LCAextend (version 1.3)

weight.famdep: performs the computation of triplet and individual weights for a pedigree under familial dependence

Description

computes the triplet and the individual weights of the E step of the EM algorithm for a pedigree in the case of familial dependence. It returns also the overall log-likelihood of the observations. This is an internal function not meant to be called by the user.

Usage

weight.famdep(id, dad, mom, status, probs, fyc, peel)

Arguments

id

individual ID of the pedigree,

dad

dad ID,

mom

mom ID,

status

symptom status: (2: symptomatic, 1: without symptoms, 0: missing),

probs

list of probability parameters of the model,

fyc

a matrix of n times K+1 given the density of observations 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,

peel

a list of pedigree peeling containing connectors by peeling order and couples of parents

Value

the function returns a list of 3 elements:

ww

triplet weights: an array of n times 2 times K+1 times K+1 times K+1, see e.step,

w

individual weights: an array of n times 2 times K+1, see e.step,

ll

log-likelihood.

Details

the function calls the functions upward and downward which perform the required probability computations by processing the pedigree by nuclear family (or equivalently by connector) following the peeling order.

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.

See Also

See also upward, downward, e.step.

Examples

# NOT RUN {
#data
data(ped.cont)
data(peel)
fam <- ped.cont[,1]
id <- ped.cont[fam==1,2]
dad <- ped.cont[fam==1,3]
mom <- ped.cont[fam==1,4]
status <- ped.cont[fam==1,6]
y <- ped.cont[fam==1,7:ncol(ped.cont)]
peel <- peel[[1]]
#probs and param
data(probs)
data(param.cont)
#densities of the observations
fyc <- matrix(1,nrow=length(id),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
weight.famdep(id,dad,mom,status,probs,fyc,peel)
# }