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Plots a cross-correlation table

Source: R/gr-LDA.R
plot_CV2.Rd

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 LDA
plot_CV2(x, ...)

# S3 method for 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)
#> 'norm=TRUE' is used and this may be troublesome. See ?efourier #Details
trilo.l <- LDA(PCA(trilo.f), 'onto')
#> 8 PC retained
trilo.l
#>  * Cross-validation table ($CV.tab):
#>       classified
#> actual  a  b  c  d
#>      a  0  5  2  0
#>      b  3 12  1  0
#>      c  0  3 11  4
#>      d  0  0  3  6
#> 
#>  * Class accuracy ($CV.ce):
#>         a         b         c         d 
#> 0.0000000 0.7500000 0.6111111 0.6666667 
#> 
#>  * Leave-one-out cross-validation ($CV.correct): (58% - 29/50): 
plot_CV2(trilo.l)


# olea example
op <- opoly(olea, 5)
#> 'nb.pts' missing and set to 91
opl <- LDA(PCA(op), 'var')
#> 4 PC retained
plot_CV2(opl)