# performs the computation of unnormalized triplet and individuals weights for a nuclear family in the pedigree 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. ```r 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. ## Returns 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 ```r #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) ```

weight.nuc function