optim.noconst.ordi: performs the M step for the measurement distribution...

Description Usage Arguments Details Value Examples

View source: R/optim.noconst.ordi.R

Description

Estimates the cumulative logistic coefficients alpha in the case of multinomial (or ordinal) data without constraint on the coefficients.

Usage

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optim.noconst.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 distribution parameters, here is a list alpha of cumulative logistic coefficients,

x

a matrix of covariates (optional). Default is NULL,

var.list

a list of integers indicating which covariates (taken from x) are used for a given type of measurment.

Details

The values of explicit estimators are computed by logistic transformation of weighted empirical frequencies.

Value

the function returns a list of estimated parameters param.

Examples

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#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.noconst.ordi(y[status==2,],status,weight,param.ordi,x=NULL,
                   var.list=NULL)

abureau/LCAextend documentation built on May 3, 2019, 9:41 p.m.