esm {lmap}

Extended Stereotype Model

Description

The function esm performs extended stereotype model analysis for multivariate logistic analysis i.e. a double constrained reduced rank multinomial logistic model

Usage

esm(
  X,
  Y,
  S = 2,
  Z = NULL,
  W = NULL,
  ord.z = 1,
  ord.m = R,
  scale.x = FALSE,
  trace = FALSE,
  maxiter = 65536,
  dcrit = 1e-06
)

Arguments

X

An N by P matrix with predictor variables

Y

An N times R binary matrix .

S

Positive number indicating the dimensionality of teh solution

Z

design matrix for response

W

design matrix for intercepts

ord.z

if Z = NULL, the function creates Z having order ord.z

ord.m

if W = NULL, the function creates W having order ord.m

scale.x

whether X should be scaled to zero mean and standard deviation one

trace

whether progress information should be printed on the screen

maxiter

maximum number of iterations

dcrit

convergence criterion

Value

This function returns an object of the class esm with components:

call

function call

Xoriginal

Matrix X from input

X

Scaled X matrix

mx

Mean values of X

sdx

Standard deviations of X

Y

Matrix Y from input

pnames

Variable names of profiles

xnames

Variable names of predictors

znames

Variable names of responses

Z

Design matrix Z

W

Design matrix W

G

Profile indicator matrix G

m

main effects

bm

regression weights for main effects

Bx

regression weights for X

Bz

regression weights for Z

A

regression weights (Bx Bz')

U

matrix with coordinates for row-objects

V

matrix with coordinates for column-objects

Ghat

Estimated values of G

deviance

value of the deviance at convergence

df

number of paramters

AIC

Akaike's informatoin criterion

iter

number of main iterations from the MM algorithm

svd

Singular value decomposition in last iteration

Examples

## Not run: 
data(dataExample_lpca)
Y = as.matrix(dataExample_lpca[ , 1:5])
X = as.matrix(dataExample_lpca[ , 9:13])
#unsupervised
output = esm(X, Y, S = 2, ord.z = 2)

## End(Not run)