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optim.const.ordi {LCAextend}

performs the M step for the measurement distribution parameters in multinomial case, with an ordinal constraint on the parameters
Package: 
LCAextend
Version: 
1.2

Description

Estimates the cumulative logistic coefficients alpha in the case of multinomial (or ordinal) data with an ordinal constraint on the parameters.

Usage

optim.const.ordi(y, status, weight, param, x = NULL, var.list = NULL)

Arguments

y
a matrix of discrete (or ordinal) measurements (only for symptomatic subjects),
status
symptom status of all individuals,
weight
a matrix of n times K of individual weights, where n is the number of individuals and K is the total number of latent classes in the model,
param
a list of measurement density parameters, here is a list of alpha,
x
a matrix of covariates (optional). Default id NULL,
var.list
a list of integers indicating which covariates (taken from x) are used for a given type of measurement

Details

the constraint on the parameters is that, for a symptom j, the rows alpha[[j]][k,] are equal for all classes k except the first values. Therefore, maximum likelihood estimators are not explicit and the function lrm of the package rms is used to perform a numerical optimization.

Values

The function returns a list of estimated parameters param satisfying the constraint.

Examples

#data
data(ped.ordi)
status <- ped.ordi[,6]
y <- ped.ordi[,7:ncol(ped.ordi)]
data(peel)
#probs and param
data(probs)
data(param.ordi)
#e step
weight <- e.step(ped.ordi,probs,param.ordi,dens.prod.ordi,peel,x=NULL,
                 var.list=NULL,famdep=TRUE)$w
weight <- matrix(weight[,1,1:length(probs$p)],nrow=nrow(ped.ordi),
                 ncol=length(probs$p))
#the function
optim.const.ordi(y[status==2,],status,weight,param.ordi,x=NULL,
                 var.list=NULL)

Documentation reproduced from package LCAextend, version 1.2. License: GPL