subset.pcss.core: Fetch the names of individuals/genotypes in the core set...

In rpcss: Constitution of Core Collections by Principal Component Scoring Strategy

Fetch the names of individuals/genotypes in the core set generated from pcss.core Output

Description

subset.pcss.core returns names of individuals/genotypes in the core collection from pcss.core Output.

Usage

## S3 method for class 'pcss.core'
subset(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.

Details

Use "size" to return names of individuals/genotypes in the core collection according to the threshold size criterion or use "variance" to return names according to the variability threshold criterion or use "logistic" to return names according to inflection point of rate of progress of cumulative variability retained identified by logistic regression.

Value

The names of individuals/genotypes in the core collection as a character vector.

See Also

pcss.core

Examples

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

suppressPackageStartupMessages(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)

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

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

# Core sets
out1$cores.info

# Fetch genotype names of core set by size criterion
subset(x = out1, criterion = "size")

# Fetch genotype names of core set by variance criterion
subset(x = out1, criterion = "variance")

# Fetch genotype names of core set by logistic regression criterion
subset(x = out1, criterion = "logistic")

#~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
# 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)

# Core sets
out2$cores.info

# Fetch genotype names of core set by size criterion
subset(x = out2, criterion = "size")

# Fetch genotype names of core set by variance criterion
subset(x = out2, criterion = "variance")

# Fetch genotype names of core set by logistic regression criterion
subset(x = out2, criterion = "logistic")
#~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
# Get core sets with PCSS (quantitative and qualitative data)
#~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~

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

# Core sets
out3$cores.info

# Fetch genotype names of core set by size criterion
subset(x = out3, criterion = "size")

# Fetch genotype names of core set by variance criterion
subset(x = out3, criterion = "variance")

# Fetch genotype names of core set by logistic regression criterion
subset(x = out3, criterion = "logistic")

rpcss documentation built on April 3, 2025, 10:57 p.m.