48 3 - (6) 3.750000 6.704752 6 0.55930478 1.40380253
#> 49 3 - (7) -11.500000 6.704752 6 -1.71520132 0.13713052
#> 50 3 - (8) 8.750000 6.704752 6 1.30504448 1.76031005
#> 51 3 - (9) 4.750000 6.704752 6 0.70845272 1.49475672
#> 52 4 - (5) 5.083333 6.704752 6 0.75816870 1.52293671
#> 53 4 - (6) 5.083333 6.704752 6 0.75816870 1.52293671
#> 54 4 - (7) -10.166667 6.704752 6 -1.51633739 0.18022066
#> 55 4 - (8) 10.083333 6.704752 6 1.50390840 1.81669827
#> 56 4 - (9) 6.083333 6.704752 6 0.90731664 1.60078804
#> 57 (5) - (6) 0.000000 8.211611 6 0.00000000 1.00000000
#> 58 (5) - (7) -15.250000 8.211611 6 -1.85712645 0.11267166
#> 59 (5) - (8) 5.000000 8.211611 6 0.60889392 1.43507937
#> 60 (5) - (9) 1.000000 7.344688 6 0.13615282 1.10384619
#> 61 (6) - (7) -15.250000 8.211611 6 -1.85712645 0.11267166
#> 62 (6) - (8) 5.000000 7.344688 6 0.68076409 1.47858869
#> 63 (6) - (9) 1.000000 8.211611 6 0.12177878 1.09294955
#> 64 (7) - (8) 20.250000 8.211611 6 2.46602036 1.95128006
#> 65 (7) - (9) 16.250000 8.211611 6 1.97890523 1.90483177
#> 66 (8) - (9) -4.000000 8.211611 6 -0.48711513 0.64346045
stopCluster(cl)
cl <- makeCluster(getOption("cl.cores", ncores))
pout2 <- pairwise.augmentedRCBD(out2, cl = cl,
p.adjust = "none")
pout2
#> contrast estimate SE df t.ratio p.value sig
#> 1 1 - (10) 7.416667 6.704752 6 1.10618056 1.68898284
#> 2 1 - (11) -1.833333 6.704752 6 -0.27343789 0.79368531
#> 3 1 - (12) 5.166667 6.704752 6 0.77059769 1.52980915
#> 4 1 - 2 5.666667 4.240458 6 1.33633373 1.77011499
#> 5 1 - 3 2.666667 4.240458 6 0.62886293 1.44738825
#> 6 1 - 4 1.333333 4.240458 6 0.31443147 1.23616021
#> 7 1 - (5) 6.416667 6.704752 6 0.95703262 1.62449310
#> 8 1 - (6) 6.416667 6.704752 6 0.95703262 1.62449310
#> 9 1 - (7) -8.833333 6.704752 6 -1.31747347 0.23575127
#> 10 1 - (8) 11.416667 6.704752 6 1.70277232 1.86049532
#> 11 1 - (9) 7.416667 6.704752 6 1.10618056 1.68898284
#> 12 (10) - (11) -9.250000 8.211611 6 -1.12645375 0.30300033
#> 13 (10) - (12) -2.250000 8.211611 6 -0.27400226 0.79327167
#> 14 (10) - 2 -1.750000 6.704752 6 -0.26100890 0.80281330
#> 15 (10) - 3 -4.750000 6.704752 6 -0.70845272 0.50524328
#> 16 (10) - 4 -6.083333 6.704752 6 -0.90731664 0.39921196
#> 17 (10) - (5) -1.000000 8.211611 6 -0.12177878 0.90705045
#> 18 (10) - (6) -1.000000 7.344688 6 -0.13615282 0.89615381
#> 19 (10) - (7) -16.250000 8.211611 6 -1.97890523 0.09516823
#> 20 (10) - (8) 4.000000 7.344688 6 0.54461128 1.39434533
#> 21 (10) - (9) 0.000000 8.211611 6 0.00000000 1.00000000
#> 22 (11) - (12) 7.000000 7.344688 6 0.95306973 1.62264457
#> 23 (11) - 2 7.500000 6.704752 6 1.11860955 1.69391854
