weight.nuc: performs the computation of unnormalized triplet and...

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

Description

the weighting algorithm proceeds by nuclear family, the function weight.nuc computes the unnormalized triplet and individuals weights for a nuclear family in the pedigree. This is an internal function not meant to be called by the user.

Usage

weight.nuc(connect, spouse.connect, children.connect, status, 
probs, fyc, p.ybarF.c, ww, w, res.upward)

Arguments

connect	a connector in the pedigree,
spouse.connect	spouse of the connector,
children.connect	children of the connector,
status	vector of symptom status of the whole pedigree,
probs	all 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,
p.ybarF.c	a array of dimension n times 2 times K+1 giving the probability of observations above the individual, depending on his status and his class and conditionally on his class,
ww	unnormalized triplet weights, an array of n times 2 times K+1 times K+1 times K+1, where n is the number of individuals and K is the total number of latent classes in the model, see e.step,
w	unnormalized individual weights, an array of n times 2 times K+1, see e.step,
res.upward	result of the upward step of the weighting algorithm, see upward,

Details

updated ww and w are computed for the current nuclear family.

Value

the function returns a list of 2 elements:

ww	unnormalized triplet weights, an array of n times 2 times K+1 times K+1 times K+1, see e.step,
w	unnormalized individual weights, an array of n times 2 times K+1, see e.step.

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 downward

Examples

#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]]
#standardize id to be 1, 2, 3, ...
id.origin <- id
standard <- function(vec) ifelse(vec%in%id.origin,which(id.origin==vec),0)
id <- apply(t(id),2,standard)
dad <- apply(t(dad),2,standard)
mom <- apply(t(mom),2,standard)
peel$couple <- cbind(apply(t(peel$couple[,1]),2,standard),
                     apply(t(peel$couple[,2]),2,standard))
for(generat in 1:peel$generation)
peel$peel.connect[generat,] <- apply(t(peel$peel.connect[generat,]),2,standard)
#the first nuclear family
generat <- peel$generation
connect <- peel$peel.connect[generat,]
connect <- connect[connect>0]
spouse.connect <- peel$couple[peel$couple[,1]==connect,2]
children.connect <- union(id[dad==connect],id[mom==connect])
#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))
#triplet and individual weights
ww <- array(0,dim=c(length(id),rep(2,3),rep(length(probs$p)+1,3)))
w <- array(0,dim=c(length(id),2,length(probs$p)+1))
#probability of the observations below
p.ybarF.c <- array(1,dim=c(length(id),2,length(probs$p)+1))
p.ybarF.c[connect,,] <- p.post.found(connect,status,probs,fyc)
#the upward step
res.upward <- upward(id,dad,mom,status,probs,fyc,peel)
#the function
weight.nuc(connect,spouse.connect,children.connect,status,probs,fyc,
           p.ybarF.c,ww,w,res.upward)

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