Plot Frequency Distribution from augmentedRCBD.menv Output
Source: R/freqdist.augmentedRCBD.menv.R
freqdist.augmentedRCBD.menv.Rdfreqdist.augmentedRCBD.menv plots frequency distribution from an object
of class augmentedRCBD.menv along with the corresponding normal curve
and check means with standard errors (if specified by argument
highlight.check).
Usage
# S3 method for class 'augmentedRCBD.menv'
freqdist(aug, xlab, highlight.check = TRUE, check.col = "red", ...)Arguments
- aug
An object of class
augmentedRCBD.menv.- xlab
The text for x axis label as a character string.
- highlight.check
If
TRUE, the check means and standard errors are also plotted. Default isTRUE.- check.col
The colour(s) to be used to highlight check values in the plot as a character vector. Must be valid colour values in R (named colours, hexadecimal representation, index of colours [
1:8] in default Rpalette()etc.).- ...
Unused
Examples
#~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
# Scenario 1: Test treatments are replicated across all environments
#~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
# Example data
blk1 <- c(1, 1, 1, 1, 1, 1, 1, 2, 2, 2, 2, 2, 2, 3, 3, 3, 3, 3, 3, 3,
4, 4, 4, 4, 4, 4, 4, 5, 5, 5, 5, 5, 5, 6, 6, 6, 6, 6, 6, 6,
7, 7, 7, 7, 7, 7, 7, 8, 8, 8, 8, 8, 8, 9, 9, 9, 9, 9, 9, 9)
trt1 <- c(1, 2, 3, 4, 7, 11, 12, 1, 2, 3, 4, 5, 9, 1, 2, 3, 4, 8, 6, 10,
1, 2, 3, 4, 8, 11, 5, 1, 2, 3, 4, 12, 9, 1, 2, 3, 4, 7, 6, 10,
1, 2, 3, 4, 7, 9, 12, 1, 2, 3, 4, 5, 6, 1, 2, 3, 4, 8, 11, 10)
y1 <- c(92, 79, 87, 81, 96, 89, 82, 79, 81, 81, 91, 79, 78, 83, 77,
78, 78, 70, 75, 74, 90, 80, 85, 78, 95, 86, 81, 78, 78, 76, 88,
76, 79, 80, 76, 75, 74, 77, 75, 72, 91, 81, 86, 80, 94, 87, 83,
78, 79, 77, 90, 74, 76, 82, 83, 86, 76, 73, 74, 69)
env1 <- c(1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1,
1, 1, 1, 1, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2,
2, 2, 2, 2, 2, 2, 2, 2, 3, 3, 3, 3, 3, 3, 3, 3,
3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3)
data1 <- data.frame(env1, blk1, trt1, y1)
chks1 <- c(1, 2, 3, 4)
# Convert block, treatment and environment to factors
data1$blk1 <- as.factor(data1$blk1)
data1$trt1 <- as.factor(data1$trt1)
data1$env1 <- as.factor(data1$env1)
# Contingency tables of factors
table(data1$env1, data1$trt1)
#>
#> 1 2 3 4 5 6 7 8 9 10 11 12
#> 1 3 3 3 3 1 1 1 1 1 1 1 1
#> 2 3 3 3 3 1 1 1 1 1 1 1 1
#> 3 3 3 3 3 1 1 1 1 1 1 1 1
table(data1$env1, data1$blk1)
#>
#> 1 2 3 4 5 6 7 8 9
#> 1 7 6 7 0 0 0 0 0 0
#> 2 0 0 0 7 6 7 0 0 0
#> 3 0 0 0 0 0 0 7 6 7
table(data1$blk1, data1$trt1)
#>
#> 1 2 3 4 5 6 7 8 9 10 11 12
#> 1 1 1 1 1 0 0 1 0 0 0 1 1
#> 2 1 1 1 1 1 0 0 0 1 0 0 0
#> 3 1 1 1 1 0 1 0 1 0 1 0 0
#> 4 1 1 1 1 1 0 0 1 0 0 1 0
#> 5 1 1 1 1 0 0 0 0 1 0 0 1
#> 6 1 1 1 1 0 1 1 0 0 1 0 0
#> 7 1 1 1 1 0 0 1 0 1 0 0 1
#> 8 1 1 1 1 1 1 0 0 0 0 0 0
