contrib: Plot Contribution or Loadings of Traits for each...

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

Plot Contribution or Loadings of Traits for each Dimension/Factor from pcss.core Output

Description

contrib.pcss.core generates bar plots of contributions or loadings ("right singular vectors") of traits for each dimension/factor from the output of pcss.core.

Usage

contrib(x, ...)

## Default S3 method:
contrib(
  x,
  ndim = NULL,
  plot.loadings = FALSE,
  use.sign = TRUE,
  sort.value = TRUE,
  ...
)

## S3 method for class 'pcss.core'
contrib(
  x,
  ndim = NULL,
  plot.loadings = FALSE,
  use.sign = TRUE,
  sort.value = TRUE,
  ...
)

contrib(x, ...)

Arguments

x	An object of class pcss.core.
...	Unused.
ndim	The number of dimensions for which contribution or loadings of traits are to be plotted.
plot.loadings	If TRUE, the loadings or "right singular vectors" are plotted instead of contributions. Default is FALSE.
use.sign	If TRUE, contributions of variables are given the sign of their corresponding coordinates. Default is TRUE.
sort.value	If TRUE, the bars are sorted according to their value.

Value

The contributions/loadings bar plot as a ggplot object.

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)


library(FactoMineR)
suppressPackageStartupMessages(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)


# Plot contributions of genotypes - with sign - sorted
contrib(x = out1, ndim = 5)

# Plot contributions of genotypes - without sign - sorted
contrib(x = out1, ndim = 5, use.sign = FALSE)

# Plot loadings/coordinates of genotypes - with sign - sorted
contrib(x = out1, ndim = 5, plot.loadings = TRUE)

# Plot contributions of genotypes - with sign - unsorted
contrib(x = out1, ndim = 5, sort.value = FALSE)

# Plot biplot with factoextra
fviz_contrib(out1$raw.out, choice = "var", axes = 1)
fviz_contrib(out1$raw.out, choice = "var", axes = 2)


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


# Plot contributions of genotypes - with sign - sorted
contrib(x = out2, ndim = 5)

# Plot contributions of genotypes - without sign - sorted
contrib(x = out2, ndim = 5, use.sign = FALSE)

# Plot loadings/coordinates of genotypes - with sign - sorted
contrib(x = out2, ndim = 5, plot.loadings = TRUE)

# Plot contributions of genotypes - with sign - unsorted
contrib(x = out2, ndim = 5, sort.value = FALSE)

# Plot biplot with factoextra
fviz_contrib(out2$raw.out, choice = "var", axes = 1)
fviz_contrib(out2$raw.out, choice = "var", axes = 2)


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

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


# Plot contributions of genotypes - sorted
contrib(x = out3, ndim = 5)

# Plot contributions of genotypes - without sign - sorted
contrib(x = out3, ndim = 5, use.sign = FALSE)

# Plot loadings/coordinates of genotypes - sorted
contrib(x = out3, ndim = 5, plot.loadings = TRUE)

# Plot contributions of genotypes - with sign - unsorted
contrib(x = out3, ndim = 5, sort.value = FALSE)

# Plot biplot with factoextra
# fviz_contrib(out3$raw.out, choice = "quanti.var", axes = 1)
# fviz_contrib(out3$raw.out, choice = "quali.var", axes = 1)
# fviz_contrib(out3$raw.out, choice = "quanti.var", axes = 2)
# fviz_contrib(out3$raw.out, choice = "quali.var", axes = 2)

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