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drizopoulos · Jun 17, 2024 · df2d6c2 · df2d6c2
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diff --git a/DESCRIPTION b/DESCRIPTION
@@ -1,9 +1,9 @@
 Package: GLMMadaptive
 Title: Generalized Linear Mixed Models using Adaptive Gaussian Quadrature
-Version: 0.9-4
+Version: 0.9-5
 Authors@R: person("Dimitris", "Rizopoulos", email = "d.rizopoulos@erasmusmc.nl",
                   role = c("aut", "cre"), comment = c(ORCID = '0000-0001-9397-0900'))
-Date: 2024-03-24
+Date: 2024-06-17
 Maintainer: Dimitris Rizopoulos <d.rizopoulos@erasmusmc.nl>
 BugReports: https://github.com/drizopoulos/GLMMadaptive/issues
 Description: Fits generalized linear mixed models for a single grouping factor under

diff --git a/NEWS.md b/NEWS.md
@@ -1,7 +1,7 @@
 # GLMMadaptive 0.9.1
 
 ## Minor
-* For hurdle log-normal models predictions are now calculated on the orginal outcome scale - not the log.
+* For hurdle log-normal models predictions are now calculated on the original outcome scale - not the log.
 
 # GLMMadaptive 0.9.0
 

diff --git a/R/mixed_fit.R b/R/mixed_fit.R
@@ -340,7 +340,7 @@ mixed_fit <- function (y, X, Z, X_zi, Z_zi, id, offset, offset_zi, family,
                                             mu.eta_fun, score_eta_fun, score_eta_zi_fun, score_phis_fun, 
                                             list_thetas, diag_D, penalized, pen_mu, pen_invSigma, pen_df,
                                             i_contributions = TRUE, weights = weights)
-    names(GH$post_vars) <- rownames(GH$post_modes)
+    #names(GH$post_vars) <- rownames(GH$post_modes)
     list(coefficients = betas, phis = if (has_phis) phis, D = D, gammas = gammas,
          post_modes = GH$post_modes, post_vars = GH$post_vars,
          logLik = logLik, logLik_contributions = logLik_contributions,

diff --git a/R/mixed_model.R b/R/mixed_model.R
@@ -46,7 +46,7 @@ mixed_model <- function (fixed, random, data, family, weights = NULL,
         unique(c(all.vars(getID_Formula(random)), 
                  if (!is.null(zi_random)) all.vars(getID_Formula(zi_random))))
     data[groups] <- lapply(data[groups], 
-                           function (x) if (!is.factor(x)) factor(x) else x)
+                           function (x) if (!is.factor(x)) factor(x, levels = unique(x)) else x)
     data <- data[order(data[[groups[1L]]]), ]
     # drop unused levels of factors
     factors <- sapply(data, is.factor)
@@ -306,7 +306,7 @@ mixed_model <- function (fixed, random, data, family, weights = NULL,
     out$id_name <- id_nam 
     out$offset <- offset
     dimnames(out$post_modes) <- list(unique(id_orig[[1L]]), RE_nams)
-    names(out$post_vars) <- unique(id_orig)
+    names(out$post_vars) <- unique(id_orig[[1L]])
     out$post_vars[] <- lapply(out$post_vars, function (v) {
         dimnames(v) <- list(RE_nams, RE_nams)
         v

diff --git a/man/GLMMadaptive.Rd b/man/GLMMadaptive.Rd
@@ -17,8 +17,8 @@ Gaussian quadrature rule.
 \tabular{ll}{
 Package: \tab GLMMadaptive\cr
 Type: \tab Package\cr
-Version: \tab 0.9-3\cr
-Date: \tab 2024-03-21\cr
+Version: \tab 0.9-5\cr
+Date: \tab 2024-06-17\cr
 License: \tab GPL (>=3)\cr
 }