Description Usage Arguments Details Author(s) References See Also Examples
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.
1 2 3 | ggtladleplot(x, crit = "gn", type = "l", scales = "free",
position = "horizontal", ylab = crit,
xlab = "component", main = deparse(substitute(x)), ...)
|
x |
an object of class ladle. |
crit |
the criterion to be plotted, options are |
type |
plotting type, either lines |
position |
placement of augmentation plots for separate modes, options are |
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, |
ylab |
default ylab value. |
xlab |
default xlab value. |
main |
default title. |
... |
other arguments for the plotting functions. |
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.
Klaus Nordhausen, Joni Virta
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>
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 | 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")
|
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