tensorBSS (version 0.3.9)

ggtladleplot: Ladle plot for each mode of an object of class tladle using ggplot2

Description

The ladle plot is a measure for deciding about the number of interesting components. Of interest for the ladle criterion is the minimum. The function here offers however also to plot other criterion values which are part of the actual ladle criterion.

Usage

ggtladleplot(x, crit = "gn", type = "l", scales = "free", 
  position = "horizontal", ylab = crit,  
  xlab = "component", main = deparse(substitute(x)), ...)

Arguments

x

an object of class ladle.

crit

the criterion to be plotted, options are "gn", "fn", "phin" and "lambda".

type

plotting type, either lines l or points p.

position

placement of augmentation plots for separate modes, options are "horizontal" and "vertical".

scales

determines whether the x- and y-axis scales are shared or allowed to vary freely across the subplots. The options are: both axes are free (the default, "free"), x-axis is free ("free_x"), y-axis is free ("free_y"), neither is free ("fixed").

ylab

default ylab value.

xlab

default xlab value.

main

default title.

...

other arguments for the plotting functions.

Author

Klaus Nordhausen, Joni Virta

Details

The main criterion of the ladle is the scaled sum of the eigenvalues and the measure of variation of the eigenvectors up to the component of interest.

The sum is denoted "gn" and the individual parts are "fn" for the measure of the eigenvector variation and "phin" for the scaled eigenvalues. The last option "lambda" corresponds to the unscaled eigenvalues yielding then a screeplot.

The plot is drawn separately for each mode of the data.

References

Koesner, C, Nordhausen, K. and Virta, J. (2019), Estimating the signal tensor dimension using tensorial PCA. Manuscript.

Luo, W. and Li, B. (2016), Combining Eigenvalues and Variation of Eigenvectors for Order Determination, Biometrika, 103. 875--887. <doi:10.1093/biomet/asw051>

See Also

tPCAladle

Examples

library(ICtest)
n <- 500
sig <- 0.6

Z <- rbind(sqrt(0.7)*rt(n,df=5)*sqrt(3/5),
           sqrt(0.3)*runif(n,-sqrt(3),sqrt(3)),
           sqrt(0.3)*(rchisq(n,df=3)-3)/sqrt(6),
           sqrt(0.9)*(rexp(n)-1),
           sqrt(0.1)*rlogis(n,0,sqrt(3)/pi),
           sqrt(0.5)*(rbeta(n,2,2)-0.5)*sqrt(20)
)

dim(Z) <- c(3, 2, n)

U1 <- rorth(12)[,1:3]
U2 <- rorth(8)[,1:2]
U <- list(U1=U1, U2=U2)
Y <- tensorTransform2(Z,U,1:2)
EPS <- array(rnorm(12*8*n, mean=0, sd=sig), dim=c(12,8,n))
X <- Y + EPS


TEST <- tPCAladle(X, n.boot = 100)
TEST
ggtladleplot(TEST, crit = "gn")
ggtladleplot(TEST, crit = "fn")
ggtladleplot(TEST, crit = "phin")
ggtladleplot(TEST, crit = "lambda")