optim.noconst.ordi {LCAextend} | R Documentation |
performs the M step for the measurement distribution parameters in multinomial case without constraint on the parameters
Description
Estimates the cumulative logistic coefficients alpha
in the case of multinomial (or ordinal) data without constraint on the coefficients.
Usage
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 |
param |
a list of measurement distribution parameters, here is a list |
x |
a matrix of covariates (optional). Default is |
var.list |
a list of integers indicating which covariates (taken from |
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
#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)