Supervised PCA {MXM} | R Documentation |
Supervised PCA
Description
Supervised PCA.
Usage
supervised.pca(target, dataset, indices, center = TRUE, scale = TRUE,
colours = NULL, graph = TRUE)
Arguments
target |
A numerical vector or a factor denoting the class of each sample, the response variable. |
dataset |
A matrix with numerical data (the predictor variables). |
indices |
A vector with indices denoting whcih variables have been selected. |
center |
In the calculation of the PCA, should the data be centered? Default value is TRUE. |
scale |
In the calculation of the PCA, should the data be scaled to unity variance? Default value is TRUE. |
colours |
Should the colour of the points be defined by the target variable or do you want to pass your own colours? This must be a vector whose length is equal to the length of the target. |
graph |
Should two graphs be returned? The scores of the frist two principal components based on all the data and based on the selected variables. |
Details
This is not exactly the standard supervised PCA as suggested by Bair et al (2006). What we do here essentially is the following: PCA on all variables and on the variables selected by a variable selection algortihm.
Value
A list including:
mod.all |
The output returned by |
mode.sel |
The output returned by |
var.percent |
The percentage of variance explained by the selected variables. |
Author(s)
Michail Tsagris
R implementation and documentation: Michail Tsagris mtsagris@uoc.gr
References
Bair E., Hastie T., Debashis P. and Tibshirani R. (2006). Prediction by supervised principal components. Journal of the American Statistical Association 101(473): 119–137.
See Also
Examples
x <- as.matrix(iris[, 1:4])
target <- iris[, 5]
supervised.pca(target, x, indices = 1:2)