plot_CV2: Plots a cross-correlation table

In Momocs: Morphometrics using R

Plots a cross-correlation table

Description

Or any contingency/confusion table. A simple graphic representation based on variable width and/or color for arrows or segments, based on the relative frequencies.

Usage

plot_CV2(x, ...)

## S3 method for class 'LDA'
plot_CV2(x, ...)

## S3 method for class 'table'
plot_CV2(
  x,
  links.FUN = arrows,
  col = TRUE,
  col0 = "black",
  col.breaks = 5,
  palette = col_heat,
  lwd = TRUE,
  lwd0 = 5,
  gap.dots = 0.2,
  pch.dots = 20,
  gap.names = 0.25,
  cex.names = 1,
  legend = TRUE,
  ...
)

Arguments

x	an LDA object, a table or a squared matrix
...	useless here.
links.FUN	a function to draw the links: eg segments (by default), arrows, etc.
col	logical whether to vary the color of the links
col0	a color for the default link (when col = FALSE)
col.breaks	the number of different colors
palette	a color palette, eg col_summer, col_hot, etc.
lwd	logical whether to vary the width of the links
lwd0	a width for the default link (when lwd = FALSE)
gap.dots	numeric to set space between the dots and the links
pch.dots	a pch for the dots
gap.names	numeric to set the space between the dots and the group names
cex.names	a cex for the names
legend	logical whether to add a legend

Value

a ggplot2 object

Note

When freq=FALSE, the fill colors are not weighted within actual classes and should not be displayed if classes sizes are not balanced.

See Also

LDA, plot.LDA, plot_CV.

Examples

# Below various table that you can try. We will use the last one for the examples.
#pure random
a <- sample(rep(letters[1:4], each=10))
b <- sample(rep(letters[1:4], each=10))
tab <- table(a, b)

# veryhuge + some structure
a <- sample(rep(letters[1:10], each=10))
b <- sample(rep(letters[1:10], each=10))
tab <- table(a, b)
diag(tab) <- round(runif(10, 10, 20))

tab <- matrix(c(8, 3, 1, 0, 0,
                2, 7, 1, 2, 3,
                3, 5, 9, 1, 1,
                1, 1, 2, 7, 1,
                0, 9, 1, 4, 5), 5, 5, byrow=TRUE)
tab <- as.table(tab)

# good prediction
tab <- matrix(c(8, 1, 1, 0, 0,
               1, 7, 1, 0, 0,
                1, 2, 9, 1, 0,
                1, 1, 1, 7, 1,
                0, 0, 0, 1, 8), 5, 5, byrow=TRUE)
tab <- as.table(tab)


plot_CV2(tab)
plot_CV2(tab, arrows) # if you prefer arrows
plot_CV2(tab, lwd=FALSE, lwd0=1, palette=col_india) # if you like india but not lwds
plot_CV2(tab, col=FALSE, col0='pink') # only lwd
plot_CV2(tab, col=FALSE, lwd0=10, cex.names=2) # if you're getting old
plot_CV2(tab, col=FALSE, lwd=FALSE) # pretty but useless
plot_CV2(tab, col.breaks=2) # if you think it's either good or bad
plot_CV2(tab, pch=NA) # if you do not like dots
plot_CV2(tab, gap.dots=0) # if you want to 'fill the gap'
plot_CV2(tab, gap.dots=1) # or not

#trilo examples
trilo.f <- efourier(trilo, 8)
trilo.l <- LDA(PCA(trilo.f), 'onto')
trilo.l
plot_CV2(trilo.l)

# olea example
op <- opoly(olea, 5)
opl <- LDA(PCA(op), 'var')
plot_CV2(opl)

Momocs documentation built on Nov. 13, 2023, 5:07 p.m.