# optim.equal.norm {LCAextend}

### Description

Estimates the mean `mu`

and parameters of the variance-covariance matrix `sigma`

of a multinormal distribution for the measurements with equal variance for all measurements and equal covariance between all pairs of measurements within each class. The variance and covariance parameters are however distinct for each class.

### Usage

optim.equal.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`

. The variance-covariance matrices `sigma`

are distinct for each class. 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.equal.norm(y[status==2,],status,weight,param.cont,x=NULL, var.list=NULL)

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