biplot.pcss.core: Generate Biplots from 'pcss.core' Output

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

Generate Biplots from pcss.core Output

Description

biplot.pcss.core generates biplots of scores of genotypes with or without vectors for traits from the output of pcss.core.

Usage

## S3 method for class 'pcss.core'
biplot(
  x,
  ndim = 3,
  highlight.core = c("size", "variance", "logistic", "none"),
  show.traits = c("all", "none", "quantitative", "qualitative"),
  qual.scale = 1,
  quant.scale = 1,
  point.alpha = 0.8,
  segment.alpha = 0.8,
  ...
)

Arguments

x	An object of class pcss.core.
ndim	The number of dimensions for which biplots have to plotted.
highlight.core	The core collection to be highlighted. Either "size", "variance", "logistic", or "none". See Details.
show.traits	Which kind of the traits to be shown in the biplot. Either "all", "none", "quantitative" or "qualitative".
qual.scale	A scale factor to be applied to qualitative trait coordinates plotted in biplot.
quant.scale	A scale factor to be applied to quantitative trait coordinates plotted in biplot.
point.alpha	Alpha transparency value for biplot points.
segment.alpha	Alpha transparency value for biplot segments.
...	Unused.

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. Use "none" to not highlight any accessions.

Value

A list of biplots as ggplot objects.

See Also

pcss.core, plot.PCA, plot.MCA, plot.FAMD, fviz_pca, fviz_mca, fviz_famd

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 biplot
biplot(out1, ndim = 3, highlight.core = "size", quant.scale = 3,
       point.alpha = 0.5)

# Plot biplot with FactoMineR
plot(out1$raw.out, choix=c("ind"), label  = "none", axes = c(1, 2))

plot(out1$raw.out, choix=c("ind"), label  = "none", axes = c(1, 3))

plot(out1$raw.out, choix=c("ind"), label  = "none", axes = c(2, 3))

# Plot biplot with factoextra
fviz_pca_biplot(out1$raw.out, geom.ind = "point", axes = c(1, 2))

fviz_pca_biplot(out1$raw.out, geom.ind = "point", axes = c(1, 3))

fviz_pca_biplot(out1$raw.out, geom.ind = "point", axes = c(2, 3))


#~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
# 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 biplot
biplot(out2, ndim = 3, highlight.core = "size", qual.scale = 1,
       point.alpha = 0.5)

# Plot biplot with FactoMineR
plot(out2$raw.out, choix=c("ind"), label  = "none", axes = c(1, 2))

plot(out2$raw.out, choix=c("ind"), label  = "none", axes = c(1, 3))

plot(out2$raw.out, choix=c("ind"), label  = "none", axes = c(2, 3))

# Plot biplot with factoextra
fviz_mca_biplot(out2$raw.out, geom.ind = "point", axes = c(1, 2))

fviz_mca_biplot(out2$raw.out, geom.ind = "point", axes = c(1, 3))

fviz_mca_biplot(out2$raw.out, geom.ind = "point", axes = c(2, 3))


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

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


# Plot biplot
biplot(out3, ndim = 3, highlight.core = "size",
       quant.scale = 3, qual.scale = 1,
       point.alpha = 0.5)

# Plot biplot with FactoMineR
plot(out3$raw.out, choix=c("ind"), label  = "none", axes = c(1, 2))

plot(out3$raw.out, choix=c("ind"), label  = "none", axes = c(1, 3))

plot(out3$raw.out, choix=c("ind"), label  = "none", axes = c(2, 3))

# Plot biplot with factoextra

# Fix rownames
row.names(out3$raw.out$quali.var$coord) <-
  unlist(lapply(seq_along(data[, qual]),
                function(i) paste(qual[i],
                                  levels(data[, qual[i]]), sep = "_")))

fviz_famd_ind(out3$raw.out, geom = "point", axes = c(1, 2))

fviz_famd_ind(out3$raw.out, geom = "point", axes = c(1, 3))

fviz_famd_ind(out3$raw.out, geom = "point", axes = c(2, 3))

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