changed com_data and added Distance matrix option for fit_models

DESCRIPTION

@@ -15,7 +15,7 @@ Description: Provides tools for model selection and model averaging of PerMANOVA
     (<doi:10.1007/b97636>).
 License: MIT + file LICENSE
 Encoding: UTF-8
-Roxygen: list(markdown = TRUE)
+Roxygen: list(markdown = TRUE, roclets = c ("namespace", "rd", "srr::srr_stats_roclet"))
 RoxygenNote: 7.2.3
 Imports: 
     broom,
@@ -41,3 +41,5 @@ Suggests:
     testthat (>= 3.0.0)
 Config/testthat/edition: 3
 VignetteBuilder: knitr
+Enhances: 
+    betapart

R/fit_models.R

@@ -3,14 +3,17 @@
 #' @description This function fits PERMANOVA models for all combinations of variables in a given dataset, and arranges the models by Akaike Information Criterion (AICc) score. The function also calculates the maximum variance inflation factor (max_vif) for each model.
 #'
 #' @param all_forms A data frame generated by \code{\link{make_models}}
-#' @param veg_data A dataset with vegetation presence absense or abundance data
+#' @param com_data A dataset with community presence absense or
+#' abundance data, you can also use a dist class file generated from
+#' vegan, betapart or other packages
 #' @param env_data A dataset with the variables described in all_froms
 #' @param ncores An integer specifying the number of cores to use for parallel processing
 #' @param log logical if true, a log file will be generated
 #' @param verbose logical, defaults TRUE, sends messages about processing times
 #' @param logfile the text file that will be generated as a log
 #' @param multiple after how many loops to write a log file
-#' @param method method for distance from \code{\link{vegdist}}
+#' @param method method for distance from \code{\link{vegdist}},
+#' this will be ignored if com_data is a distance file
 #' @param strata a block variable similar to the use in \code{\link{adonis2}}
 #'
 #' @return A data.frame with fitted models arranged by AICc, including the formula used, the number of
@@ -28,6 +31,7 @@
 #' @importFrom stats rnorm lm as.formula complete.cases
 #' @examples
 #' \donttest{
+#' ## example with dataframe as community data
 #' library(vegan)
 #' data(dune)
 #' data(dune.env)
@@ -36,9 +40,19 @@
 #'
 #' fit_models(
 #'   all_forms = AllModels,
-#'   veg_data = dune,
+#'   com_data = dune,
 #'   env_data = dune.env
 #' )
+#' ## example with distance as community data
+#' library(betapart)
+#' Distance <- beta.pair.abund(dune)
+#' Distance <- Distance$beta.bray.bal
+#' fit_models(
+#'   all_forms = AllModels,
+#'   com_data = Distance,
+#'   env_data = dune.env
+#' )
+#'
 #' }
 #'
 #' @references
@@ -49,7 +63,7 @@
 #' @export
 
 fit_models <- function(all_forms,
-                       veg_data,
+                       com_data,
                        env_data,
                        method = "bray",
                        ncores = 2,
@@ -69,32 +83,38 @@ fit_models <- function(all_forms,
   if (!("max_vif" %in% colnames(meta_data))) {
     meta_data$max_vif <- NA
   }
-  vegetation_data <- veg_data
-
-  # Check for missing values
-  missing_rows <- !complete.cases(env_data)
-
-  if (any(missing_rows)) {
-    if (verbose) {
-      # Print message about missing rows and columns
-      message(sprintf("Removing %d rows with missing values\n", sum(missing_rows)))
-      message("Columns with missing values: ")
-      message(names(env_data)[colSums(is.na(env_data)) > 0], sep = ", ")
+  community_data <- com_data
+
+  if(class(community_data) != "dist"){
+    # Check for missing values
+    missing_rows <- !complete.cases(env_data)
+
+    if (any(missing_rows)) {
+      if (verbose) {
+        # Print message about missing rows and columns
+        message(sprintf("Removing %d rows with missing values\n", sum(missing_rows)))
+        message("Columns with missing values: ")
+        message(names(env_data)[colSums(is.na(env_data)) > 0], sep = ", ")
+      }
     }
-  }
 
-  # Filter out missing rows
-  new_env_data <- env_data[complete.cases(env_data), ]
-  vegetation_data <- vegetation_data[complete.cases(env_data), ]
+    # Filter out missing rows
+    env_data <- env_data[complete.cases(env_data), ]
+    community_data <- community_data[complete.cases(env_data), ]
+    Distance <- vegan::vegdist(community_data, method = method)
+  }
 
+  if(class(community_data) != "dist"){
+    Distance <- community_data
+  }
 
   cl <- parallel::makeCluster(ncores)
   doParallel::registerDoParallel(cl)
 
-  Distance <- vegan::vegdist(vegetation_data, method = method)
 
   Fs <- foreach(x = 1:nrow(meta_data), .packages = c("vegan", "dplyr", "AICcPermanova", "tidyr", "broom"), .combine = bind_rows, .export = c("Distance")) %dopar% {
-    Response <- new_env_data
+
+    Response <- env_data
     Response$y <- rnorm(n = nrow(Response))
     gc()
 
@@ -110,8 +130,6 @@ fit_models <- function(all_forms,
       Model <- try(with(Response, vegan::adonis2(as.formula(Temp$form[1]), data = Response, by = "margin", strata = strata_factor)), silent = TRUE)
     }
 
-
-
     Temp <- tryCatch(
       expr = cbind(Temp, AICcPermanova::AICc_permanova2(Model)),
       error = function(e) NA
@@ -142,7 +160,6 @@ fit_models <- function(all_forms,
       }
     )
 
-
     if (log) {
       if ((x %% multiple) == 0) {
         sink(logfile, append = TRUE)
@@ -152,7 +169,8 @@ fit_models <- function(all_forms,
       }
     }
 
-    Temp <- bind_cols(Temp, Rs)
+    Temp <- dplyr::bind_cols(Temp, Rs)
+    sink(logfile, append = TRUE)
     Temp
   }
   parallel::stopCluster(cl)

vignettes/getting-started-with-aiccpermanova.Rmd

@@ -116,7 +116,7 @@ After filtering out collinear models, we can fit all the remaining non-collinear
 ```{r}
 Fitted <- fit_models(
   all_forms = NonColinear,
-  veg_data = dune,
+  com_data = dune,
   env_data = dune.env,
   ncores = 1,
   method = "bray"
@@ -134,7 +134,7 @@ If there is a block variable to be used (such as Use in the dune.env object), yo
 ```{r fitwihtstrata}
 Fitted2 <- fit_models(
   all_forms = NonColinear,
-  veg_data = dune,
+  com_data = dune,
   env_data = dune.env,
   ncores = 1,
   method = "bray",