#> 24 (11) - 3 4.500000 6.704752 6 0.67116573 1.47290592
#> 25 (11) - 4 3.166667 6.704752 6 0.47230181 1.34659999
#> 26 (11) - (5) 8.250000 8.211611 6 1.00467496 1.64616381
#> 27 (11) - (6) 8.250000 8.211611 6 1.00467496 1.64616381
#> 28 (11) - (7) -7.000000 7.344688 6 -0.95306973 0.37735543
#> 29 (11) - (8) 13.250000 8.211611 6 1.61356888 1.84225214
#> 30 (11) - (9) 9.250000 8.211611 6 1.12645375 1.69699967
#> 31 (12) - 2 0.500000 6.704752 6 0.07457397 1.05702215
#> 32 (12) - 3 -2.500000 6.704752 6 -0.37286985 0.72206273
#> 33 (12) - 4 -3.833333 6.704752 6 -0.57173377 0.58826470
#> 34 (12) - (5) 1.250000 8.211611 6 0.15222348 1.11599954
#> 35 (12) - (6) 1.250000 8.211611 6 0.15222348 1.11599954
#> 36 (12) - (7) -14.000000 7.344688 6 -1.90613947 0.10526990
#> 37 (12) - (8) 6.250000 8.211611 6 0.76111740 1.52457343
#> 38 (12) - (9) 2.250000 8.211611 6 0.27400226 1.20672833
#> 39 2 - 3 -3.000000 4.240458 6 -0.70747080 0.50581089
#> 40 2 - 4 -4.333333 4.240458 6 -1.02190227 0.34624985
#> 41 2 - (5) 0.750000 6.704752 6 0.11186096 1.08541796
#> 42 2 - (6) 0.750000 6.704752 6 0.11186096 1.08541796
#> 43 2 - (7) -14.500000 6.704752 6 -2.16264514 0.07380701
#> 44 2 - (8) 5.750000 6.704752 6 0.85760066 1.57596055
#> 45 2 - (9) 1.750000 6.704752 6 0.26100890 1.19718670
#> 46 3 - 4 -1.333333 4.240458 6 -0.31443147 0.76383979
#> 47 3 - (5) 3.750000 6.704752 6 0.55930478 1.40380253
#> 48 3 - (6) 3.750000 6.704752 6 0.55930478 1.40380253
#> 49 3 - (7) -11.500000 6.704752 6 -1.71520132 0.13713052
#> 50 3 - (8) 8.750000 6.704752 6 1.30504448 1.76031005
#> 51 3 - (9) 4.750000 6.704752 6 0.70845272 1.49475672
#> 52 4 - (5) 5.083333 6.704752 6 0.75816870 1.52293671
#> 53 4 - (6) 5.083333 6.704752 6 0.75816870 1.52293671
#> 54 4 - (7) -10.166667 6.704752 6 -1.51633739 0.18022066
#> 55 4 - (8) 10.083333 6.704752 6 1.50390840 1.81669827
#> 56 4 - (9) 6.083333 6.704752 6 0.90731664 1.60078804
#> 57 (5) - (6) 0.000000 8.211611 6 0.00000000 1.00000000
#> 58 (5) - (7) -15.250000 8.211611 6 -1.85712645 0.11267166
#> 59 (5) - (8) 5.000000 8.211611 6 0.60889392 1.43507937
#> 60 (5) - (9) 1.000000 7.344688 6 0.13615282 1.10384619
#> 61 (6) - (7) -15.250000 8.211611 6 -1.85712645 0.11267166
#> 62 (6) - (8) 5.000000 7.344688 6 0.68076409 1.47858869
#> 63 (6) - (9) 1.000000 8.211611 6 0.12177878 1.09294955
#> 64 (7) - (8) 20.250000 8.211611 6 2.46602036 1.95128006
#> 65 (7) - (9) 16.250000 8.211611 6 1.97890523 1.90483177
#> 66 (8) - (9) -4.000000 8.211611 6 -0.48711513 0.64346045
stopCluster(cl)
# Pairwise t test with tukey adjustment ----
cl <- makeCluster(getOption("cl.cores", ncores))
