closes #58

R/blr-backward-elimination.R

@@ -17,6 +17,7 @@
 #' \code{"blr_step_aic_backward"}. An object of class
 #' \code{"blr_step_aic_backward"} is a list containing the following components:
 #'
+#' \item{model}{model with the least AIC; an object of class \code{glm}}
 #' \item{candidates}{candidate predictor variables}
 #' \item{steps}{total number of steps}
 #' \item{predictors}{variables removed from the model}
@@ -40,6 +41,11 @@
 #'
 #' # plot
 #' plot(blr_step_aic_backward(model))
+#'
+#' # final model
+#' k <- blr_step_aic_backward(model)
+#' k$model
+#'
 #' }
 #'
 #' @family variable selection procedures
@@ -256,13 +262,17 @@ blr_step_aic_backward.default <- function(model, details = FALSE, ...) {
     print(fi)
   }
 
+  final_model <- glm(paste(response, "~", paste(preds, collapse = " + ")), 
+    data = l, family = binomial(link = 'logit'))
+
   out <- list(
     candidates = nam,
     steps      = step,
     predictors = rpred,
     aics       = laic,
     bics       = lbic,
-    devs       = ldev
+    devs       = ldev,
+    model      = final_model
   )
 
   class(out) <- "blr_step_aic_backward"

R/blr-forward-selection.R

@@ -16,6 +16,7 @@
 #' \code{"blr_step_aic_forward"}. An object of class
 #' \code{"blr_step_aic_forward"} is a list containing the following components:
 #'
+#' \item{model}{model with the least AIC; an object of class \code{glm}}
 #' \item{candidates}{candidate predictor variables}
 #' \item{steps}{total number of steps}
 #' \item{predictors}{variables entered into the model}
@@ -39,6 +40,11 @@
 #'
 #' # plot
 #' plot(blr_step_aic_forward(model))
+#'
+#' # final model
+#' k <- blr_step_aic_forward(model)
+#' k$model
+#'
 #' }
 #' 
 #' @importFrom shiny isRunning
@@ -273,13 +279,17 @@ blr_step_aic_forward.default <- function(model, details = FALSE, ...) {
     print(fi)
   }
 
+  final_model <- glm(paste(response, "~", paste(preds, collapse = " + ")), 
+    data = l, family = binomial(link = 'logit'))
+
   out <- list(
     candidates = nam,
     steps      = step,
     predictors = preds,
     aics       = laic,
     bics       = lbic,
-    devs       = ldev
+    devs       = ldev,
+    model      = final_model
   )
 
   class(out) <- "blr_step_aic_forward"

R/blr-stepwise-backward-regression.R

@@ -18,6 +18,7 @@
 #' An object of class \code{"blr_step_p_backward"} is a list containing the
 #' following components:
 #'
+#' \item{model}{model with the least AIC; an object of class \code{glm}}
 #' \item{steps}{total number of steps}
 #' \item{removed}{variables removed from the model}
 #' \item{aic}{akaike information criteria}
@@ -40,6 +41,10 @@
 #'   data = hsb2, family = binomial(link = 'logit'))
 #' k <- blr_step_p_backward(model)
 #' plot(k)
+#'
+#' # final model
+#' k$model
+#'
 #' }
 #'
 #' @importFrom dplyr full_join select
@@ -167,12 +172,16 @@ blr_step_p_backward.default <- function(model, prem = 0.3, details = FALSE, ...)
   )
   print(fi)
 
+  final_model <- glm(paste(response, "~", paste(preds, collapse = " + ")), 
+    data = l, family = binomial(link = 'logit'))
+
   out <- list(removed    = rpred,
               indvar     = cterms,
               steps      = step,
               bic        = bic,
               aic        = aic,
-              dev        = dev)
+              dev        = dev,
+              model      = final_model)
 
   class(out) <- "blr_step_p_backward"
 

