Enhanced Output for Principal and Sparse Principal Components
princmp(
formula,
data = environment(formula),
method = c("regular", "sparse"),
k = min(5, p - 1),
kapprox = min(5, k),
cor = TRUE,
sw = FALSE,
nvmax = 5
)a list of class princmp with elements scores, a k-column matrix with principal component scores, with NAs when the input data had an NA, and other components useful for printing and plotting. If k=1
scores is a vector. Other components include vars (vector of variances explained), method, k.
a formula with no left hand side, or a numeric matrix
a data frame or table. By default variables come from the calling environment.
specifies whether to use regular or sparse principal components are computed
the number of components to plot, display, and return
the number of components to approximate with stepwise regression when sw=TRUE
set to FALSE to compute PCs on the original data scale, which is useful if all variables have the same units of measurement
set to TRUE to run stepwise regression PC prediction/approximation
maximum number of predictors to allow in stepwise regression PC approximations
Frank Harrell
Expands any categorical predictors into indicator variables, and calls princomp (if method='regular' (the default)) or sPCAgrid in the pcaPP package (method='sparse') to compute lasso-penalized sparse principal components. By default all variables are first scaled by their standard deviation after observations with any NAs on any variables in formula are removed. Loadings of standardized variables, and if orig=TRUE loadings on the original data scale are printed. If pl=TRUE a scree plot is drawn with text added to indicate cumulative proportions of variance explained. If sw=TRUE, the leaps package regsubsets function is used to approximate the PCs using forward stepwise regression with the original variables as individual predictors.
A print method prints the results and a plot method plots the scree plot of variance explained.