pout1_tukey <- pairwise.augmentedRCBD(out1, cl = cl,
p.adjust = "tukey")
pout1_tukey
#> contrast estimate SE df t.ratio p.value sig
#> 1 1 - (10) 7.416667 6.704752 6 1.10618056 0.9985579
#> 2 1 - (11) -1.833333 6.704752 6 -0.27343789 1.0000000
#> 3 1 - (12) 5.166667 6.704752 6 0.77059769 0.9999418
#> 4 1 - 2 5.666667 4.240458 6 1.33633373 0.9937163
#> 5 1 - 3 2.666667 4.240458 6 0.62886293 0.9999919
#> 6 1 - 4 1.333333 4.240458 6 0.31443147 1.0000000
#> 7 1 - (5) 6.416667 6.704752 6 0.95703262 0.9995791
#> 8 1 - (6) 6.416667 6.704752 6 0.95703262 0.9995791
#> 9 1 - (7) -8.833333 6.704752 6 -1.31747347 1.0000000
#> 10 1 - (8) 11.416667 6.704752 6 1.70277232 0.9682124
#> 11 1 - (9) 7.416667 6.704752 6 1.10618056 0.9985579
#> 12 (10) - (11) -9.250000 8.211611 6 -1.12645375 1.0000000
#> 13 (10) - (12) -2.250000 8.211611 6 -0.27400226 1.0000000
#> 14 (10) - 2 -1.750000 6.704752 6 -0.26100890 1.0000000
#> 15 (10) - 3 -4.750000 6.704752 6 -0.70845272 1.0000000
#> 16 (10) - 4 -6.083333 6.704752 6 -0.90731664 1.0000000
#> 17 (10) - (5) -1.000000 8.211611 6 -0.12177878 1.0000000
#> 18 (10) - (6) -1.000000 7.344688 6 -0.13615282 1.0000000
#> 19 (10) - (7) -16.250000 8.211611 6 -1.97890523 1.0000000
#> 20 (10) - (8) 4.000000 7.344688 6 0.54461128 0.9999981
#> 21 (10) - (9) 0.000000 8.211611 6 0.00000000 1.0000000
#> 22 (11) - (12) 7.000000 7.344688 6 0.95306973 0.9995942
#> 23 (11) - 2 7.500000 6.704752 6 1.11860955 0.9984198
#> 24 (11) - 3 4.500000 6.704752 6 0.67116573 0.9999846
#> 25 (11) - 4 3.166667 6.704752 6 0.47230181 0.9999996
#> 26 (11) - (5) 8.250000 8.211611 6 1.00467496 0.9993581
#> 27 (11) - (6) 8.250000 8.211611 6 1.00467496 0.9993581
#> 28 (11) - (7) -7.000000 7.344688 6 -0.95306973 1.0000000
#> 29 (11) - (8) 13.250000 8.211611 6 1.61356888 0.9771900
#> 30 (11) - (9) 9.250000 8.211611 6 1.12645375 0.9983271
#> 31 (12) - 2 0.500000 6.704752 6 0.07457397 1.0000000
#> 32 (12) - 3 -2.500000 6.704752 6 -0.37286985 1.0000000
#> 33 (12) - 4 -3.833333 6.704752 6 -0.57173377 1.0000000
#> 34 (12) - (5) 1.250000 8.211611 6 0.15222348 1.0000000
#> 35 (12) - (6) 1.250000 8.211611 6 0.15222348 1.0000000
#> 36 (12) - (7) -14.000000 7.344688 6 -1.90613947 1.0000000
#> 37 (12) - (8) 6.250000 8.211611 6 0.76111740 0.9999482
#> 38 (12) - (9) 2.250000 8.211611 6 0.27400226 1.0000000
#> 39 2 - 3 -3.000000 4.240458 6 -0.70747080 1.0000000
#> 40 2 - 4 -4.333333 4.240458 6 -1.02190227 1.0000000
#> 41 2 - (5) 0.750000 6.704752 6 0.11186096 1.0000000
#> 42 2 - (6) 0.750000 6.704752 6 0.11186096 1.0000000
#> 43 2 - (7) -14.500000 6.704752 6 -2.16264514 1.0000000
#> 44 2 - (8) 5.750000 6.704752 6 0.85760066 0.9998426
