CEPlot {PCADSC}

Cumulative eigenvalue plot

Description

Produce a cumulative eigenvalue (CE) plot from a full or partial PCADSC object, as obtained from a call to PCADSC. In either case, this PCADSC object must have a non-NULL CEInfo slot (see examples). The CE plot compares the eigenvalues obtained from PCA performed separately and jointly on two datasets that consist of different observations of the same variables.

Usage

CEPlot(x, nDraw = NULL)

Arguments

x

x A PCADSC or angleInfo object, as produced by PCADSC or doAngle, respectively.

nDraw

A positive integer. The number of simulated cumulative eigenvalue curves that should be added to the plot.

Details

In the x-coordinates, cumulative differences in eigenvalues are shown, while the y-coordinates are the cumulative sum of the joint eigenvalues. The plot is annotated with Kolmogorov-Smirnov and Cramer-von Mises tests evaluated by permutation tests, testing the null hypothesis of no difference in eigenvalues. The plot also features a number of cumulative simulated cumulative eigenvalue curves as dashed lines. Moreover, a shaded area presents pointwise 95 % confidence bands for the cumulative difference, also obtained using the permutation test.

See Also

PCADSC, doCE

Examples

#load iris data
data(iris)

#Define grouping variable, grouping the observations by whether their species is
#Setosa or not
iris$group <- "setosa"
iris$group[iris$Species != "setosa"] <- "non-setosa"
iris$Species <- NULL

## Not run: 
#make a PCADSC object, splitting the data by "group"
irisPCADSC <- PCADSC(iris, "group")

#make a partial PCADSC object from iris and fill out CEInfo in the next call
irisPCADSC2 <- PCADSC(iris, "group", doCE = FALSE)
irisPCADSC2 <- doCE(irisPCADSC2)

#make a CE plot
CEPlot(irisPCADSC)
CEPlot(irisPCADSC2)

## End(Not run)

#Only do CE information and use less resamplings for a faster runtime
irisPCADSC_fast <- PCADSC(iris, "group", doAngle = FALSE, doChroma = FALSE,
  B = 1000)
CEPlot(irisPCADSC_fast)