mrrr: Multinomial Reduced Rank Regression

In lmap: Logistic Mapping

Multinomial Reduced Rank Regression

Description

The function mrrr performs multinomial reduced rank regression for a nominal response variable and a set of predictor variables.

Usage

mrrr(y, X, S = 2, trace = FALSE, maxiter = 65536, dcrit = 1e-06, start = NULL)

Arguments

y	An N vector of the responses (categorical).
X	An N by P matrix with predictor variables
S	Positive number indicating the dimensionality of teh solution
trace	Boolean indicating whether a trace of the algorithm should be printed on the console.
maxiter	maximum number of iterations
dcrit	convergence criterion
start	start values. If start=NULL, the algorithm computes the start values.

Value

Xoriginal Matrix X from input

X Scaled X matrix

G class indicator matrix

ynames class names of response classes

xnames variable names of the predictors

mx means of the predictor variables

sdx standard deviations of the predictor variables

U coordinate matrix of row objects

B matrix with regression coefficients

V Class coordinate matrix

iters number of iterations

deviance value of the deviance at convergence

Examples

## Not run: 
data(dataExample_mru)
y = as.matrix(dataExample_mru[ , 1])
X = as.matrix(dataExample_mru[ , 2:6])
output = mrrr(y = y, X = X, S = 2)

## End(Not run)

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