#> 45 2 - (9) 1.750000 6.704752 6 0.26100890 1.0000000
#> 46 3 - 4 -1.333333 4.240458 6 -0.31443147 1.0000000
#> 47 3 - (5) 3.750000 6.704752 6 0.55930478 0.9999975
#> 48 3 - (6) 3.750000 6.704752 6 0.55930478 0.9999975
#> 49 3 - (7) -11.500000 6.704752 6 -1.71520132 1.0000000
#> 50 3 - (8) 8.750000 6.704752 6 1.30504448 0.9947262
#> 51 3 - (9) 4.750000 6.704752 6 0.70845272 0.9999740
#> 52 4 - (5) 5.083333 6.704752 6 0.75816870 0.9999501
#> 53 4 - (6) 5.083333 6.704752 6 0.75816870 0.9999501
#> 54 4 - (7) -10.166667 6.704752 6 -1.51633739 1.0000000
#> 55 4 - (8) 10.083333 6.704752 6 1.50390840 0.9855836
#> 56 4 - (9) 6.083333 6.704752 6 0.90731664 0.9997378
#> 57 (5) - (6) 0.000000 8.211611 6 0.00000000 1.0000000
#> 58 (5) - (7) -15.250000 8.211611 6 -1.85712645 1.0000000
#> 59 (5) - (8) 5.000000 8.211611 6 0.60889392 0.9999941
#> 60 (5) - (9) 1.000000 7.344688 6 0.13615282 1.0000000
#> 61 (6) - (7) -15.250000 8.211611 6 -1.85712645 1.0000000
#> 62 (6) - (8) 5.000000 7.344688 6 0.68076409 0.9999823
#> 63 (6) - (9) 1.000000 8.211611 6 0.12177878 1.0000000
#> 64 (7) - (8) 20.250000 8.211611 6 2.46602036 0.8043427
#> 65 (7) - (9) 16.250000 8.211611 6 1.97890523 0.9265988
#> 66 (8) - (9) -4.000000 8.211611 6 -0.48711513 1.0000000
stopCluster(cl)
cl <- makeCluster(getOption("cl.cores", ncores))
pout2_tukey <- pairwise.augmentedRCBD(out2, cl = cl,
p.adjust = "tukey")
pout2_tukey
#> contrast estimate SE df t.ratio p.value sig
#> 1 1 - (10) 7.416667 6.704752 6 1.10618056 0.9985579
#> 2 1 - (11) -1.833333 6.704752 6 -0.27343789 1.0000000
#> 3 1 - (12) 5.166667 6.704752 6 0.77059769 0.9999418
#> 4 1 - 2 5.666667 4.240458 6 1.33633373 0.9937163
#> 5 1 - 3 2.666667 4.240458 6 0.62886293 0.9999919
#> 6 1 - 4 1.333333 4.240458 6 0.31443147 1.0000000
#> 7 1 - (5) 6.416667 6.704752 6 0.95703262 0.9995791
#> 8 1 - (6) 6.416667 6.704752 6 0.95703262 0.9995791
#> 9 1 - (7) -8.833333 6.704752 6 -1.31747347 1.0000000
#> 10 1 - (8) 11.416667 6.704752 6 1.70277232 0.9682124
#> 11 1 - (9) 7.416667 6.704752 6 1.10618056 0.9985579
#> 12 (10) - (11) -9.250000 8.211611 6 -1.12645375 1.0000000
#> 13 (10) - (12) -2.250000 8.211611 6 -0.27400226 1.0000000
#> 14 (10) - 2 -1.750000 6.704752 6 -0.26100890 1.0000000
#> 15 (10) - 3 -4.750000 6.704752 6 -0.70845272 1.0000000
#> 16 (10) - 4 -6.083333 6.704752 6 -0.90731664 1.0000000
#> 17 (10) - (5) -1.000000 8.211611 6 -0.12177878 1.0000000
#> 18 (10) - (6) -1.000000 7.344688 6 -0.13615282 1.0000000
#> 19 (10) - (7) -16.250000 8.211611 6 -1.97890523 1.0000000
#> 20 (10) - (8) 4.000000 7.344688 6 0.54461128 0.9999981
