meancss: computes statistics necessary for the evaluation of the log...
In bclust: Bayesian Hierarchical Clustering Using Spike and Slab Models

Description Usage Arguments Details Value See Also Examples

The function is useful for deriving arguments of the loglikelihood function.

1	meancss(x,rep.id=1:nrow(x))

`x`	The data matrix, subjects in rows, variables in columns.
`rep.id`	A vector of positive integers refering to replication of the types. The same integer is associated to the the replicates of the same type. Not specifying this vector preproposes that the data are unreplicated.

This function facilitates the usage of the loglikelihood function.

`mean`	The mean of types.
`css`	The corrected sum of squares of types.
`repno`	The vector containing the number of replications of types according to `rep.id`.

loglikelihood.

data(gaelle)
gaelle.id<-rep(1:14,c(3,rep(4,13))) 
# first 3 rows replication of ColWT, 4 for the rest
mc.gaelle<-meancss(gaelle,gaelle.id)
loglikelihood(x.mean=mc.gaelle$mean,x.css=mc.gaelle$css,
repno=mc.gaelle$repno,transformed.par=rep(0,6)) 
# evalutes likelihood at rep(0,6)