Plot the cumulative variability retained by individuals/genotypes from pcss.core Output

coreplot.pcss.core generates plots of cumulative variability retained by individuals/genotypes from pcss.core Output. The size of core collection and the corresponding cumulative variance retained are highlighted according to the criterion used.

Usage

# S3 method for class 'pcss.core'
coreplot(x, criterion = c("size", "variance", "logistic"), ...)

Arguments

x

An object of class pcss.core.

criterion

The core collection generation criterion. Either "size", "variance", or "logistic". See Details.

...

Unused.

Value

A plot of cumulative variability retained by individuals/genotypes as a ggplot object. In case of criterion = "logistic", a list with plots of cumulative variability retained by individuals/genotypes and rate of progress of cumulative contribution to variability. The size and variability retained by core collection are highlighted in each plot.

Details

Use "size" to highlight core collection according to the threshold size criterion or use "variance" to highlight core collection according to the variability threshold criterion or use "logistic" to highlight core collection generated according to inflection point of rate of progress of cumulative variability retained identified by logistic regression.

See also

pcss.core

Examples

#~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
# Prepare example data
#~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~

library(EvaluateCore)

# Get data from EvaluateCore

data("cassava_EC", package = "EvaluateCore")
data = cbind(Genotypes = rownames(cassava_EC), cassava_EC)
quant <- c("NMSR", "TTRN", "TFWSR", "TTRW", "TFWSS", "TTSW", "TTPW", "AVPW",
           "ARSR", "SRDM")
qual <- c("CUAL", "LNGS", "PTLC", "DSTA", "LFRT", "LBTEF", "CBTR", "NMLB",
          "ANGB", "CUAL9M", "LVC9M", "TNPR9M", "PL9M", "STRP", "STRC",
          "PSTR")
rownames(data) <- NULL

# Convert qualitative data columns to factor
data[, qual] <- lapply(data[, qual], as.factor)


library(FactoMineR)
library(factoextra)

#~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
# With quantitative data
#~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~

out1 <- pcss.core(data = data, names = "Genotypes",
                  quantitative = quant,
                  qualitative = NULL, eigen.threshold = NULL, size = 0.2,
                  var.threshold = 0.75)

# For core set constituted by size criterion
coreplot(x = out1, criterion = "size")


# For core set constituted by variance criterion
coreplot(x = out1, criterion = "variance")


# For core set constituted by logistic regression criterion
coreplot(x = out1, criterion = "logistic")
#> $`Cumulative contribution`

#> 
#> $`Rate of cumulative contribution`

#> 

#~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
# Get core sets with PCSS (qualitative data)
#~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~

out2 <- pcss.core(data = data, names = "Genotypes", quantitative = NULL,
                  qualitative = qual, eigen.threshold = NULL,
                  size = 0.2, var.threshold = 0.75)

# For core set constituted by size criterion
coreplot(x = out2, criterion = "size")


# For core set constituted by variance criterion
coreplot(x = out2, criterion = "variance")


# For core set constituted by logistic regression criterion
coreplot(x = out2, criterion = "logistic")
#> $`Cumulative contribution`

#> 
#> $`Rate of cumulative contribution`

#> 

#~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
# Get core sets with PCSS (quantitative and qualitative data)
#~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~

out3 <- pcss.core(data = data, names = "Genotypes",
                  quantitative = quant,
                  qualitative = qual, eigen.threshold = NULL)

# For core set constituted by size criterion
coreplot(x = out3, criterion = "size")


# For core set constituted by variance criterion
coreplot(x = out3, criterion = "variance")


# For core set constituted by logistic regression criterion
coreplot(x = out3, criterion = "logistic")
#> $`Cumulative contribution`

#> 
#> $`Rate of cumulative contribution`

#>