#> 21 (10) - (9) 0.000000 8.211611 6 0.00000000 1.0000000
#> 22 (11) - (12) 7.000000 7.344688 6 0.95306973 0.9995942
#> 23 (11) - 2 7.500000 6.704752 6 1.11860955 0.9984198
#> 24 (11) - 3 4.500000 6.704752 6 0.67116573 0.9999846
#> 25 (11) - 4 3.166667 6.704752 6 0.47230181 0.9999996
#> 26 (11) - (5) 8.250000 8.211611 6 1.00467496 0.9993581
#> 27 (11) - (6) 8.250000 8.211611 6 1.00467496 0.9993581
#> 28 (11) - (7) -7.000000 7.344688 6 -0.95306973 1.0000000
#> 29 (11) - (8) 13.250000 8.211611 6 1.61356888 0.9771900
#> 30 (11) - (9) 9.250000 8.211611 6 1.12645375 0.9983271
#> 31 (12) - 2 0.500000 6.704752 6 0.07457397 1.0000000
#> 32 (12) - 3 -2.500000 6.704752 6 -0.37286985 1.0000000
#> 33 (12) - 4 -3.833333 6.704752 6 -0.57173377 1.0000000
#> 34 (12) - (5) 1.250000 8.211611 6 0.15222348 1.0000000
#> 35 (12) - (6) 1.250000 8.211611 6 0.15222348 1.0000000
#> 36 (12) - (7) -14.000000 7.344688 6 -1.90613947 1.0000000
#> 37 (12) - (8) 6.250000 8.211611 6 0.76111740 0.9999482
#> 38 (12) - (9) 2.250000 8.211611 6 0.27400226 1.0000000
#> 39 2 - 3 -3.000000 4.240458 6 -0.70747080 1.0000000
#> 40 2 - 4 -4.333333 4.240458 6 -1.02190227 1.0000000
#> 41 2 - (5) 0.750000 6.704752 6 0.11186096 1.0000000
#> 42 2 - (6) 0.750000 6.704752 6 0.11186096 1.0000000
#> 43 2 - (7) -14.500000 6.704752 6 -2.16264514 1.0000000
#> 44 2 - (8) 5.750000 6.704752 6 0.85760066 0.9998426
#> 45 2 - (9) 1.750000 6.704752 6 0.26100890 1.0000000
#> 46 3 - 4 -1.333333 4.240458 6 -0.31443147 1.0000000
#> 47 3 - (5) 3.750000 6.704752 6 0.55930478 0.9999975
#> 48 3 - (6) 3.750000 6.704752 6 0.55930478 0.9999975
#> 49 3 - (7) -11.500000 6.704752 6 -1.71520132 1.0000000
#> 50 3 - (8) 8.750000 6.704752 6 1.30504448 0.9947262
#> 51 3 - (9) 4.750000 6.704752 6 0.70845272 0.9999740
#> 52 4 - (5) 5.083333 6.704752 6 0.75816870 0.9999501
#> 53 4 - (6) 5.083333 6.704752 6 0.75816870 0.9999501
#> 54 4 - (7) -10.166667 6.704752 6 -1.51633739 1.0000000
#> 55 4 - (8) 10.083333 6.704752 6 1.50390840 0.9855836
#> 56 4 - (9) 6.083333 6.704752 6 0.90731664 0.9997378
#> 57 (5) - (6) 0.000000 8.211611 6 0.00000000 1.0000000
#> 58 (5) - (7) -15.250000 8.211611 6 -1.85712645 1.0000000
#> 59 (5) - (8) 5.000000 8.211611 6 0.60889392 0.9999941
#> 60 (5) - (9) 1.000000 7.344688 6 0.13615282 1.0000000
#> 61 (6) - (7) -15.250000 8.211611 6 -1.85712645 1.0000000
#> 62 (6) - (8) 5.000000 7.344688 6 0.68076409 0.9999823
#> 63 (6) - (9) 1.000000 8.211611 6 0.12177878 1.0000000
#> 64 (7) - (8) 20.250000 8.211611 6 2.46602036 0.8043427
#> 65 (7) - (9) 16.250000 8.211611 6 1.97890523 0.9265988
#> 66 (8) - (9) -4.000000 8.211611 6 -0.48711513 1.0000000
