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drizopoulos · Mar 21, 2024 · de00779 · de00779
1 parent 619d511
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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: 0.9-2
+Version: 0.9-3
 Authors@R: person("Dimitris", "Rizopoulos", email = "d.rizopoulos@erasmusmc.nl",
                   role = c("aut", "cre"), comment = c(ORCID = '0000-0001-9397-0900'))
-Date: 2024-01-16
+Date: 2024-03-21
 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/R/Functions.R b/R/Functions.R
@@ -584,15 +584,17 @@ cr_marg_probs <- function (eta, direction = c("forward", "backward")) {
     direction <- match.arg(direction)
     ncoefs <- ncol(eta)
     if (direction == "forward") {
-        cumsum_1_minus_p <- t(apply(plogis(eta[, -ncoefs], log.p = TRUE, 
-                                           lower.tail = FALSE), 1, cumsum))
+        cumsum_1_minus_p <- apply(plogis(eta[, -ncoefs, drop = FALSE], log.p = TRUE, 
+                                           lower.tail = FALSE), 1L, cumsum)
+        if (is.matrix(cumsum_1_minus_p)) cumsum_1_minus_p <- t(cumsum_1_minus_p)
         probs <- exp(plogis(eta, log.p = TRUE) + cbind(0, cumsum_1_minus_p))
         cbind(probs, 1 - rowSums(probs))
     } else {
-        cumsum_1_minus_p <- t(apply(plogis(eta[, seq(ncoefs, 2)], log.p = TRUE, 
-                                           lower.tail = FALSE), 1, cumsum))
+        cumsum_1_minus_p <- apply(plogis(eta[, seq(ncoefs, 2), drop = FALSE], log.p = TRUE, 
+                                           lower.tail = FALSE), 1L, cumsum)
+        if (is.matrix(cumsum_1_minus_p)) cumsum_1_minus_p <- t(cumsum_1_minus_p)
         probs <- exp(plogis(eta, log.p = TRUE) + 
                          cbind(cumsum_1_minus_p[, seq(ncoefs - 1, 1)], 0))
         cbind(1 - rowSums(probs), probs)
     }
-}
+}
diff --git a/R/methods.R b/R/methods.R
@@ -537,7 +537,7 @@ marginal_coefs.MixMod <- function (object, std_errors = FALSE, link_fun = NULL,
     compute_marg_coefs <- function (object, X, betas, Z, X_zi, gammas, Z_zi, D, M, 
                                     link_fun, seed) {
         if (!exists(".Random.seed", envir = .GlobalEnv)) 
-            runif(1)
+            runif(1L)
         RNGstate <- get(".Random.seed", envir = .GlobalEnv)
         on.exit(assign(".Random.seed", RNGstate, envir = .GlobalEnv))
         mu_fun <- object$Funs$mu_fun
@@ -549,12 +549,12 @@ marginal_coefs.MixMod <- function (object, std_errors = FALSE, link_fun = NULL,
         }
         Xbetas <- c(X %*% betas)
         if (!is.null(offset)) {
-            Xbetas <- Xbetas #+ offset
+            Xbetas <- Xbetas + offset
         }
         if (!is.null(gammas)) {
             eta_zi <- c(X_zi %*% gammas)
             if (!is.null(offset_zi)) {
-                eta_zi <- eta_zi #+ offset_zi
+                eta_zi <- eta_zi + offset_zi
             }
         }
         id <- match(object$id[[1]], unique(object$id[[1]]))
@@ -605,7 +605,7 @@ marginal_coefs.MixMod <- function (object, std_errors = FALSE, link_fun = NULL,
                                                 Z_zi, M, compute_marg_coefs, chol_transf,
                                                 object, link_fun, seed) {
             if (!exists(".Random.seed", envir = .GlobalEnv)) 
-                runif(1)
+                runif(1L)
             RNGstate <- get(".Random.seed", envir = .GlobalEnv)
             on.exit(assign(".Random.seed", RNGstate, envir = .GlobalEnv))
             n_block <- length(block)
@@ -687,7 +687,7 @@ effectPlotData.MixMod <- function (object, newdata, level = 0.95, marginal = FAL
     X <- model.matrix(termsX, mfX)
     if (is.null(object$gammas)) {
         if (marginal) {
-            mcoefs <- marginal_coefs(object, std_errors = TRUE, ...)
+            mcoefs <- marginal_coefs(object, std_errors = TRUE, seed = seed, ...)
             betas <- mcoefs$betas
             var_betas <- mcoefs$var_betas
         } else {
@@ -702,7 +702,7 @@ effectPlotData.MixMod <- function (object, newdata, level = 0.95, marginal = FAL
             newdata$upp <- pred + qnorm((1 + level) / 2) * ses
         } else {
             if (!exists(".Random.seed", envir = .GlobalEnv, inherits = FALSE)) 
-                runif(1)
+                runif(1L)
             R.seed <- get(".Random.seed", envir = .GlobalEnv)
             set.seed(seed)
             RNGstate <- structure(seed, kind = as.list(RNGkind()))
@@ -752,7 +752,8 @@ effectPlotData.MixMod <- function (object, newdata, level = 0.95, marginal = FAL
         V <- V[ind, ind, drop = FALSE]
         new_tht <- MASS::mvrnorm(K, tht, V)
         if (marginal) {
-            mcoefs <- marginal_coefs(object, std_errors = TRUE, ...)
+            mcoefs <- marginal_coefs(object, std_errors = TRUE, seed = seed, 
+                                     ...)
             betas <- mcoefs$betas
             var_betas <- mcoefs$var_betas
             pred <- c(X %*% betas)

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 0.9-2\cr
-Date: \tab 2024-01-16\cr
+Version: \tab 0.9-3\cr
+Date: \tab 2024-03-21\cr
 License: \tab GPL (>=3)\cr
 }