augmentedRCBD.bulk: Analysis of Augmented Randomised Complete Block Design for...
In augmentedRCBD: Analysis of Augmented Randomised Complete Block Designs

augmentedRCBD.bulk

R Documentation

Analysis of Augmented Randomised Complete Block Design for Multiple Traits/Characters

Description

augmentedRCBD.bulk is a wrapper around the functions augmentedRCBD, describe.augmentedRCBD, freqdist.augmentedRCBD and gva.augmentedRCBD. It will carry out these analyses for multiple traits/characters from the input data as a data frame object.

Usage

augmentedRCBD.bulk(
  data,
  block,
  treatment,
  traits,
  checks = NULL,
  alpha = 0.05,
  describe = TRUE,
  freqdist = TRUE,
  gva = TRUE,
  k = 2.063,
  check.col = "red",
  console = TRUE
)

Arguments

`data`	The data as a data frame object. The data frame should possess columns specifying the block, treatment and multiple traits/characters.
`block`	Name of column specifying the blocks in the design as a character string.
`treatment`	Name of column specifying the treatments as a character string.
`traits`	Name of columns specifying the multiple traits/characters as a character vector.
`checks`	Character vector of the checks present in `treatment` levels. If not specified, checks are inferred from the data on the basis of number of replications of treatments/genotypes.
`alpha`	Type I error probability (Significance level) to be used for multiple comparisons.
`describe`	If `TRUE`, descriptive statistics will be computed. Default is `TRUE`.
`freqdist`	If `TRUE`, frequency distributions be plotted. Default is `TRUE`.
`gva`	If `TRUE`, genetic variability analysis will be done. Default is `TRUE`.
`k`	The standardized selection differential or selection intensity required for computation of Genetic advance. Default is 2.063 for 5% selection proportion (see Details in `gva.augmentedRCBD`). Ignored if `gva = FALSE`.
`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.).
`console`	If `TRUE`, output will be printed to console. Default is `TRUE`.

Value

A list of class augmentedRCBD.bulk containing the following components:

`Details`	Details of the augmented design used and the traits/characters.
`ANOVA, Treatment Adjusted`	A data frame of mean sum of squares, p value and stastical significance of the specified traits from treatment adjusted ANOVA.
`ANOVA, Block Adjusted`	A data frame of mean sum of squares, p value and stastical significance of the specified traits from block adjusted ANOVA
`Means`	A data frame of the adjusted means of the treatments for the specified traits.
`Check statistics`	A list of data frames with check statistics such as number of replications, standard error, minimum and maximum value
`alpha`	Type I error probability (Significance level) used.
`Std. Errors`	A data frame of standard error of difference between various combinations for the specified traits.
`CD`	A data frame of critical difference (at the specified alpha) between various combinations for the specified traits.
`Overall adjusted mean`	A data frame of the overall adjusted mean for the specified traits.
`CV`	A data frame of the coefficient of variance for the specified traits.
`Descriptive statistics`	A data frame of descriptive statistics for the specified traits.
`Frequency distribution`	A list of ggplot2 plot grobs of the frequency distribution plots.
`k`	The standardized selection differential or selection intensity used for computaton of Genetic advance.
`Genetic variability analysis`	A data frame of genetic variability statistics for the specified traits.
`GVA plots`	A list of three ggplot2 objects with the plots for (a) Phenotypic and Genotypic CV, (b) Broad sense heritability and (c) Genetic advance over mean
`warnings`	A list of warning messages (if any) captured during model fitting, frequency distribution plotting and genetic variability analysis.

Note

In this case treatment comparisons/grouping by least significant difference or Tukey's honest significant difference method is not computed. Also the output object size is reduced using the simplify = TRUE argument in the augmentedRCBD function.

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)
dataf <- data.frame(blk, trt, y1, y2)

bout <- augmentedRCBD.bulk(data = dataf, block = "blk",
                           treatment = "trt", traits = c("y1", "y2"),
                           checks = NULL, alpha = 0.05, describe = TRUE,
                           freqdist = TRUE, gva = TRUE,
                           check.col = c("brown", "darkcyan",
                                         "forestgreen", "purple"),
                           console = TRUE)

# Frequency distribution plots
lapply(bout$`Frequency distribution`, plot)

# GVA plots
bout$`GVA plots`

augmentedRCBD documentation built on Aug. 19, 2023, 1:06 a.m.