# optim.indep.norm {LCAextend}

### Description

Estimates the mean `mu`

and parameters of the variance-covariance matrix `sigma`

of a multinormal distribution for the measurements with diagonal variance-covariance matrices for each class, i.e. measurements are supposed independent.

### Usage

optim.indep.norm(y, status, weight, param, x = NULL, var.list = NULL)

### Arguments

- y
- a matrix of continuous 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
`mu`

and`sigma`

, - 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 values of explicit estimators are computed for both `mu`

and `sigma`

. All variance-covariance matrices `sigma`

are diagonal, i.e. measurements are supposed independent. Treatment of covariates is not yet implemented, and any provided covariate value will be ignored.

### Values

The function returns a list of estimated parameters `param`

.

### Examples

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

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