#> 9 1 1 1 1 0 0 0 1 0 1 1 0
# Results
out1 <- augmentedRCBD.menv(block = data1$blk1, treatment = data1$trt1,
env = data1$env1, y = data1$y1, checks = chks1,
scenario = 1, method.comp = "lsd", alpha = 0.05,
group = TRUE, console = TRUE)
#> NOTE: Results may be misleading due to involvement in interactions
#>
#> Augmented Design Details
#> ========================
#>
#> Number of blocks "9"
#> Number of treatments "12"
#> Number of environments "3"
#> Number of check treatments "4"
#> Number of test treatments "8"
#> Check treatments "1, 2, 3, 4"
#>
#>
#> ANOVA, Treatment Adjusted
#> =========================
#> Df Sum Sq Mean Sq
#> Environment 2 24.7 12.35
#> Treatment (eliminating Blocks) 11 691.2 62.83
#> Treatment: Check 3 85.9 28.63
#> Treatment: Test and Test vs. Check 8 605.3 75.66
#> Block (within Environment, ignoring Treatments) 6 672.5 112.08
#> Environment × Treatment interaction 22 398.7 18.12
#> Interaction: Check × Environment 6 17.6 2.94
#> Interaction: Test and Test vs. Check × Environment 16 381.1 23.82
#> Residuals 18 546.5 30.36
#> F value Pr(>F)
#> Environment 0.407 0.6718
#> Treatment (eliminating Blocks) 2.070 0.0824 .
#> Treatment: Check 0.943 0.4406
#> Treatment: Test and Test vs. Check 2.492 0.0513 .
#> Block (within Environment, ignoring Treatments) 3.692 0.0144 *
#> Environment × Treatment interaction 0.597 0.8753
#> Interaction: Check × Environment 0.097 0.9958
#> Interaction: Test and Test vs. Check × Environment 0.785 0.6846
#> Residuals
#> ---
#> Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1
#>
#> ANOVA, Block Adjusted
#> =====================
#> Df Sum Sq Mean Sq
#> Environment 2 24.7 12.35
#> Treatment (ignoring Blocks) 11 691.2 62.83
#> Treatment: Check 3 85.9 28.63
#> Treatment: Test 7 561.2 80.17
#> Treatment: Test vs. Check 1 44.1 44.10
#> Block (within Environment, eliminating Treatments) 6 672.5 112.08
#> Environment × Treatment interaction 22 398.7 18.12
#> Interaction: Check × Environment 6 17.6 2.94
#> Interaction: Test × Environment 14 340.2 24.30
#> Interaction: Test vs. Check × Environment 2 40.9 20.47
#> Residuals 18 546.5 30.36
#> F value Pr(>F)
#> Environment 0.407 0.6718
#> Treatment (ignoring Blocks) 2.070 0.0824 .
#> Treatment: Check 0.943 0.4406
#> Treatment: Test 2.640 0.0459 *
#> Treatment: Test vs. Check 1.453 0.2437
#> Block (within Environment, eliminating Treatments) 3.692 0.0144 *
#> Environment × Treatment interaction 0.597 0.8753
#> Interaction: Check × Environment 0.097 0.9958
#> Interaction: Test × Environment 0.800 0.6596
#> Interaction: Test vs. Check × Environment 0.674 0.5219
#> Residuals
#> ---
#> Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1
#>
#> Coefficient of Variation
#> ========================
#> 6.81942
#>
#> Overall Adjusted Mean
#> =====================
#> 80.31944
#>
#> Standard Errors
#> ===============
