createPCAmodelmatrix: Create Principal Component Analysis (PCA) scores matrix

In predkmeans: Covariate Adaptive Clustering

Description

Wrapper function for creating PCA scores to be used in a regression analysis.

Usage

createPCAmodelmatrix(
  data,
  ncomps,
  covarnames = colnames(data),
  center = TRUE,
  scale = TRUE,
  matrixonly = TRUE
)

Arguments

data	Matrix or data frame of data
ncomps	Number of PCA components to return.
covarnames	Names of variables or column numbers in data on which the PCA is to be run.
center	Logical indicator of whether data should be centered. Passed to prcomp.
scale	Logical indicator of whether data should be scaled. Passed to prcomp.
matrixonly	Logical indicator of whether only the model matrix should be returned, or the full output from prcomp.

Details

This is a wrapper around prcomp, which does the necessary computation.

Value

If matrixonly=TRUE, a matrix of PCA scores. Otherwise a list containing two elements: X, a matrix of scores, and pca, the output from prcomp.

Author(s)

Joshua Keller

See Also

createTPRSmodelmatrix, predkmeansCVest

Examples

n <- 100
d <- 15
X <- matrix(rnorm(n*d), ncol=d, nrow=n)
X <- as.data.frame(X)
mx <- createPCAmodelmatrix(data=X, ncomps=2)

predkmeans documentation built on Jan. 11, 2020, 9:29 a.m.