EvaluateCore (version 0.1.1)

percentdiff.evaluate.core: Percentage Difference of Means and Variances

Description

Compute the following differences between the entire collection (EC) and core set (CS).

• Percentage of significant differences of mean () hu_methods_2000EvaluateCore

• Percentage of significant differences of variance () hu_methods_2000EvaluateCore

• Average of absolute differences between means () kim_powercore_2007EvaluateCore

• Average of absolute differences between variances () kim_powercore_2007EvaluateCore

• Percentage difference between the mean squared Euclidean distance among accessions () studnicki_comparing_2013EvaluateCore

Usage

percentdiff.evaluate.core(data, names, quantitative, selected, alpha = 0.05)

Arguments

data

The data as a data frame object. The data frame should possess one row per individual and columns with the individual names and multiple trait/character data.

names

Name of column with the individual names as a character string

quantitative

Name of columns with the quantitative traits as a character vector.

selected

Character vector with the names of individuals selected in core collection and present in the names column.

alpha

Type I error probability (Significance level) of difference.

Value

A data frame with the values of , , , and .

Details

The differences are computed as follows.

Where, is the number of traits with a significant difference between the means of the EC and the CS and is the total number of traits.

Where, is the number of traits with a significant difference between the variances of the EC and the CS and is the total number of traits.

Where, is the mean of the EC for the th trait, is the mean of the CS for the th trait and is the total number of traits.

Where, is the variance of the EC for the th trait, is the variance of the CS for the th trait and is the total number of traits.

Where, is the mean squared Euclidean distance among accessions in the CS and is the mean squared Euclidean distance among accessions in the EC.

References

See Also

snk.evaluate.core, snk.evaluate.core

Examples

# NOT RUN {
####################################
# Use data from R package ccChooser
####################################

library(ccChooser)
data("dactylis_CC")
data("dactylis_EC")

ec <- cbind(genotypes = rownames(dactylis_EC), dactylis_EC[, -1])
ec$genotypes <- as.character(ec$genotypes)
rownames(ec) <- NULL
ec[, c("X1", "X6", "X7")] <- lapply(ec[, c("X1", "X6", "X7")],
                                    function(x) cut(x, breaks = 4))
ec[, c("X1", "X6", "X7")] <- lapply(ec[, c("X1", "X6", "X7")],
                                    function(x) factor(as.numeric(x)))
head(ec)

core <- rownames(dactylis_CC)

quant <- c("X2", "X3", "X4", "X5", "X8")
qual <- c("X1", "X6", "X7")

####################################
# EvaluateCore
####################################

percentdiff.evaluate.core(data = ec, names = "genotypes",
                          quantitative = quant, selected = core)


# }