#> Std. Error of Diff. CD (5%)
#> Control Treatment Means 1.499657 3.150662
#> Two Test Treatments (Same Block) 4.498971 9.451987
#> Two Test Treatments (Different Blocks) 5.030003 10.567643
#> A Test Treatment and a Control Treatment 3.749143 7.876656
#>
#> Treatment Means
#> ===============
#> Treatment Means SE r Min Max Adjusted Means
#> 1 83.67 1.92 9 78.00 92.00 83.67
#> 10 71.67 1.45 3 69.00 74.00 74.17
#> 11 83.00 4.58 3 74.00 89.00 81.25
#> 12 80.33 2.19 3 76.00 83.00 78.75
#> 2 79.33 0.73 9 76.00 83.00 79.33
#> 3 81.22 1.61 9 75.00 87.00 81.22
#> 4 81.78 2.10 9 74.00 91.00 81.78
#> 5 78.00 2.08 3 74.00 81.00 77.08
#> 6 75.33 0.33 3 75.00 76.00 78.08
#> 7 89.00 6.03 3 77.00 96.00 88.67
#> 8 79.33 7.88 3 70.00 95.00 79.50
#> 9 81.33 2.85 3 78.00 87.00 80.33
#>
#>
#> Comparisons
#> ===========
#>
#> Method : lsd
#>
#> contrast estimate SE df t.ratio p.value sig
#> treatment1 - treatment2 4.33 2.60 18 1.668 0.113
#> treatment1 - treatment3 2.44 2.60 18 0.941 0.359
#> treatment1 - treatment4 1.89 2.60 18 0.727 0.476
#> treatment1 - treatment5 6.58 3.90 18 1.690 0.108
#> treatment1 - treatment6 5.58 3.90 18 1.433 0.169
#> treatment1 - treatment7 -5.00 3.90 18 -1.283 0.216
#> treatment1 - treatment8 4.17 3.90 18 1.069 0.299
#> treatment1 - treatment9 3.33 3.90 18 0.856 0.404
#> treatment1 - treatment10 9.50 3.90 18 2.438 0.025 *
#> treatment1 - treatment11 2.42 3.90 18 0.620 0.543
#> treatment1 - treatment12 4.92 3.90 18 1.262 0.223
#> treatment2 - treatment3 -1.89 2.60 18 -0.727 0.476
#> treatment2 - treatment4 -2.44 2.60 18 -0.941 0.359
#> treatment2 - treatment5 2.25 3.90 18 0.577 0.571
#> treatment2 - treatment6 1.25 3.90 18 0.321 0.752
#> treatment2 - treatment7 -9.33 3.90 18 -2.395 0.028 *
#> treatment2 - treatment8 -0.17 3.90 18 -0.043 0.966
#> treatment2 - treatment9 -1.00 3.90 18 -0.257 0.800
#> treatment2 - treatment10 5.17 3.90 18 1.326 0.201
#> treatment2 - treatment11 -1.92 3.90 18 -0.492 0.629
#> treatment2 - treatment12 0.58 3.90 18 0.150 0.883
#> treatment3 - treatment4 -0.56 2.60 18 -0.214 0.833
#> treatment3 - treatment5 4.14 3.90 18 1.062 0.302
#> treatment3 - treatment6 3.14 3.90 18 0.806 0.431
#> treatment3 - treatment7 -7.44 3.90 18 -1.911 0.072
#> treatment3 - treatment8 1.72 3.90 18 0.442 0.664
#> treatment3 - treatment9 0.89 3.90 18 0.228 0.822
#> treatment3 - treatment10 7.06 3.90 18 1.811 0.087
#> treatment3 - treatment11 -0.03 3.90 18 -0.007 0.994
#> treatment3 - treatment12 2.47 3.90 18 0.635 0.534
#> treatment4 - treatment5 4.69 3.90 18 1.205 0.244
#> treatment4 - treatment6 3.69 3.90 18 0.948 0.356
#> treatment4 - treatment7 -6.89 3.90 18 -1.768 0.094
#> treatment4 - treatment8 2.28 3.90 18 0.585 0.566
#> treatment4 - treatment9 1.44 3.90 18 0.371 0.715
#> treatment4 - treatment10 7.61 3.90 18 1.953 0.066
#> treatment4 - treatment11 0.53 3.90 18 0.135 0.894
