summary.ergmm {latentnet} | R Documentation |
summary.ergmm
prodcues a summary of an ergmm
object, including
point estimates, standard errors, and BIC calculation.
## S3 method for class 'ergmm' summary(object, point.est = c("pmean", "mkl"), quantiles = c(0.025, 0.975), se = FALSE, ...) bic.ergmm(object)
object |
An |
point.est |
Point estimates to compute: a character vector with some subset of the following: "mle", "pmean","mkl","pmode". |
quantiles |
Posterior quantiles (credible intervals) to compute. |
se |
Whether to compute standard errors. |
... |
Additional arguments. Currently unused. |
For summary
, an object of class
summary.ergmm
. A print method is
available.
The BICs are available as the element "bic" of the object returned. Note that BIC computed for the random effects models may not be correct.
bic.ergmm
returns the BIC for the model directly.
Chris Fraley and Adrian E. Raftery (2002). Model-based clustering, discriminant analysis, and density estimation. Journal of the American Statistical Association, 97(458), 611-631.
data(sampson) # Fit the model for cluster sizes 1 through 5: fits<-list( ergmm(samplike~euclidean(d=2,G=1)), ergmm(samplike~euclidean(d=2,G=2)), ergmm(samplike~euclidean(d=2,G=3)), ergmm(samplike~euclidean(d=2,G=4)), ergmm(samplike~euclidean(d=2,G=5)) ) ## Not run: sapply(fits,plot) ## End(Not run) # Compute the BICs for the fits and plot them: (bics<-reshape(as.data.frame(t(sapply(fits,function(x)c(G=x$model$G,unlist(bic.ergmm(x))[c("Y","Z","overall")])))),list(c("Y","Z","overall")),idvar="G",v.names="BIC",timevar="Component",times=c("likelihood","clustering","overall"),direction="long")) with(bics,interaction.plot(G,Component,BIC,type="b",xlab="Clusters", ylab="BIC")) # Summarize and plot whichever fit has the lowest overall BIC: bestG<-with(bics[bics$Component=="overall",],G[which.min(BIC)]) summary(fits[[bestG]]) plot(fits[[bestG]])