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")
pout1_sidak
#> contrast estimate SE df t.ratio p.value sig
#> 1 1 - (10) 7.416667 6.704752 6 1.10618056 1.0000000
#> 2 1 - (11) -1.833333 6.704752 6 -0.27343789 1.0000000
#> 3 1 - (12) 5.166667 6.704752 6 0.77059769 1.0000000
#> 4 1 - 2 5.666667 4.240458 6 1.33633373 1.0000000
#> 5 1 - 3 2.666667 4.240458 6 0.62886293 1.0000000
#> 6 1 - 4 1.333333 4.240458 6 0.31443147 1.0000000
#> 7 1 - (5) 6.416667 6.704752 6 0.95703262 1.0000000
#> 8 1 - (6) 6.416667 6.704752 6 0.95703262 1.0000000
#> 9 1 - (7) -8.833333 6.704752 6 -1.31747347 1.0000000
#> 10 1 - (8) 11.416667 6.704752 6 1.70277232 0.9999506
#> 11 1 - (9) 7.416667 6.704752 6 1.10618056 1.0000000
#> 12 (10) - (11) -9.250000 8.211611 6 -1.12645375 1.0000000
#> 13 (10) - (12) -2.250000 8.211611 6 -0.27400226 1.0000000
#> 14 (10) - 2 -1.750000 6.704752 6 -0.26100890 1.0000000
#> 15 (10) - 3 -4.750000 6.704752 6 -0.70845272 1.0000000
#> 16 (10) - 4 -6.083333 6.704752 6 -0.90731664 1.0000000
#> 17 (10) - (5) -1.000000 8.211611 6 -0.12177878 1.0000000
#> 18 (10) - (6) -1.000000 7.344688 6 -0.13615282 1.0000000
#> 19 (10) - (7) -16.250000 8.211611 6 -1.97890523 0.9986402
#> 20 (10) - (8) 4.000000 7.344688 6 0.54461128 1.0000000
#> 21 (10) - (9) 0.000000 8.211611 6 0.00000000 1.0000000
#> 22 (11) - (12) 7.000000 7.344688 6 0.95306973 1.0000000
#> 23 (11) - 2 7.500000 6.704752 6 1.11860955 1.0000000
#> 24 (11) - 3 4.500000 6.704752 6 0.67116573 1.0000000
#> 25 (11) - 4 3.166667 6.704752 6 0.47230181 1.0000000
#> 26 (11) - (5) 8.250000 8.211611 6 1.00467496 1.0000000
#> 27 (11) - (6) 8.250000 8.211611 6 1.00467496 1.0000000
#> 28 (11) - (7) -7.000000 7.344688 6 -0.95306973 1.0000000
#> 29 (11) - (8) 13.250000 8.211611 6 1.61356888 0.9999880
#> 30 (11) - (9) 9.250000 8.211611 6 1.12645375 1.0000000
#> 31 (12) - 2 0.500000 6.704752 6 0.07457397 1.0000000
#> 32 (12) - 3 -2.500000 6.704752 6 -0.37286985 1.0000000
#> 33 (12) - 4 -3.833333 6.704752 6 -0.57173377 1.0000000
#> 34 (12) - (5) 1.250000 8.211611 6 0.15222348 1.0000000
#> 35 (12) - (6) 1.250000 8.211611 6 0.15222348 1.0000000
#> 36 (12) - (7) -14.000000 7.344688 6 -1.90613947 0.9993519
#> 37 (12) - (8) 6.250000 8.211611 6 0.76111740 1.0000000
#> 38 (12) - (9) 2.250000 8.211611 6 0.27400226 1.0000000
#> 39 2 - 3 -3.000000 4.240458 6 -0.70747080 1.0000000
#> 40 2 - 4 -4.333333 4.240458 6 -1.02190227 1.0000000
#> 41 2 - (5) 0.750000 6.704752 6 0.11186096 1.0000000
#> 42 2 - (6) 0.750000 6.704752 6 0.11186096 1.0000000
#> 43 2 - (7) -14.500000 6.704752 6 -2.16264514 0.9936569
#> 44 2 - (8) 5.750000 6.704752 6 0.85760066 1.0000000
