lmap (version 0.2.4)

clpca: Cumulative Logistic (Restricted) PCA

Description

Cumulative Logistic (Restricted) PCA

Usage

clpca(
  Y,
  X = NULL,
  S = 2,
  start = NULL,
  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

start

Starting values for U or B and V

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)
}