optim.diff.norm: performs the M step for measurement density parameters in...

In LCAextend: Latent Class Analysis (LCA) with Familial Dependence in Extended Pedigrees

Description

Estimates the mean mu and parameters of the variance-covariance matrix sigma of a multinormal distribution for the measurements with a general variance-covariance matrix identical for all classes.

Usage

optim.diff.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 identical for all classes. Treatment of covariates is not yet implemented, and any provided covariate value will be ignored.

Value

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.diff.norm(y[status==2,],status,weight,param.cont,x=NULL,
                 var.list=NULL)

LCAextend documentation built on May 2, 2019, 2:02 a.m.