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.

1 | ```
weight.famdep(id, dad, mom, status, probs, fyc, peel)
``` |

`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 |

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

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.

the function returns a list of 3 elements:

`ww` |
triplet weights: an array of |

`w` |
individual weights: an array of |

`ll` |
log-likelihood. |

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 `upward`

, `downward`

, `e.step`

.

1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 | ```
#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)
``` |

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