#> treatment4 - treatment12 3.03 3.90 18 0.777 0.447
#> treatment5 - treatment6 -1.00 4.86 18 -0.206 0.839
#> treatment5 - treatment7 -11.58 5.03 18 -2.303 0.033 *
#> treatment5 - treatment8 -2.42 4.86 18 -0.497 0.625
#> treatment5 - treatment9 -3.25 4.86 18 -0.669 0.512
#> treatment5 - treatment10 2.92 5.03 18 0.580 0.569
#> treatment5 - treatment11 -4.17 4.86 18 -0.857 0.402
#> treatment5 - treatment12 -1.67 5.03 18 -0.331 0.744
#> treatment6 - treatment7 -10.58 4.86 18 -2.178 0.043 *
#> treatment6 - treatment8 -1.42 4.86 18 -0.292 0.774
#> treatment6 - treatment9 -2.25 5.03 18 -0.447 0.660
#> treatment6 - treatment10 3.92 4.68 18 0.836 0.414
#> treatment6 - treatment11 -3.17 5.03 18 -0.630 0.537
#> treatment6 - treatment12 -0.67 5.03 18 -0.133 0.896
#> treatment7 - treatment8 9.17 5.03 18 1.822 0.085
#> treatment7 - treatment9 8.33 4.86 18 1.715 0.104
#> treatment7 - treatment10 14.50 4.86 18 2.984 0.008 **
#> treatment7 - treatment11 7.42 4.86 18 1.526 0.144
#> treatment7 - treatment12 9.92 4.68 18 2.118 0.048 *
#> treatment8 - treatment9 -0.83 5.03 18 -0.166 0.870
#> treatment8 - treatment10 5.33 4.68 18 1.139 0.270
#> treatment8 - treatment11 -1.75 4.68 18 -0.374 0.713
#> treatment8 - treatment12 0.75 5.03 18 0.149 0.883
#> treatment9 - treatment10 6.17 5.03 18 1.226 0.236
#> treatment9 - treatment11 -0.92 5.03 18 -0.182 0.857
#> treatment9 - treatment12 1.58 4.68 18 0.338 0.739
#> treatment10 - treatment11 -7.08 4.86 18 -1.458 0.162
#> treatment10 - treatment12 -4.58 5.03 18 -0.911 0.374
#> treatment11 - treatment12 2.50 4.86 18 0.514 0.613
#>
#> Treatment Groups
#> ================
#>
#> Method : lsd
#>
#> Treatment Adjusted Means SE df lower.CL upper.CL Group
#> 10 74.17 3.44 18 66.95 81.39 1
#> 5 77.08 3.44 18 69.86 84.30 12
#> 6 78.08 3.44 18 70.86 85.30 12
#> 12 78.75 3.44 18 71.53 85.97 12
#> 2 79.33 1.84 18 75.47 83.19 12
#> 8 79.50 3.44 18 72.28 86.72 123
#> 9 80.33 3.44 18 73.11 87.55 123
#> 3 81.22 1.84 18 77.36 85.08 123
#> 11 81.25 3.44 18 74.03 88.47 123
#> 4 81.78 1.84 18 77.92 85.64 123
#> 1 83.67 1.84 18 79.81 87.53 23
#> 7 88.67 3.44 18 81.45 95.89 3
# Frequency distribution plots
freq1 <- freqdist(out1, xlab = "Trait 1")
#> Warning: Removed 2 rows containing missing values or values outside the scale range
#> (`geom_bar()`).
class(freq1)
#> [1] "gtable" "gTree" "grob" "gDesc"
plot(freq1)
# Change check colours
colset <- c("red3", "green4", "purple3", "darkorange3")
freq1 <- freqdist(out1, xlab = "Trait 1", check.col = colset)
#> Warning: Removed 2 rows containing missing values or values outside the scale range
#> (`geom_bar()`).
plot(freq1)
# Without checks highlighted
freq1 <- freqdist(out1, xlab = "Trait 1", highlight.check = FALSE)
#> Warning: Removed 2 rows containing missing values or values outside the scale range
#> (`geom_bar()`).
