MultBiplotR (version 23.11.0)

plot.PCA.Bootstrap: Plots the Bootstrap information for Principal Components Analysis (PCA)

Description

Plots an object of class "PCA.Bootstrap"

Usage

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

Value

No value returned

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

Author

Jose Luis Vicente Villardon

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

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)