#> 45 2 - (9) 1.750000 6.704752 6 0.26100890 1.0000000
#> 46 3 - 4 -1.333333 4.240458 6 -0.31443147 1.0000000
#> 47 3 - (5) 3.750000 6.704752 6 0.55930478 1.0000000
#> 48 3 - (6) 3.750000 6.704752 6 0.55930478 1.0000000
#> 49 3 - (7) -11.500000 6.704752 6 -1.71520132 0.9999408
#> 50 3 - (8) 8.750000 6.704752 6 1.30504448 1.0000000
#> 51 3 - (9) 4.750000 6.704752 6 0.70845272 1.0000000
#> 52 4 - (5) 5.083333 6.704752 6 0.75816870 1.0000000
#> 53 4 - (6) 5.083333 6.704752 6 0.75816870 1.0000000
#> 54 4 - (7) -10.166667 6.704752 6 -1.51633739 0.9999980
#> 55 4 - (8) 10.083333 6.704752 6 1.50390840 0.9999984
#> 56 4 - (9) 6.083333 6.704752 6 0.90731664 1.0000000
#> 57 (5) - (6) 0.000000 8.211611 6 0.00000000 1.0000000
#> 58 (5) - (7) -15.250000 8.211611 6 -1.85712645 0.9996254
#> 59 (5) - (8) 5.000000 8.211611 6 0.60889392 1.0000000
#> 60 (5) - (9) 1.000000 7.344688 6 0.13615282 1.0000000
#> 61 (6) - (7) -15.250000 8.211611 6 -1.85712645 0.9996254
#> 62 (6) - (8) 5.000000 7.344688 6 0.68076409 1.0000000
#> 63 (6) - (9) 1.000000 8.211611 6 0.12177878 1.0000000
#> 64 (7) - (8) 20.250000 8.211611 6 2.46602036 0.9629870
#> 65 (7) - (9) 16.250000 8.211611 6 1.97890523 0.9986402
#> 66 (8) - (9) -4.000000 8.211611 6 -0.48711513 1.0000000
stopCluster(cl)
cl <- makeCluster(getOption("cl.cores", ncores))
pout2_sidak <- pairwise.augmentedRCBD(out2, cl = cl,
p.adjust = "sidak")
pout2_sidak
#> contrast estimate SE df t.ratio p.value sig
#> 1 1 - (10) 7.416667 6.704752 6 1.10618056 1.0000000
#> 2 1 - (11) -1.833333 6.704752 6 -0.27343789 1.0000000
#> 3 1 - (12) 5.166667 6.704752 6 0.77059769 1.0000000
#> 4 1 - 2 5.666667 4.240458 6 1.33633373 1.0000000
#> 5 1 - 3 2.666667 4.240458 6 0.62886293 1.0000000
#> 6 1 - 4 1.333333 4.240458 6 0.31443147 1.0000000
#> 7 1 - (5) 6.416667 6.704752 6 0.95703262 1.0000000
#> 8 1 - (6) 6.416667 6.704752 6 0.95703262 1.0000000
#> 9 1 - (7) -8.833333 6.704752 6 -1.31747347 1.0000000
#> 10 1 - (8) 11.416667 6.704752 6 1.70277232 0.9999506
#> 11 1 - (9) 7.416667 6.704752 6 1.10618056 1.0000000
#> 12 (10) - (11) -9.250000 8.211611 6 -1.12645375 1.0000000
#> 13 (10) - (12) -2.250000 8.211611 6 -0.27400226 1.0000000
#> 14 (10) - 2 -1.750000 6.704752 6 -0.26100890 1.0000000
#> 15 (10) - 3 -4.750000 6.704752 6 -0.70845272 1.0000000
#> 16 (10) - 4 -6.083333 6.704752 6 -0.90731664 1.0000000
#> 17 (10) - (5) -1.000000 8.211611 6 -0.12177878 1.0000000
#> 18 (10) - (6) -1.000000 7.344688 6 -0.13615282 1.0000000
#> 19 (10) - (7) -16.250000 8.211611 6 -1.97890523 0.9986402
#> 20 (10) - (8) 4.000000 7.344688 6 0.54461128 1.0000000