plot(freq1)
#~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
# Test treatments are not replicated across all environments
#~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
# Example data
blk2 <- c(1, 1, 1, 1, 1, 1, 1, 2, 2, 2, 2, 2, 2, 3, 3, 3, 3, 3, 3, 3,
4, 4, 4, 4, 4, 4, 4, 5, 5, 5, 5, 5, 5, 6, 6, 6, 6, 6, 6, 6,
7, 7, 7, 7, 7, 7, 7, 8, 8, 8, 8, 8, 8, 9, 9, 9, 9, 9, 9, 9)
trt2 <- c(1, 2, 3, 4, 7, 10, 11, 1, 2, 3, 4, 5, 9, 1, 2, 3, 4, 8, 6, 12,
1, 2, 3, 4, 16, 19, 13, 1, 2, 3, 4, 20, 17, 1, 2, 3, 4, 15, 14, 18,
1, 2, 3, 4, 22, 25, 27, 1, 2, 3, 4, 21, 23, 1, 2, 3, 4, 24, 26, 28)
y2 <- c(92, 79, 87, 81, 96, 89, 82, 79, 81, 81, 91, 79, 78, 83, 77,
78, 78, 70, 75, 74, 90, 80, 85, 78, 95, 86, 81, 78, 78, 76, 88,
76, 79, 80, 76, 75, 74, 77, 75, 72, 91, 81, 86, 80, 94, 87, 83,
78, 79, 77, 90, 74, 76, 82, 83, 86, 76, 73, 74, 69)
env2 <- c(1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1,
1, 1, 1, 1, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2,
2, 2, 2, 2, 2, 2, 2, 2, 3, 3, 3, 3, 3, 3, 3, 3,
3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3)
data2 <- data.frame(env2, blk2, trt2, y2)
chks2 <- c(1, 2, 3, 4)
# Convert block, treatment and environment to factors
data2$blk2 <- as.factor(data2$blk2)
data2$trt2 <- as.factor(data2$trt2)
data2$env2 <- as.factor(data2$env2)
# Contingency tables of factors
table(data2$env2, data2$trt2)
#>
#> 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28
#> 1 3 3 3 3 1 1 1 1 1 1 1 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
#> 2 3 3 3 3 0 0 0 0 0 0 0 0 1 1 1 1 1 1 1 1 0 0 0 0 0 0 0 0
#> 3 3 3 3 3 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 1 1 1 1 1 1 1
table(data2$env2, data2$blk2)
#>
#> 1 2 3 4 5 6 7 8 9
#> 1 7 6 7 0 0 0 0 0 0
#> 2 0 0 0 7 6 7 0 0 0
#> 3 0 0 0 0 0 0 7 6 7
table(data2$blk2, data2$trt2)
#>
#> 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28
#> 1 1 1 1 1 0 0 1 0 0 1 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
#> 2 1 1 1 1 1 0 0 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
#> 3 1 1 1 1 0 1 0 1 0 0 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
#> 4 1 1 1 1 0 0 0 0 0 0 0 0 1 0 0 1 0 0 1 0 0 0 0 0 0 0 0 0
#> 5 1 1 1 1 0 0 0 0 0 0 0 0 0 0 0 0 1 0 0 1 0 0 0 0 0 0 0 0
#> 6 1 1 1 1 0 0 0 0 0 0 0 0 0 1 1 0 0 1 0 0 0 0 0 0 0 0 0 0
#> 7 1 1 1 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 0 0 1 0 1 0
#> 8 1 1 1 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 0 1 0 0 0 0 0
#> 9 1 1 1 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 0 1 0 1
# Results
out2 <- augmentedRCBD.menv(block = data2$blk2, treatment = data2$trt2,
env = data2$env2, y = data2$y2, checks = chks2,
scenario = 2, method.comp = "lsd", alpha = 0.05,
group = TRUE, console = TRUE)
#>
#> Augmented Design Details
#> ========================
#>
#> Number of blocks "9"
#> Number of treatments "28"
#> Number of environments "3"
#> Number of check treatments "4"
#> Number of test treatments "24"
#> Check treatments "1, 2, 3, 4"
#>
#>
#> ANOVA, Treatment Adjusted
#> =========================
#> Df Sum Sq Mean Sq
#> Environment 2 24.7 12.35
#> Treatment (eliminating Blocks) 27 1550.0 57.41
#> Treatment: Check 3 85.9 28.63
#> Treatment: Test and Test vs. Check 24 1464.1 61.00
#> Block (within Environment, ignoring Treatments) 6 194.8 32.47
#> Environment × Treatment interaction 6 17.6 2.94
#> Residuals 18 546.5 30.36
#> F value Pr(>F)
#> Environment 0.407 0.6718
#> Treatment (eliminating Blocks) 1.891 0.0817 .
#> Treatment: Check 0.943 0.4406
#> Treatment: Test and Test vs. Check 2.009 0.0664 .
#> Block (within Environment, ignoring Treatments) 1.070 0.4162
#> Environment × Treatment interaction 0.097 0.9958
#> Residuals
#> ---
#> Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1
#>
#> ANOVA, Block Adjusted
#> =====================
#> Df Sum Sq Mean Sq
#> Environment 2 24.7 12.35
#> Treatment (ignoring Blocks) 27 1550.0 57.41
#> Treatment: Check 3 85.9 28.63
#> Treatment: Test 23 1420.0 61.74
#> Treatment: Test vs. Check 1 44.1 44.10
#> Block (within Environment, eliminating Treatments) 6 194.8 32.47
#> Environment × Treatment interaction 6 17.6 2.94
#> Residuals 18 546.5 30.36
#> F value Pr(>F)
#> Environment 0.407 0.6718
#> Treatment (ignoring Blocks) 1.891 0.0817 .
