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Returns posterior standard deviation estimates for point estimates returned by blca.em. These are obtained via asymptotic estmation of the Observed Information matrix. The Hessian of the log-posterior is also checked to determine whether point estimates occur at at least a local maximum.
blca.em.sd(fit, x, counts.n = 1)An object of class "blca.em".
A binary matrix. An object of class data.blca may also be supplied. In this case the argument counts.n is ignored.
A vector which supplies the corresponding number of times each pattern in X occurs in the data.
A list containing:
Posterior standard deviation estimates of the item probabilities.
Posterior standard deviation estimates of the class probabilities.
An integer value denoting whether point estimates occur at at least a local maximum. 1 denotes acceptable convergence, 2 denotes that it converged at a saddle point, 3 that the algorithm ended before it converged and 4 denotes that at least one parameter value converged at a boundary value.
This function is primarily intended for use in conjunction with blca.em, and may be called directly by that function by setting se=TRUE. However it can in fact be used with any blca object.
# NOT RUN {
type1 <- c(0.8, 0.8, 0.2, 0.2)
type2 <- c(0.2, 0.2, 0.8, 0.8)
x<- rlca(1000, rbind(type1,type2), c(0.6,0.4))
dat<- data.blca(x)
set.seed(1)
fit1 <- blca.em(dat, 2, se=TRUE)
fit1$itemprob.sd
fit1$classprob.sd
set.seed(1)
fit2<- blca.em(dat, 2, se=FALSE)
fit2.sd<- blca.em.sd(fit2, dat)
fit2.sd$itemprob
fit2.sd$classprob
# }
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