#> 21 (10) - (9) 0.000000 8.211611 6 0.00000000 1.0000000
#> 22 (11) - (12) 7.000000 7.344688 6 0.95306973 1.0000000
#> 23 (11) - 2 7.500000 6.704752 6 1.11860955 1.0000000
#> 24 (11) - 3 4.500000 6.704752 6 0.67116573 1.0000000
#> 25 (11) - 4 3.166667 6.704752 6 0.47230181 1.0000000
#> 26 (11) - (5) 8.250000 8.211611 6 1.00467496 1.0000000
#> 27 (11) - (6) 8.250000 8.211611 6 1.00467496 1.0000000
#> 28 (11) - (7) -7.000000 7.344688 6 -0.95306973 1.0000000
#> 29 (11) - (8) 13.250000 8.211611 6 1.61356888 0.9999880
#> 30 (11) - (9) 9.250000 8.211611 6 1.12645375 1.0000000
#> 31 (12) - 2 0.500000 6.704752 6 0.07457397 1.0000000
#> 32 (12) - 3 -2.500000 6.704752 6 -0.37286985 1.0000000
#> 33 (12) - 4 -3.833333 6.704752 6 -0.57173377 1.0000000
#> 34 (12) - (5) 1.250000 8.211611 6 0.15222348 1.0000000
#> 35 (12) - (6) 1.250000 8.211611 6 0.15222348 1.0000000
#> 36 (12) - (7) -14.000000 7.344688 6 -1.90613947 0.9993519
#> 37 (12) - (8) 6.250000 8.211611 6 0.76111740 1.0000000
#> 38 (12) - (9) 2.250000 8.211611 6 0.27400226 1.0000000
#> 39 2 - 3 -3.000000 4.240458 6 -0.70747080 1.0000000
#> 40 2 - 4 -4.333333 4.240458 6 -1.02190227 1.0000000
#> 41 2 - (5) 0.750000 6.704752 6 0.11186096 1.0000000
#> 42 2 - (6) 0.750000 6.704752 6 0.11186096 1.0000000
#> 43 2 - (7) -14.500000 6.704752 6 -2.16264514 0.9936569
#> 44 2 - (8) 5.750000 6.704752 6 0.85760066 1.0000000
#> 45 2 - (9) 1.750000 6.704752 6 0.26100890 1.0000000
#> 46 3 - 4 -1.333333 4.240458 6 -0.31443147 1.0000000
#> 47 3 - (5) 3.750000 6.704752 6 0.55930478 1.0000000
#> 48 3 - (6) 3.750000 6.704752 6 0.55930478 1.0000000
#> 49 3 - (7) -11.500000 6.704752 6 -1.71520132 0.9999408
#> 50 3 - (8) 8.750000 6.704752 6 1.30504448 1.0000000
#> 51 3 - (9) 4.750000 6.704752 6 0.70845272 1.0000000
#> 52 4 - (5) 5.083333 6.704752 6 0.75816870 1.0000000
#> 53 4 - (6) 5.083333 6.704752 6 0.75816870 1.0000000
#> 54 4 - (7) -10.166667 6.704752 6 -1.51633739 0.9999980
#> 55 4 - (8) 10.083333 6.704752 6 1.50390840 0.9999984
#> 56 4 - (9) 6.083333 6.704752 6 0.90731664 1.0000000
#> 57 (5) - (6) 0.000000 8.211611 6 0.00000000 1.0000000
#> 58 (5) - (7) -15.250000 8.211611 6 -1.85712645 0.9996254
#> 59 (5) - (8) 5.000000 8.211611 6 0.60889392 1.0000000
#> 60 (5) - (9) 1.000000 7.344688 6 0.13615282 1.0000000
#> 61 (6) - (7) -15.250000 8.211611 6 -1.85712645 0.9996254
#> 62 (6) - (8) 5.000000 7.344688 6 0.68076409 1.0000000
#> 63 (6) - (9) 1.000000 8.211611 6 0.12177878 1.0000000
#> 64 (7) - (8) 20.250000 8.211611 6 2.46602036 0.9629870
#> 65 (7) - (9) 16.250000 8.211611 6 1.97890523 0.9986402
#> 66 (8) - (9) -4.000000 8.211611 6 -0.48711513 1.0000000
stopCluster(cl)