Add auditorData

Author: agosiewska

.Rbuildignore

@@ -12,3 +12,4 @@ materials
 jupyter-notebooks
 ^codecov\.yml$
 binder
+^_pkgdown\.yml$

NEWS.md

@@ -1,5 +1,5 @@
 # auditor 0.3.0
-* Five new vignettes with examples.
+* New five vignettes with examples.
 * Second pipeline with new computational functions: `modelResiduals()`, `modelEvaluation()`, `modelFit()`, `modelPerformance()`, `observationInfluence()`.
 * New plot: `plotResidualBoxplot()`
 

R/auditorData.R

@@ -0,0 +1,12 @@
+#' @title auditorData
+#' @description The auditor Data is an artificial data set.
+#' It consists of 2000 observations.
+#' First four of simulated variables are treated as continuous while the fifth one is categorical.
+#'
+#' @name auditorData
+#' @docType data
+#'
+#' @examples
+#' data("auditorData", package = "auditor")
+#' head(auditorData)
+NULL

R/modelEvaluation.R

@@ -5,6 +5,17 @@
 #' @param object An object of class ModelAudit.
 #' @param variable Optional. Name of variable to order residuals. If value is NULL data order is taken. If value is "Predicted response" or "Fitted values" then data is ordered by fitted values. If value is "Observed response" the data is ordered by a vector of actual response (\code{y} parameter passed to the \code{\link{audit}} function).
 #'
+#' @examples
+#' library(mlbench)
+#' data("PimaIndiansDiabetes")
+#' Pima <- PimaIndiansDiabetes
+#' Pima$diabetes <- ifelse(Pima$diabetes == "pos", 1, 0)
+#' glm_model <- glm(diabetes~., family=binomial,	data=Pima)
+#' glm_au <- audit(glm_model, data = Pima, y = Pima$diabetes)
+#'
+#' modelEvaluation(glm_au)
+#'
+#'
 #' @export
 modelEvaluation <- function(object, variable = NULL){
   if(!("modelAudit" %in% class(object))) stop("The function requires an object created with audit().")

R/modelFit.R

@@ -6,6 +6,13 @@
 #' @param quant.scale if TRUE values on axis are on quantile scale.
 #' @param ... other parameters passed do \code{\link[hnp]{hnp}} function.
 #'
+#' @examples
+#' library(MASS)
+#' model.glm <- glm(Postwt ~ Prewt + Treat + offset(Prewt), family = gaussian, data = anorexia)
+#' audit.glm <- audit(model.glm)
+#'
+#' mf.glm <- modelFit(audit.glm)
+#'
 #' @export
 modelFit <- function(object, quant.scale = FALSE, ...){
   if(!("modelAudit" %in% class(object))) stop("The function requires an object created with audit().")

R/modelPerformance.R

@@ -7,6 +7,15 @@
 #' @param new.score A named list of functions that take one argument: object of class ModelAudit and return a numeric value. The measure calculated by the function should have the property that lower score value indicates better model.
 #' @param ... other parameters.
 #'
+#'
+#' @examples
+#' library(MASS)
+#' model.glm <- glm(Postwt ~ Prewt + Treat + offset(Prewt), family = gaussian, data = anorexia)
+#' audit.glm <- audit(model.glm)
+#'
+#' mp.glm <- modelPerformance(audit.glm)
+#'
+#'
 #' @export
 modelPerformance <- function(object, scores = c("MAE", "MSE", "REC", "RROC"), new.score = NULL){
   if(!("modelAudit" %in% class(object))) stop("The function requires an object created with audit().")

R/modelResiduals.R

@@ -5,6 +5,13 @@
 #' @param object An object of class ModelAudit.
 #' @param variable Optional. Name of variable to order residuals. If value is NULL data order is taken. If value is "Predicted response" or "Fitted values" then data is ordered by fitted values. If value is "Observed response" the data is ordered by a vector of actual response (\code{y} parameter passed to the \code{\link{audit}} function).
 #'
+#' @examples
+#' library(MASS)
+#' model.glm <- glm(Postwt ~ Prewt + Treat + offset(Prewt), family = gaussian, data = anorexia)
+#' audit.glm <- audit(model.glm)
+#'
+#' mr.glm <- modelResiduals(audit.glm)
+#'
 #' @export
 modelResiduals <- function(object, variable = NULL){
   if(!("modelAudit" %in% class(object))) stop("The function requires an object created with audit().")

R/observationInfluence.R

@@ -5,6 +5,13 @@
 #' @param object An object of class ModelAudit.
 #' @param ... other parameters.
 #'
+#' @examples
+#' library(MASS)
+#' model.glm <- glm(Postwt ~ Prewt + Treat + offset(Prewt), family = gaussian, data = anorexia)
+#' audit.glm <- audit(model.glm)
+#'
+#' oi.glm <- observationInfluence(audit.glm)
+#'
 #' @export
 observationInfluence <- function(object, ...){