# optim.const.ordi {LCAextend}

### 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