optim.probs: performs the M step of the EM algorithm for the probability...

In abureau/LCAextend: Latent Class Analysis (LCA) with familial dependence in extended pedigrees

Description

estimates the probability parameters (p, p.trans, p0,...) in the M step of the EM algorithm in both cases with and without familial dependence. This is an internal function not meant to be called by the user.

Usage

optim.probs(ped, probs, optim.probs.indic = c(TRUE, TRUE, TRUE, TRUE), 
            res.weight, famdep = TRUE)

Arguments

ped	a matrix of pedigrees data, see e.step for more details,
probs	all probability parameters to be optimized,
optim.probs.indic	a vector of logical values indicating which probability parameters to be updated,
res.weight	a matrix of n times K, individual weights, where n is the number of individuals and K is the total number of latent classes in the model, resulting from the E step of the EM algorithm (see e.step)
famdep	a logical variable indicating if familial dependence model is used or not. Default is TRUE.

Details

explicit estimators are computed in function of the weights.

Value

the function returns the estimated probs of all probability parameters.

References

TAYEB et al.: Solving Genetic Heterogeneity in Extended Families by Identifying Sub-types of Complex Diseases. Computational Statistics, 2011, DOI: 10.1007/s00180-010-0224-2.

Examples

#data
data(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)
#the function
optim.probs(ped.cont,probs,weight,optim.probs.indic=
            c(TRUE,TRUE,TRUE,TRUE),famdep=TRUE)

abureau/LCAextend documentation built on May 3, 2019, 9:41 p.m.