lmap (version 0.1.3)

clpca: Cumulative Logistic (Restrcited) PCA

Description

Cumulative Logistic (Restrcited) PCA

Usage

clpca(
  Y,
  X = NULL,
  S = 2,
  lambda = FALSE,
  trace = FALSE,
  maxiter = 65536,
  dcrit = 1e-06
)

Value

Y Matrix Y from input

Xoriginal Matrix X from input

X Scaled X matrix

mx Mean values of X

sdx Standard deviations of X

ynames Variable names of responses

xnames Variable names of predictors

probabilities Estimated values of Y

m main effects

U matrix with coordinates for row-objects

B matrix with regression weight (U = XB)

V matrix with vectors for items/responses

iter number of main iterations from the MM algorithm

deviance value of the deviance at convergence

Arguments

Y

An N times R ordinal matrix .

X

An N by P matrix with predictor variables

S

Positive number indicating the dimensionality of the solution

lambda

if TRUE does lambda scaling (see Understanding Biplots, p24)

trace

tracing information during iterations

maxiter

maximum number of iterations

dcrit

convergence criterion

Examples

if (FALSE) {
data(dataExample_clpca)
Y<-as.matrix(dataExample_clpca[,5:8])
X<-as.matrix(dataExample_clpca[,1:4])
out = clpca(Y)
out = clpca(Y, X)
}