Feature: add logistic regression and multiple regression as options

DESCRIPTION

@@ -1,7 +1,7 @@
 Package: rwa
 Type: Package
 Title: Perform a Relative Weights Analysis
-Version: 0.1.1
+Version: 0.1.2.9000
 Authors@R: person(given = "Martin",
                   family = "Chan",
                   role = c("aut", "cre"),

NAMESPACE

@@ -4,6 +4,8 @@ export("%>%")
 export(plot_rwa)
 export(remove_all_na_cols)
 export(rwa)
+export(rwa_logit)
+export(rwa_multiregress)
 import(dplyr)
 import(ggplot2)
 importFrom(boot,boot)
@@ -17,8 +19,14 @@ importFrom(dplyr,select)
 importFrom(dplyr,tibble)
 importFrom(magrittr,"%>%")
 importFrom(purrr,map_dfr)
+importFrom(stats,binomial)
+importFrom(stats,coef)
 importFrom(stats,cor)
+importFrom(stats,glm)
+importFrom(stats,lm)
+importFrom(stats,predict)
 importFrom(stats,rnorm)
+importFrom(stats,sd)
 importFrom(stats,var)
 importFrom(tidyr,drop_na)
 importFrom(utils,head)

NEWS.md

@@ -1,3 +1,9 @@
+# rwa (development version)
+
+- Added `rwa_logit()` and `rwa_multiregress()` to support logistic regression and multiple regression. 
+- Added new vignette to cover the new regression methods. 
+- Improved test coverage and minor bugfixes.
+
 # rwa 0.1.1
 
 ## Improvements

R/globals.R

@@ -9,6 +9,7 @@ utils::globalVariables(
     "Sign",
     "Rescaled.RelWeight",
     "Variables",
-    "Sign.Rescaled.RelWeight"
+    "Sign.Rescaled.RelWeight",
+    "variable_name"
   )
 )

R/plot_rwa.R

@@ -26,24 +26,48 @@ plot_rwa <- function(rwa){
     dplyr::mutate(Sign.Rescaled.RelWeight = ifelse(Sign == "-",
                                                    Rescaled.RelWeight * -1,
                                                    Rescaled.RelWeight))
+
+  # Get variable names from the Variables column
+  if (!"Variables" %in% names(result)) {
+    stop("Could not find 'Variables' column in result.")
+  }
+  result$variable_name <- result$Variables
 
+  # Calculate appropriate axis limits for both positive and negative values
+  max_abs_weight <- max(abs(result$Sign.Rescaled.RelWeight))
+  min_weight <- min(result$Sign.Rescaled.RelWeight)
   max_weight <- max(result$Sign.Rescaled.RelWeight)
+
+  # Set symmetric or appropriate limits based on data
+  if (min_weight < 0 && max_weight > 0) {
+    # Mixed signs: use symmetric limits
+    y_limits <- c(-max_abs_weight * 1.15, max_abs_weight * 1.15)
+  } else if (min_weight < 0) {
+    # All negative: extend left for labels
+    y_limits <- c(min_weight * 1.15, max(0, max_weight * 1.1))
+  } else {
+    # All positive: extend right for labels
+    y_limits <- c(min(0, min_weight), max_weight * 1.15)
+  }
 
   result %>%
-    ggplot(aes(x = stats::reorder(Variables, Sign.Rescaled.RelWeight), 
+    ggplot(aes(x = stats::reorder(variable_name, Sign.Rescaled.RelWeight), 
                y = Sign.Rescaled.RelWeight)) +
     geom_col(fill = "#0066cc") +
-    geom_text(aes(label = round(Sign.Rescaled.RelWeight, 1)), hjust = -0.3) +
+    geom_text(aes(label = round(Sign.Rescaled.RelWeight, 1),
+                  hjust = ifelse(Sign.Rescaled.RelWeight >= 0, -0.3, 1.3))) +
     coord_flip() +
-    ylim(c(NA, max_weight * 1.1)) + # Automatic lower limit
+    ylim(y_limits) +
     labs(title = "Variable importance estimates",
          subtitle = "Using the Relative Weights Analysis method",
          x = "Predictor variables",
-         y = "Rescaled Relative Weights",
-         caption = paste0("Note: Rescaled Relative Weights sum to 100%. n = ",
+         y = "Rescaled Relative Weights (with sign)",
+         caption = paste0("Note: Absolute Rescaled Relative Weights sum to 100%. n = ",
                           rwa$n, ". ",
-                          "R-squared: ",
-                          round(rwa$rsquare, 2),
-                          ".")) +
+                          if (!is.null(rwa$rsquare)) {
+                            paste0("R-squared: ", round(rwa$rsquare, 2), ".")
+                          } else {
+                            ""
+                          })) +
     theme_classic()
 }