Likelihood: Compute the log-likelihood of a parameter
In ElstonStewart: Elston-Stewart Algorithm

Description Usage Arguments Details Value See Also Examples

Compute the log-likelihood of a parameter theta, given a list of pedigrees and a modele, using multiple cores and memoization

Likelihood(ped.set, modele, theta, 
                  n.cores = getOption("mc.cores", 2L), 
                  optim.alloc = TRUE, sum.likelihoods = TRUE,
                  PSOCK = Sys.info()["sysname"] != "Linux")

`ped.set`	a list of pedigrees, each created with `es.pedigree`
`modele`	a modele
`theta`	a parameter for the modele
`n.cores`	number of cores on which to run the computation
`optim.alloc`	cf Details
`sum.likelihoods`	cf Value
`PSOCK`	Use PSOCK cluster instead of fork cluster (defaults to `TRUE` on non-Linux)

The parameter theta will be given to the functions in modele to compute the likelihoods.

If n.cores > 1 a cluster is created and left open for futur use with the same parameters ped.set and modele. Open clusters can be closed with es.stopCluster().

If optim.alloc = TRUE, the function tries to optimize the distribution of the computation (if n.cores > 1) between the cluster nodes. This has no effect on the first run for given parameters ped.set and modele, but will reduce running time if the algorithm is ran several times with different values of theta and same parameters ped.set and modele. This is typically usefull for likelihood maximization.

If sum.likelihoods = TRUE, the function returns a single value, the sum of the log-likelihoods computed for each pedigree of ped.set. Else, the function returns a vector containing these log-likelihoods.

Elston, es.pedigree, es.stopCluster

data(fams)

# this data frame contains various families
# getting their famid
fam.ids <- unique(fams$fam);

# creating a list of genotypes corresponding to all individuals in fams
# NA -> 0, 1 or 2
genotypes <- lapply( fams$genotype, function(x) if(is.na(x)) 0:2 else x )

# creating a list of es.pedigree objects
X <- vector("list", length(fam.ids))
for(i in seq_along(fam.ids))
{
  w <- which(fams$fam == fam.ids[i])
  X[[i]] <- es.pedigree( id = fams$id[w], father = fams$father[w],
      mother = fams$mother[w], sex = fams$sex[w], pheno = rep(0, length(w)), 
      geno = genotypes[w], famid = fam.ids[i] )
}

## Not run: # computing the likelihood for a single value p
Likelihood(X, modele.di, theta = list( p=0.5), n.cores=1 )

# computing the likelihood for a vector p (Elston-Stewart is ran only once!)
p <- seq(0,1,length=501)
L <- Likelihood(X, modele.di, theta = list( p=p ), n.cores=1 ) 
plot( p, exp(L), type="l")

# running an optimization algorithm
# Elston-Stewart is ran several times
# here we run the algorithm with 2 cores
L <- function(p) -Likelihood(X, modele.di, theta = list( p=p ), n.cores=2 ) 
optimize(L , c(0.35,0.45) )
## End(Not run)