lmdu {lmap}

Logistic (Restricted) MDU

Description

This function runs: logistic multidimensional unfolding (if X = NULL) logistic restricted multidimensional unfolding (if X != NULL)

Usage

lmdu(
  Y,
  f = NULL,
  X = NULL,
  S = 2,
  start = "svd",
  maxiter = 65536,
  dcrit = 1e-06
)

Arguments

Y

An N times R binary matrix .

f

Vector with frequencies of response patterns in Y (only applicable if (X = NULL))

X

An N by P matrix with predictor variables

S

Positive number indicating the dimensionality of the solution

start

Either user provided starting values (start should be a list with U and V) or a way to compute starting values (choices: random, svd, ca)

maxiter

maximum number of iterations

dcrit

convergence criterion

Value

deviance

call

Call to the function

Yoriginal

Matrix Y from input

Y

Matrix Y from input

f

frequencies of rows of Y

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

npar

number of estimated parameters

AIC

Akaike's Information Criterion

BIC

Bayesian Information Criterion

Examples

## Not run: 
data(dataExample_lmdu)
Y = as.matrix(dataExample_lmdu[ , 1:8])
X = as.matrix(dataExample_lmdu[ , 9:13])
# unsupervised
output = lmdu(Y = Y, S = 2)
# supervised
output2 = lmdu(Y = Y, X = X, S = 2)

## End(Not run)