#> Treatment: Check 0.943 0.4406
#> Treatment: Test 2.033 0.0643 .
#> Treatment: Test vs. Check 1.453 0.2437
#> Block (within Environment, eliminating Treatments) 1.070 0.4162
#> Environment × Treatment interaction 0.097 0.9958
#> Residuals
#> ---
#> Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1
#>
#> Coefficient of Variation
#> ========================
#> NA
#>
#> Overall Adjusted Mean
#> =====================
#> 79.98214
#>
#> Standard Errors
#> ===============
#> Std. Error of Diff. CD (5%)
#> Control Treatment Means NA NA
#> Two Test Treatments (Same Block) NA NA
#> Two Test Treatments (Different Blocks) NA NA
#> A Test Treatment and a Control Treatment NA NA
#>
#> Treatment Means
#> ===============
#> Treatment Means SE r Min Max Adjusted Means
#> 1 83.67 1.92 9 78.00 92.00 83.67
#> 10 89.00 <NA> 1 89.00 89.00 85.75
#> 11 82.00 <NA> 1 82.00 82.00 78.75
#> 12 74.00 <NA> 1 74.00 74.00 76.50
#> 13 81.00 <NA> 1 81.00 81.00 79.25
#> 14 75.00 <NA> 1 75.00 75.00 80.25
#> 15 77.00 <NA> 1 77.00 77.00 82.25
#> 16 95.00 <NA> 1 95.00 95.00 93.25
#> 17 79.00 <NA> 1 79.00 79.00 80.50
#> 18 72.00 <NA> 1 72.00 72.00 77.25
#> 19 86.00 <NA> 1 86.00 86.00 84.25
#> 2 79.33 0.73 9 76.00 83.00 79.33
#> 20 76.00 <NA> 1 76.00 76.00 77.50
#> 21 74.00 <NA> 1 74.00 74.00 74.50
#> 22 94.00 <NA> 1 94.00 94.00 91.00
#> 23 76.00 <NA> 1 76.00 76.00 76.50
#> 24 73.00 <NA> 1 73.00 73.00 72.75
#> 25 87.00 <NA> 1 87.00 87.00 84.00
#> 26 74.00 <NA> 1 74.00 74.00 73.75
#> 27 83.00 <NA> 1 83.00 83.00 80.00
#> 28 69.00 <NA> 1 69.00 69.00 68.75
#> 3 81.22 1.61 9 75.00 87.00 81.22
#> 4 81.78 2.10 9 74.00 91.00 81.78
#> 5 79.00 <NA> 1 79.00 79.00 77.50
#> 6 75.00 <NA> 1 75.00 75.00 77.50
#> 7 96.00 <NA> 1 96.00 96.00 92.75
#> 8 70.00 <NA> 1 70.00 70.00 72.50
#> 9 78.00 <NA> 1 78.00 78.00 76.50
#>
#>
#> Comparisons
#> ===========
#>
#> Method : lsd
#>
#> contrast estimate SE df t.ratio p.value sig
#> treatment1 - treatment2 4.33 2.29 24 1.896 0.070
#> treatment1 - treatment3 2.44 2.29 24 1.070 0.295
#> treatment1 - treatment4 1.89 2.29 24 0.826 0.417
#> treatment1 - treatment5 6.17 5.60 24 1.102 0.282
#> treatment1 - treatment6 6.17 5.60 24 1.102 0.282
#> treatment1 - treatment7 -9.08 5.60 24 -1.623 0.118
#> treatment1 - treatment8 11.17 5.60 24 1.995 0.058
#> treatment1 - treatment9 7.17 5.60 24 1.280 0.213
#> treatment1 - treatment10 -2.08 5.60 24 -0.372 0.713
#> treatment1 - treatment11 4.92 5.60 24 0.878 0.389
#> treatment1 - treatment12 7.17 5.60 24 1.280 0.213
#> treatment1 - treatment13 4.42 5.60 24 0.789 0.438
#> treatment1 - treatment14 3.42 5.60 24 0.610 0.547
#> treatment1 - treatment15 1.42 5.60 24 0.253 0.802
#> treatment1 - treatment16 -9.58 5.60 24 -1.712 0.100
#> treatment1 - treatment17 3.17 5.60 24 0.566 0.577
#> treatment1 - treatment18 6.42 5.60 24 1.146 0.263
#> treatment1 - treatment19 -0.58 5.60 24 -0.104 0.918
#> treatment1 - treatment20 6.17 5.60 24 1.102 0.282
