hompage update

DESCRIPTION

@@ -27,7 +27,7 @@ BugReports: https://github.com/kim0sun/glca/issues/
 Encoding: UTF-8
 LazyData: true
 Depends: R (>= 3.5.0)
-RoxygenNote: 7.1.1
+RoxygenNote: 7.2.1
 Imports: 
     MASS,
     Rcpp,

R/glca_output.R

@@ -281,9 +281,8 @@ glca_output <- function(
    P = do.call(rbind, pclass)
    gof <- list(
       loglik = EM$loglik,
-      aic = -2 * EM$loglik + 2 * npar,
-      caic = -2 * EM$loglik + (log(N) + 1L) * npar,
-      bic = -2 * EM$loglik + log(N) * npar,
+      AIC = -2 * EM$loglik + 2 * npar,
+      BIC = -2 * EM$loglik + log(N) * npar,
       entropy = 1 - sum(-P[P != 0] * log(P)[P != 0]) / (N * log(C)),
       df = df,
       Gsq = 2 * (datalist$loglik0 - EM$loglik)

R/gofglca.R

@@ -1,6 +1,6 @@
 #' Goodness of Fit Tests for Fitted \code{glca} Model
 #'
-#' Provides AIC, CAIC, BIC, entropy and deviance statitistic for goodness of fit test for the fitted model. Given \code{object2}, the function computes the log-likelihood ratio (LRT) statisic for comparing the goodness of fit for two models. The bootstrap p-value can be obtained from the empirical distribution of LRT statistic by choosing \code{test = "boot"}.
+#' Provides AIC, BIC, entropy and deviance statitistic for goodness of fit test for the fitted model. Given \code{object2}, the function computes the log-likelihood ratio (LRT) statisic for comparing the goodness of fit for two models. The bootstrap p-value can be obtained from the empirical distribution of LRT statistic by choosing \code{test = "boot"}.
 #'
 #' @param object an object of "\code{glca}", usually, a result of a call to \code{glca}.
 #' @param ... an optional object of "\code{glca}" to be compared with \code{object}.
@@ -69,7 +69,7 @@
 
 gofglca <- function(
    object, ..., test = NULL, nboot = 50,
-   criteria = c("logLik", "AIC", "CAIC", "BIC", "entropy"),
+   criteria = c("AIC", "BIC", "entropy"),
    maxiter = 500, eps = 1e-4, seed = NULL, verbose = FALSE
 )
 {
@@ -105,8 +105,7 @@ gofglca <- function(
    })
 
    criteria <- match.arg(criteria, several.ok = TRUE)
-   valid.ind <- match(criteria, c("logLik", "AIC", "CAIC", "BIC", "entropy"), 0L)
-   gof <- lapply(obj, function(x) x$gof[c(valid.ind, 6L:7L)])
+   gof <- lapply(obj, function(x) x$gof[c("loglik", criteria, "df", "Gsq")])
 
    models <- sapply(obj, function(x) x$model)
    resp <- sapply(obj, function(x) paste0(x$var.names$y.names, collapse = ","))
@@ -122,7 +121,6 @@ gofglca <- function(
    gsqR <- 2L * diff(llik)
 
    gtable <- as.matrix(do.call(rbind, lapply(gof[ord], unlist)))
-   colnames(gtable) <- c(criteria, "Res.Df", "Gsq")
    rownames(gtable) <- ord
 
    # random seed
@@ -189,7 +187,7 @@ gofglca <- function(
       npar <- sapply(obj[ord], function(x) x$model$npar)
       Df  <- diff(npar)
       dtable <- cbind(npar, llik, c(NA, Df), c(NA, round(gsqR, 3L)))
-      colnames(dtable) <- c("npar", "logLik", "Df", "Deviance")
+      colnames(dtable) <- c("npar", "loglik", "Df", "Deviance")
       rownames(dtable) <- ord
 
       if (!is.null(test))

R/print.glca.R

@@ -31,6 +31,6 @@ print.glca = function(x, ...)
 
    cat("\nlog-likelihood :", gof$loglik,
        "\n     G-squared :", gof$Gsq,
-       "\n           AIC :", gof$aic,
-       "\n           BIC :", gof$bic, "\n")
+       "\n           AIC :", gof$AIC,
+       "\n           BIC :", gof$BIC, "\n")
 }

R/reorder.glca.R

@@ -32,7 +32,7 @@
 #' @import stats
 #' @export
 
-reorder.glca <- function(x, ..., class.order = NULL, cluster.order = NULL, decreasing = TRUE)
+reorder.glca <- function(x, class.order = NULL, cluster.order = NULL, decreasing = TRUE, ...)
 {
    if (!is.null(class.order)) {
       if (!setequal(as.numeric(class.order), 1:x$model$C))

R/summary.glca.R

@@ -51,8 +51,8 @@ summary.glca <- function(
 
    cat("\nlog-likelihood :", gof$loglik,
        "\n     G-squared :", gof$Gsq,
-       "\n           AIC :", gof$aic,
-       "\n           BIC :", gof$bic, "\n")
+       "\n           AIC :", gof$AIC,
+       "\n           BIC :", gof$BIC, "\n")
 
    if (model$W > 1L){
       cat("\nMarginal prevalences for latent classes :\n")
@@ -61,19 +61,22 @@ summary.glca <- function(
       print(round(colMeans(posterior$cluster), 5L))
       cat("\nClass prevalences by cluster :\n")
       print(round(posterior$wclass, 5L))
+      cat("\n")
    } else {
       cat("\nMarginal prevalences for latent classes :\n")
       print(round(colMeans(do.call(rbind, posterior)), 5L))
-      if (model$G < 15L) {
-         cat("\nClass prevalences by group :\n")
-         prev = as.matrix(do.call(rbind, lapply(posterior, colMeans)))
-         dimnames(prev) = list(var.names$g.names,
-                               paste0("Class ", 1L:model$C))
-         print(round(prev, 5L))
-         cat("\n")
-      } else {
-         cat("\nToo many groups to be printed.\n")
-      }
+      if (model$G > 1) {
+         if (model$G < 15L) {
+            cat("\nClass prevalences by group :\n")
+            prev = as.matrix(do.call(rbind, lapply(posterior, colMeans)))
+            dimnames(prev) = list(var.names$g.names,
+                                  paste0("Class ", 1L:model$C))
+            print(round(prev, 5L))
+            cat("\n")
+         } else {
+            cat("\nToo many groups to be printed.\n")
+         }
+      } else cat("\n")
    }
 
    if (model$W > 1L) {
@@ -87,6 +90,7 @@ summary.glca <- function(
          if (model$Q > 0L) {
             cat("Logistic regression coefficients (level 2) :\n")
             print(round(param$beta[[2L]], digits))
+            cat("\n")
          }
          cat("\n")
       }