plot.PCA.Bootstrap: Plots the Bootstrap information for Principal Components...

In MultBiplotR: Multivariate Analysis Using Biplots in R

Plots the Bootstrap information for Principal Components Analysis (PCA)

Description

Plots an object of class "PCA.Bootstrap"

Usage

## S3 method for class 'PCA.Bootstrap'
plot(x, Eigenvalues = TRUE, 
Inertia = FALSE, EigenVectors = TRUE, Structure = TRUE, 
Squared = TRUE, Scores = TRUE, ColorInd = "black", TypeScores = "ch", ...)

Arguments

x	An object of class "PCA.Bootstrap"
Eigenvalues	Should the information for the eigenvalues be plotted?
Inertia	Should the information for the inertia be plotted?
EigenVectors	Should the information for the eigenvectors be plotted?
Structure	Should the information for the correlations (variables-dimensions) be plotted?
Squared	Should the information for the correlations (variables-dimensions) be plotted?
Scores	Should the row (individual) scores be plotted?
ColorInd	Colors for the rows
TypeScores	Type of plot for the scores
...	Any other graphical argument

Details

For each parameter, box-plots and confidence intervals are plotted. The initial estimator and the bootstrap mean are plotted.

For the eigenvectors, loadings and contributions, the graph is divided into as many rows as dimensions, each row contains a plot of the hole set of variables.

The scores are plotted on a two dimensional

Value

No value returned

Author(s)

Jose Luis Vicente Villardon

References

Daudin, J. J., Duby, C., & Trecourt, P. (1988). Stability of principal component analysis studied by the bootstrap method. Statistics: A journal of theoretical and applied statistics, 19(2), 241-258.

Chateau, F., & Lebart, L. (1996). Assessing sample variability in the visualization techniques related to principal component analysis: bootstrap and alternative simulation methods. COMPSTAT, Physica-Verlag, 205-210.

Babamoradi, H., van den Berg, F., & Rinnan, Å. (2013). Bootstrap based confidence limits in principal component analysis: A case study. Chemometrics and Intelligent Laboratory Systems, 120, 97-105.

Fisher, A., Caffo, B., Schwartz, B., & Zipunnikov, V. (2016). Fast, exact bootstrap principal component analysis for p> 1 million. Journal of the American Statistical Association, 111(514), 846-860.

See Also

PCA.Bootstrap

Examples

X=wine[,4:21]
grupo=wine$Group
rownames(X)=paste(1:45, grupo, sep="-")
pcaboot=PCA.Bootstrap(X, dimens=2, Scaling = "Standardize columns", B=1000)
plot(pcaboot, ColorInd=as.numeric(grupo))
summary(pcaboot)

MultBiplotR documentation built on Nov. 21, 2023, 5:08 p.m.