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#> treatment6 - treatment28 8.75 7.67 24 1.141 0.265
#> treatment7 - treatment8 20.25 7.67 24 2.642 0.014 *
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#> treatment7 - treatment12 16.25 7.67 24 2.120 0.045 *
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#> treatment7 - treatment20 15.25 7.67 24 1.989 0.058
#> treatment7 - treatment21 18.25 7.67 24 2.381 0.026 *
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#> treatment23 - treatment28 7.75 7.67 24 1.011 0.322
#> treatment24 - treatment25 -11.25 7.67 24 -1.468 0.155
#> treatment24 - treatment26 -1.00 6.86 24 -0.146 0.885
#> treatment24 - treatment27 -7.25 7.67 24 -0.946 0.354
#> treatment24 - treatment28 4.00 6.86 24 0.583 0.565
#> treatment25 - treatment26 10.25 7.67 24 1.337 0.194
#> treatment25 - treatment27 4.00 6.86 24 0.583 0.565
#> treatment25 - treatment28 15.25 7.67 24 1.989 0.058
#> treatment26 - treatment27 -6.25 7.67 24 -0.815 0.423
#> treatment26 - treatment28 5.00 6.86 24 0.729 0.473
#> treatment27 - treatment28 11.25 7.67 24 1.468 0.155
#>
#> Treatment Groups
#> ================
#>
#> Method : lsd
#>
#> Treatment Adjusted Means SE df lower.CL upper.CL Group
#> 28 68.75 5.36 24 57.69 79.81 1
#> 8 72.50 5.36 24 61.44 83.56 12
#> 24 72.75 5.36 24 61.69 83.81 12
#> 26 73.75 5.36 24 62.69 84.81 12
#> 21 74.50 5.36 24 63.44 85.56 12
#> 9 76.50 5.36 24 65.44 87.56 123
#> 23 76.50 5.36 24 65.44 87.56 123
#> 12 76.50 5.36 24 65.44 87.56 123
#> 18 77.25 5.36 24 66.19 88.31 1234
#> 5 77.50 5.36 24 66.44 88.56 12345
#> 6 77.50 5.36 24 66.44 88.56 12345
#> 20 77.50 5.36 24 66.44 88.56 12345
#> 11 78.75 5.36 24 67.69 89.81 12345
#> 13 79.25 5.36 24 68.19 90.31 12345
#> 2 79.33 1.62 24 76.00 82.67 12
#> 27 80.00 5.36 24 68.94 91.06 12345
#> 14 80.25 5.36 24 69.19 91.31 12345
#> 17 80.50 5.36 24 69.44 91.56 12345
#> 3 81.22 1.62 24 77.89 84.56 234
#> 4 81.78 1.62 24 78.44 85.11 2345
#> 15 82.25 5.36 24 71.19 93.31 12345
#> 1 83.67 1.62 24 80.33 87.00 2345
#> 25 84.00 5.36 24 72.94 95.06 12345
#> 19 84.25 5.36 24 73.19 95.31 12345
#> 10 85.75 5.36 24 74.69 96.81 2345
#> 22 91.00 5.36 24 79.94 102.06 345
#> 7 92.75 5.36 24 81.69 103.81 45
#> 16 93.25 5.36 24 82.19 104.31 5
# Frequency distribution plots
freq2 <- freqdist(out2, xlab = "Trait 1")
#> Warning: Removed 2 rows containing missing values or values outside the scale range
#> (`geom_bar()`).
class(freq2)
#> [1] "gtable" "gTree" "grob" "gDesc"
plot(freq2)
# Change check colours
colset <- c("red3", "green4", "purple3", "darkorange3")
freq2 <- freqdist(out2, xlab = "Trait 1", check.col = colset)
#> Warning: Removed 2 rows containing missing values or values outside the scale range
#> (`geom_bar()`).
plot(freq2)
# Without checks highlighted
freq2 <- freqdist(out2, xlab = "Trait 1", highlight.check = FALSE)
#> Warning: Removed 2 rows containing missing values or values outside the scale range
#> (`geom_bar()`).
plot(freq2)
