Description: This package performs a Latent Class Analysis of phenotypic measurements in pedigrees and a model selection based on one of two methods: likelihood-based cross-validation and Bayesian Information Criterion. It computes also individual and triplet child-parents weights in a pedigree using an upward-downward algorithm. It takes into account the familial dependence defined by the pedigree structure by considering that a class of a child depends on his parents classes via triplet-transition probabilities of the classes. The package handles the case where measurements are available on all subjects and the case where measurements are available only on symptomatic (i.e. affected) subjects. Distributions for discrete (or ordinal) and continous data are currently implemented. The package can deal with missing data.
TAYEB, A. LABBE, A., BUREAU, A. and MERETTE, C. (2011) Solving Genetic Heterogeneity in Extended Families by Identifying Sub-types of Complex Diseases. Computational Statistics, 26(3): 539-560. DOI: 10.1007/s00180-010-0224-2,
LABBE, A., BUREAU, A. and MERETTE, C. (2009) Integration of Genetic Familial Dependence Structure in Latent Class Models. The International Journal of Biostatistics, 5(1): Article 6.
#data data(ped.cont) fam <- ped.cont[,1] #the function applied for the two first families of ped.cont res <- model.select(ped.cont[fam%in%1:2,],distribution="normal",trans.const=TRUE, optim.indep.norm,optim.probs.indic=c(TRUE,TRUE,TRUE,TRUE), famdep=TRUE,selec="bic",K.vec=1:3,tol=0.001,x=NULL,var.list=NULL) plot(1:3,res$bic,type="l",col="blue",xlab="K",ylab="BIC",main="model selection using BIC")
Documentation reproduced from package LCAextend, version 1.2. License: GPL