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Generate Biplots from pcss.core Output

Source: R/biplot.pcss.core.R
biplot.pcss.core.Rd

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.

Value

A list of biplots as ggplot objects.

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.

See also

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

Examples


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

library(EvaluateCore)
#> 
#> --------------------------------------------------------------------------------
#> Welcome to EvaluateCore version 0.1.3
#> 
#> 
#> # To know whats new in this version type:
#>   news(package='EvaluateCore')
#>   for the NEWS file.
#> 
#> # To cite the methods in the package type:
#>   citation(package='EvaluateCore')
#> 
#> # To suppress this message use:
#>   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)
library(factoextra)
#> Loading required package: ggplot2
#> Welcome! Want to learn more? See two factoextra-related books at https://goo.gl/ve3WBa

#~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
# 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)
#> $`Dim 1 vs. Dim 2`

#> 
#> $`Dim 1 vs. Dim 3`
#> Warning: ggrepel: 4 unlabeled data points (too many overlaps). Consider increasing max.overlaps

#> 
#> $`Dim 2 vs. Dim 3`

#> 

# 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)
#> $`Dim 1 vs. Dim 2`
#> Warning: ggrepel: 30 unlabeled data points (too many overlaps). Consider increasing max.overlaps

#> 
#> $`Dim 1 vs. Dim 3`
#> Warning: ggrepel: 51 unlabeled data points (too many overlaps). Consider increasing max.overlaps

#> 
#> $`Dim 2 vs. Dim 3`
#> Warning: ggrepel: 50 unlabeled data points (too many overlaps). Consider increasing max.overlaps

#> 

# 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)
#> $`Dim 1 vs. Dim 2`
#> Warning: ggrepel: 77 unlabeled data points (too many overlaps). Consider increasing max.overlaps

#> 
#> $`Dim 1 vs. Dim 3`
#> Warning: ggrepel: 68 unlabeled data points (too many overlaps). Consider increasing max.overlaps

#> 
#> $`Dim 2 vs. Dim 3`
#> Warning: ggrepel: 69 unlabeled data points (too many overlaps). Consider increasing max.overlaps

#> 

# Plot biplot with FactoMineR
plot(out3$raw.out, choix=c("ind"), label  = "none", axes = c(1, 2))
#> Warning: ggrepel: 1662 unlabeled data points (too many overlaps). Consider increasing max.overlaps
#> Warning: ggrepel: 65 unlabeled data points (too many overlaps). Consider increasing max.overlaps


plot(out3$raw.out, choix=c("ind"), label  = "none", axes = c(1, 3))
#> Warning: ggrepel: 1671 unlabeled data points (too many overlaps). Consider increasing max.overlaps
#> Warning: ggrepel: 59 unlabeled data points (too many overlaps). Consider increasing max.overlaps


plot(out3$raw.out, choix=c("ind"), label  = "none", axes = c(2, 3))
#> Warning: ggrepel: 1663 unlabeled data points (too many overlaps). Consider increasing max.overlaps
#> Warning: ggrepel: 58 unlabeled data points (too many overlaps). Consider increasing max.overlaps


# 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))