clpca {lmap} | R Documentation |
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
)
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 |
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
Examples
## Not run:
data(dataExample_clpca)
Y<-as.matrix(dataExample_clpca[,5:8])
X<-as.matrix(dataExample_clpca[,1:4])
out = clpca(Y)
out = clpca(Y, X)
## End(Not run)