add sabic

R/poLCA.R

@@ -159,6 +159,7 @@ function(formula,data,nclass=2,maxiter=1000,graphs=FALSE,tol=1e-10,
     ret$npar <- (R*sum(K.j-1)) + (R-1)                  # number of degrees of freedom used by the model (number of estimated parameters)
     if (S>1) { ret$npar <- ret$npar + (S*(R-1)) - (R-1) }
     ret$aic <- (-2 * ret$llik) + (2 * ret$npar)         # Akaike Information Criterion
+    ret$sabic <- (-2 * ret$llik) + (log((N + 2) / 24) * ret$npar)
     ret$bic <- (-2 * ret$llik) + (log(N) * ret$npar)    # Schwarz-Bayesian Information Criterion
     ret$Nobs <- sum(rowSums(y==0)==0)                   # number of fully observed cases (if na.rm=F)
     if (all(rowSums(y==0)>0)) { # if no rows are fully observed

R/print.poLCA.R

@@ -28,6 +28,7 @@ function(x, ...) {
     cat("maximum log-likelihood:", x$llik, "\n \n")
     cat("AIC(",R,"): ",x$aic,"\n",sep="")
     cat("BIC(",R,"): ",x$bic,"\n",sep="")
+    cat("SABIC(",R,"): ",x$sabic,"\n",sep="")
     if (S==1) cat("G^2(",R,"): ",x$Gsq," (Likelihood ratio/deviance statistic) \n",sep="")
     cat("X^2(",R,"): ",x$Chisq," (Chi-square goodness of fit) \n \n",sep="")
     if (x$numiter==x$maxiter) cat("ALERT: iterations finished, MAXIMUM LIKELIHOOD NOT FOUND \n \n")

man/poLCA.Rd

@@ -48,6 +48,7 @@ Model specification: Latent class models have more than one manifest variable, s
 \item{coeff.V}{covariance matrix of coefficient estimates on covariates (when estimated).}
 \item{aic}{Akaike Information Criterion.}
 \item{bic}{Bayesian Information Criterion.}
+\item{sabic}{Sample-Size adjusted Bayesian Information Criterion.}
 \item{Gsq}{Likelihood ratio/deviance statistic.}
 \item{Chisq}{Pearson Chi-square goodness of fit statistic for fitted vs. observed multiway tables.}
 \item{time}{length of time it took to run the model.}