mcd2: Multinomial Canonical Decomposition Model for a multinomial...

In lmap: Logistic Mapping

Multinomial Canonical Decomposition Model for a multinomial outcome

Description

The function mcd2 fits the multinomial canonical decomposition model to a multinomial outcome i.e. a double constrained reduced rank multinomial logistic model

Usage

mcd2(X, G, Z, S = 2, trace = TRUE, maxiter = 65536, dcrit = 1e-06)

Arguments

X	An N by P matrix with predictor variables
G	An N times C class indicator matrix
Z	design matrix for response
S	Positive number indicating the dimensionality of teh solution
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 mcd with components:

call	function call
Xoriginal	Matrix X from input
G	Class indicator matrix G
X	Scaled X matrix
mx	Mean values of X
sdx	Standard deviations of X
pnames	Variable names of profiles
xnames	Variable names of predictors
znames	Variable names of responses
Z	Design matrix Z
m	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 = mcd1(X, Y, S = 2, ord.z = 2)

## End(Not run)

lmap documentation built on April 3, 2025, 5:47 p.m.