Bugfixes & Updates

.github/workflows/R-CMD-check.yaml

@@ -0,0 +1,41 @@
+name: R-CMD-check
+
+on:
+  push:
+  pull_request:
+
+jobs:
+  R-CMD-check:
+    name: ${{ matrix.os }} / R ${{ matrix.r }}
+    runs-on: ${{ matrix.os }}
+
+    strategy:
+      fail-fast: false
+      matrix:
+        os: [ubuntu-latest, macos-latest, windows-latest]
+        r: [release]
+
+    steps:
+      - name: Checkout repository
+        uses: actions/checkout@v4
+
+      - name: Set up R
+        uses: r-lib/actions/setup-r@v2
+        with:
+          r-version: ${{ matrix.r }}
+          use-public-rspm: true
+
+      - name: Set up pandoc
+        uses: r-lib/actions/setup-pandoc@v2
+
+      - name: Install system dependencies
+        uses: r-lib/actions/setup-r-dependencies@v2
+        with:
+          extra-packages: |
+            any::rcmdcheck
+          needs: check
+
+      - name: Check package
+        uses: r-lib/actions/check-r-package@v2
+        with:
+          upload-snapshots: true

R/ACO.R

@@ -69,6 +69,7 @@ setMethod('show',
 #' 
 #' @importFrom ggrepel geom_text_repel
 #' @importFrom tidyr gather
+#' @importFrom rlang .data
 #' 
 #' @export
 setMethod('plot',
@@ -91,12 +92,12 @@ setMethod('plot',
               pheromone_plot <-
                 ggplot2::ggplot(
                 pheromone_long,
-                ggplot2::aes_string(
-                  x = "run",
-                  y = "Pheromone",
-                  group = "Item",
-                  fill = "Item",
-                  colour = "Item"
+                ggplot2::aes(
+                  x = .data$run,
+                  y = .data$Pheromone,
+                  group = .data$Item,
+                  fill = .data$Item,
+                  colour = .data$Item
                 )
               ) +
                 ggplot2::geom_area(
@@ -122,7 +123,7 @@ setMethod('plot',
               gamma_plot <-
                 ggplot2::ggplot(
                   summary_results,
-                  ggplot2::aes_string(x = "run", y = "mean.gamma")
+                  ggplot2::aes(x = .data$run, y = .data$mean.gamma)
                 ) +
                 ggplot2::geom_line() +
                 ggplot2::ylab(expression("Mean " * gamma)) +
@@ -147,7 +148,7 @@ setMethod('plot',
               beta_plot <-
                 ggplot2::ggplot(
                   summary_results,
-                  ggplot2::aes_string(x = "run", y = "mean.beta")
+                  ggplot2::aes(x = .data$run, y = .data$mean.beta)
                 ) +
                 ggplot2::geom_line() +
                 ggplot2::ylab(expression("Mean " * beta)) +
@@ -172,7 +173,7 @@ setMethod('plot',
               variance_plot <-
                 ggplot2::ggplot(
                   summary_results,
-                  ggplot2::aes_string(x = "run", y = "mean.var.exp")
+                  ggplot2::aes(x = .data$run, y = .data$mean.var.exp)
                 ) +
                 ggplot2::geom_line() +
                 ggplot2::ylab(expression("Mean Variance Explained")) +

R/ACO_lavaan.R

@@ -208,6 +208,49 @@
 #' )
 #'
 #' abilityShortForm # print the results of the final short form
+#' 
+#' # an example using binary (ordered) data
+#' # create the simulated full model and model data
+#' sim_model <- "
+#' f1 =~ x1 + x2 + x3 + x4 + x5 + x6 + x7 + x8 + x9 + x10
+#' f2 =~ x11 + x12 + x13 + x14 + x15 + x16 + x17 + x18 + x19 + x20
+#' f3 =~ x21 + x22 + x23 + x24 + x25 + x26 + x27 + x28 + x29 + x30"
+#' 
+#' sim_data <-
+#'   cbind(
+#'     psych::sim.rasch(nvar = 10)$items,
+#'     psych::sim.rasch(nvar = 10)$items,
+#'     psych::sim.rasch(nvar = 10)$items
+#'   )
+#' 
+#' colnames(sim_data) = paste0("x", 1:30)
+#' # fit with antcolony.lavaan
+#' # note that ONLY the estimator and ordered args
+#' # of lavaan.model.specs are changed. This retains
+#' # the default args, fitting a CFA but with ordered data.
+#' example <-
+#' antcolony.lavaan(
+#'   data = sim_data,
+#'   ants = 5, evaporation = 0.7,
+#'   antModel = sim_model,
+#'   lavaan.model.specs = 
+#'     list(estimator = "wlsmv", ordered = T),
+#'   list.items = list(paste0('x', 1:10),
+#'                     paste0('x', 11:20),
+#'                     paste0('x', 21:30)), 
+#'   full = 30, 
+#'   i.per.f = c(5, 5, 5), 
+#'   factors = c("f1", "f2", "f3"),
+#'   steps = 20, 
+#'   fit.indices = c("cfi.scaled"), 
+#'   fit.statistics.test = "(cfi.scaled > 0.90)", 
+#'   summaryfile = NULL, 
+#'   feedbackfile = NULL, 
+#'   max.run = 500, 
+#'   parallel = T
+#' )
+#' # note that this example will take a bit of time to run
+#' # as ordered data factor analysis is computationally expensive.
 #' }
 #' @import lavaan utils
 #' @export
@@ -364,7 +407,20 @@ antcolony.lavaan <- function(data = NULL, sample.cov = NULL, sample.nobs = NULL,
         select.indicator <- is.element(item.vector, selected.vector)
 
         # MODIFY LAVAAN SYNTAX
-        new_ant_model <- paste(antcolony.lavaan.env$input, collapse = "\n")
+        new_ant_model <- 
+          input
+        for (factor in 1:length(factors)) {
+          temp_factor_definition <-
+            new_ant_model[grepl(factors[factor], new_ant_model)]
+          temp_factor_definition <-
+            sub(
+              "=~[[:alnum:][:space:] +]{1,}", 
+              paste0("=~ ", paste0(selected.items[[factor]], collapse = " + ")), 
+              temp_factor_definition
+              )
+          new_ant_model[grepl(factors[factor], new_ant_model)] <-
+            temp_factor_definition
+        }
 
         # Run the model check function
         # checks for and saves error/warning messages within the lavaan output,
@@ -491,8 +547,8 @@ antcolony.lavaan <- function(data = NULL, sample.cov = NULL, sample.nobs = NULL,
         antResults[[bestAnt,'pheromone']]
 
 
-      # adjusts pheromone and best.so.far values only if the current pheromone is best than the previous.
-      if (best.pheromone > best.so.far.pheromone) {
+      # adjusts pheromone and best.so.far values only if the current pheromone is as good or better than the previous.
+      if (best.pheromone >= best.so.far.pheromone) {
         include.pheromone <- antResults[[bestAnt, 'solution']] * best.pheromone
         include <- include + include.pheromone
 

R/simulated_annealing_internals.R

@@ -49,7 +49,7 @@ randomInitialModel <-
     )
   }
   newModelSyntax <- 
-    paste0(newModelSyntax, externalRelation, factorRelation, collapse = "\n")
+    paste(newModelSyntax, externalRelation, factorRelation, sep = "\n")
   newModelSyntax <-
     stringr::str_replace_all(newModelSyntax, "\n\n", "\n")