R/blr-stepwise-forward-regression.R

@@ -18,6 +18,7 @@
 #' An object of class \code{"blr_step_p_forward"} is a list containing the
 #' following components:
 #'
+#' \item{model}{model with the least AIC; an object of class \code{glm}}
 #' \item{steps}{number of steps}
 #' \item{predictors}{variables added to the model}
 #' \item{aic}{akaike information criteria}
@@ -44,6 +45,10 @@
 #'   family = binomial(link = 'logit'))
 #' k <- blr_step_p_forward(model)
 #' plot(k)
+#'
+#' # final model
+#' k$model
+#'
 #' }
 #'
 #' @importFrom stats qt
@@ -231,13 +236,16 @@ blr_step_p_forward.default <- function(model, penter = 0.3, details = FALSE, ...
   )
   print(fi)
 
+  final_model <- glm(paste(response, "~", paste(preds, collapse = " + ")), 
+    data = l, family = binomial(link = 'logit'))
 
   out <- list(predictors = preds,
               indvar     = cterms,
               steps      = step,
                bic       = bic,
               aic        = aic,
-              dev        = dev)
+              dev        = dev,
+              model      = final_model)
 
   class(out) <- "blr_step_p_forward"
 

R/blr-stepwise-regression.R

@@ -19,6 +19,7 @@
 #' An object of class \code{"blr_step_p_both"} is a list containing the
 #' following components:
 #'
+#' \item{model}{final model; an object of class \code{glm}}
 #' \item{orders}{candidate predictor variables according to the order by which they were added or removed from the model}
 #' \item{method}{addition/deletion}
 #' \item{steps}{total number of steps}
@@ -41,6 +42,10 @@
 #' model <- glm(y ~ ., data = stepwise)
 #' k <- blr_step_p_both(model)
 #' plot(k)
+#'
+#' # final model
+#' k$model
+#'
 #' }
 #'
 #' @family variable selection_procedures
@@ -329,6 +334,9 @@ blr_step_p_both.default <- function(model, pent = 0.1, prem = 0.3, details = FAL
   )
   print(fi)
 
+  final_model <- glm(paste(response, "~", paste(preds, collapse = " + ")), 
+    data = l, family = binomial(link = 'logit'))
+
   out <- list(
     orders     = var_index,
     method     = method,
@@ -337,7 +345,8 @@ blr_step_p_both.default <- function(model, pent = 0.1, prem = 0.3, details = FAL
     aic        = aic,
     bic        = bic,
     dev        = dev,
-    indvar     = cterms
+    indvar     = cterms,
+    model      = final_model
   )
 
   class(out) <- "blr_step_p_both"

R/blr-stepwise-selection.R

@@ -16,6 +16,7 @@
 #' An object of class \code{"blr_step_aic_both"} is a list containing the
 #' following components:
 #'
+#' \item{model}{model with the least AIC; an object of class \code{glm}}
 #' \item{candidates}{candidate predictor variables}
 #' \item{predictors}{variables added/removed from the model}
 #' \item{method}{addition/deletion}
@@ -39,6 +40,11 @@
 #'
 #' # plot
 #' plot(blr_step_aic_both(model))
+#'
+#' # final model
+#' k <- blr_step_aic_both(model)
+#' k$model
+#'
 #' }
 #'
 #' @family variable selection procedures
@@ -286,14 +292,18 @@ blr_step_aic_both.default <- function(model, details = FALSE, ...) {
     print(fi)
   }
 
+  final_model <- glm(paste(response, "~", paste(preds, collapse = " + ")), 
+    data = l, family = binomial(link = 'logit'))
+
   out <- list(
     candidates = nam,
     predictors = var_index,
     method     = method,
     aic        = laic,
     bic        = lbic,
     dev        = ldev,
-    steps      = all_step
+    steps      = all_step,
+    model      = final_model
   )
 
   class(out) <- "blr_step_aic_both"