Composite Likelihood for probit latent variable models
Composite Likelihood for probit latent variable models
Estimate parameters in a probit latent variable model via a composite likelihood decomposition.
complik( x, data, k =2, type = c("all","nearest"), pairlist, messages =0, estimator ="normal", quick =FALSE,...)
Arguments
x: lvm-object
data: data.frame
k: Size of composite groups
type: Determines number of groups. With type="nearest" (default) only neighboring items will be grouped, e.g. for k=2
(y1,y2),(y2,y3),... With type="all" all combinations of size k
are included
pairlist: A list of indices specifying the composite groups. Optional argument which overrides k and type but gives complete flexibility in the specification of the composite likelihood
messages: Control amount of messages printed
estimator: Model (pseudo-likelihood) to use for the pairs/groups
quick: If TRUE the parameter estimates are calculated but all additional information such as standard errors are skipped
...: Additional arguments parsed on to lower-level functions
Returns
An object of class estimate.complik inheriting methods from lvm
Examples
m <- lvm(c(y1,y2,y3)~b*x+1*u[0],latent=~u)ordinal(m,K=2)<-~y1+y2+y3
d <- sim(m,50,seed=1)if(requireNamespace("mets", quietly=TRUE)){ e1 <- complik(m,d,control=list(trace=1),type="all")}