p.post.found: computes the posterior probability of observations of a...

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

Description

computes the posterior probability of measurements of a founder for each class and each symptom status of the founder. This is an internal function not meant to be called by the user.

Usage

p.post.found(found, status, probs, fyc)

Arguments

found	a founder in the pedigree (individual without parents in the pedigree),
status	the symptom status vector of the whole pedigree,
probs	a list of all probability parameters of the model,
fyc	a matrix of n times K+1 given the density of measurements of each individual if allocated to class k, where n is the number of individuals and K is the total number of latent classes in the model,

Value

the function returns p.found a matrix of 2 times K+1 entries: p.found[s,k] is the posterior probability of the observations Y_found under status S_found=s and when he is assigned to class k.

References

TAYEB et al.: Solving Genetic Heterogeneity in Extended Families by Identifying Sub-types of Complex Diseases. Computational Statistics, 2001, DOI: 10.1007/s00180-010-0224-2.

Examples

#data
data(ped.cont)
fam <- ped.cont[,1]
dad <- ped.cont[fam==1,3]
status <- ped.cont[fam==1,6]
y <- ped.cont[fam==1,7:ncol(ped.cont)]
#a founder
found <- which(dad==0)[1]
data(probs)
data(param.cont)
#densities of the observations
fyc <- matrix(1,nrow=nrow(y),ncol=length(probs$p)+1)
fyc[status==2,1:length(probs$p)] <- t(apply(y[status==2,],1,dens.norm,
                                            param.cont,NULL))
#the function
p.post.found(found,status,probs,fyc)

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