freqdist.augmentedRCBD plots frequency distribution from an object of class augmentedRCBD along with the corresponding normal curve and check means with standard errors (if specified by argument highlight.check).

freqdist.augmentedRCBD(aug, xlab, highlight.check = TRUE, check.col = "red")

Arguments

aug

An object of class augmentedRCBD.

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 is TRUE.

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 R palette() etc.).

Value

The frequency distribution plot as a ggplot2 plot grob.

See also

Examples

# Example data 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 #> #> Treatment Means #> =============== #> Treatment Block Means SE r Min Max Adjusted Means #> 1 1 84.66667 3.844188 3 79 92 84.66667 #> 2 10 3 74.00000 NA 1 74 74 77.25000 #> 3 11 1 89.00000 NA 1 89 89 86.50000 #> 4 12 1 82.00000 NA 1 82 82 79.50000 #> 5 2 79.00000 1.154701 3 77 81 79.00000 #> 6 3 82.00000 2.645751 3 78 87 82.00000 #> 7 4 83.33333 3.929942 3 78 91 83.33333 #> 8 5 2 79.00000 NA 1 79 79 78.25000 #> 9 6 3 75.00000 NA 1 75 75 78.25000 #> 10 7 1 96.00000 NA 1 96 96 93.50000 #> 11 8 3 70.00000 NA 1 70 70 73.25000 #> 12 9 2 78.00000 NA 1 78 78 77.25000 #> #> 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 Groups #> ================== #> #> Method : lsd #> #> Treatment Adjusted Means SE df lower.CL upper.CL Group #> 8 8 73.25000 5.609598 6 59.52381 86.97619 1 #> 9 9 77.25000 5.609598 6 63.52381 90.97619 12 #> 10 10 77.25000 5.609598 6 63.52381 90.97619 12 #> 5 5 78.25000 5.609598 6 64.52381 91.97619 12 #> 6 6 78.25000 5.609598 6 64.52381 91.97619 12 #> 2 2 79.00000 2.998456 6 71.66304 86.33696 12 #> 12 12 79.50000 5.609598 6 65.77381 93.22619 12 #> 3 3 82.00000 2.998456 6 74.66304 89.33696 12 #> 4 4 83.33333 2.998456 6 75.99637 90.67029 12 #> 1 1 84.66667 2.998456 6 77.32971 92.00363 12 #> 11 11 86.50000 5.609598 6 72.77381 100.22619 12 #> 7 7 93.50000 5.609598 6 79.77381 107.22619 2
# Results for variable y2 out2 <- augmentedRCBD(data$blk, data$trt, data$y2, 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 7019 3510 12.261 0.007597 ** #> Treatment (eliminating Blocks) 11 58965 5360 18.727 0.000920 *** #> Treatment: Check 3 2150 717 2.504 0.156116 #> Treatment: Test and Test vs. Check 8 56815 7102 24.810 0.000473 *** #> Residuals 6 1717 286 #> --- #> 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 64708 5883 20.550 0.000707 *** #> Treatment: Check 3 2150 717 2.504 0.156116 #> Treatment: Test 7 34863 4980 17.399 0.001366 ** #> Treatment: Test vs. Check 1 27694 27694 96.749 6.36e-05 *** #> Block (eliminating Treatments) 2 1277 639 2.231 0.188645 #> Residuals 6 1718 286 #> --- #> Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1 #> #> Treatment Means #> =============== #> Treatment Block Means SE r Min Max Adjusted Means #> 1 1 256.0000 3.055050 3 250 260 256.0000 #> 2 10 3 450.0000 NA 1 450 450 437.6667 #> 3 11 1 300.0000 NA 1 300 300 299.4167 #> 4 12 1 289.0000 NA 1 289 289 288.4167 #> 5 2 228.0000 6.110101 3 220 240 228.0000 #> 6 3 247.6667 10.170764 3 237 268 247.6667 #> 7 4 264.0000 18.681542 3 227 287 264.0000 #> 8 5 2 281.0000 NA 1 281 281 293.9167 #> 9 6 3 395.0000 NA 1 395 395 382.6667 #> 10 7 1 347.0000 NA 1 347 347 346.4167 #> 11 8 3 226.0000 NA 1 226 226 213.6667 #> 12 9 2 311.0000 NA 1 311 311 323.9167 #> #> Coefficient of Variation #> ======================== #> 6.057617 #> #> Overall Adjusted Mean #> ===================== #> 298.4792 #> #> Standard Errors #> =================== #> Std. Error of Diff. CD (5%) #> Control Treatment Means 13.81424 33.80224 #> Two Test Treatments (Same Block) 23.92697 58.54719 #> Two Test Treatments (Different Blocks) 26.75117 65.45775 #> A Test Treatment and a Control Treatment 21.84224 53.44603 #> #> Treatment Groups #> ================== #> #> Method : lsd #> #> Treatment Adjusted Means SE df lower.CL upper.CL Group #> 8 8 213.6667 18.274527 6 168.9505 258.3828 12 #> 2 2 228.0000 9.768146 6 204.0982 251.9018 1 #> 3 3 247.6667 9.768146 6 223.7649 271.5685 123 #> 1 1 256.0000 9.768146 6 232.0982 279.9018 1234 #> 4 4 264.0000 9.768146 6 240.0982 287.9018 234 #> 12 12 288.4167 18.274527 6 243.7005 333.1328 345 #> 5 5 293.9167 18.274527 6 249.2005 338.6328 345 #> 11 11 299.4167 18.274527 6 254.7005 344.1328 45 #> 9 9 323.9167 18.274527 6 279.2005 368.6328 56 #> 7 7 346.4167 18.274527 6 301.7005 391.1328 56 #> 6 6 382.6667 18.274527 6 337.9505 427.3828 67 #> 10 10 437.6667 18.274527 6 392.9505 482.3828 7
# Frequency distribution plots freq1 <- freqdist.augmentedRCBD(out1, xlab = "Trait 1")
#> Warning: Removed 2 rows containing missing values (geom_bar).
class(freq1)
#> [1] "gtable" "gTree" "grob" "gDesc"
plot(freq1)
freq2 <- freqdist.augmentedRCBD(out2, xlab = "Trait 2")
#> Warning: Removed 2 rows containing missing values (geom_bar).
plot(freq2)
# Change check colours colset <- c("red3", "green4", "purple3", "darkorange3") freq1 <- freqdist.augmentedRCBD(out1, xlab = "Trait 1", check.col = colset)
#> Warning: Removed 2 rows containing missing values (geom_bar).
plot(freq1)
freq2 <- freqdist.augmentedRCBD(out2, xlab = "Trait 2", check.col = colset)
#> Warning: Removed 2 rows containing missing values (geom_bar).
plot(freq2)
# Without checks highlighted freq1 <- freqdist.augmentedRCBD(out1, xlab = "Trait 1", highlight.check = FALSE)
#> Warning: Removed 2 rows containing missing values (geom_bar).
plot(freq1)
freq2 <- freqdist.augmentedRCBD(out2, xlab = "Trait 2", highlight.check = FALSE)
#> Warning: Removed 2 rows containing missing values (geom_bar).
plot(freq2)