From b9c5f647bc455cf3eff2eb6225d5acae83f7caa6 Mon Sep 17 00:00:00 2001
From: KelvynBladen <kelvynbladen@gmail.com>
Date: Fri, 1 Dec 2023 13:04:42 -0700
Subject: [PATCH] Move nice_plot_breaks to misc dev

---
 NAMESPACE               |   1 -
 R/nice_plot_breaks.R    | 122 ----------------------------------------
 man/nice_plot_breaks.Rd |  27 ---------
 man/pdp_compare.Rd      |  30 +---------
 4 files changed, 1 insertion(+), 179 deletions(-)
 delete mode 100644 R/nice_plot_breaks.R
 delete mode 100644 man/nice_plot_breaks.Rd

diff --git a/NAMESPACE b/NAMESPACE
index 6dfee06..6239302 100644
--- a/NAMESPACE
+++ b/NAMESPACE
@@ -4,7 +4,6 @@ export(caret_plot)
 export(ggvip)
 export(mtry_compare)
 export(mtry_pdp_compare)
-export(nice_plot_breaks)
 export(partial_cor)
 export(pdp_compare)
 export(robust_vifs)
diff --git a/R/nice_plot_breaks.R b/R/nice_plot_breaks.R
deleted file mode 100644
index 7ad7397..0000000
--- a/R/nice_plot_breaks.R
+++ /dev/null
@@ -1,122 +0,0 @@
-#' Generates Nice Plot Breaks and Limits from Data Range
-#' @name nice_plot_breaks
-#' @importFrom dplyr %>% summarise group_by case_when relocate
-#' @importFrom ggplot2 ggplot geom_point geom_line geom_tile ggtitle facet_grid
-#'   scale_fill_gradient aes scale_x_continuous scale_y_continuous
-#' @description This function uses caret grid training results to generate
-#'   performance data.frames, heatmaps, and other plots for comparing the
-#'   performance of models across their hyper-parameters and evaluating
-#'   interactions between different model hyper-parameters.
-#' @param x An object of class train.
-#' @param sqrt Boolean value indicating whether assessment metrics should be
-#'   adjusted via a square root transformation. Default is FALSE.
-#' @return A list of caret training performance data.frames, heatmaps, and
-#'   plots.
-#' @examples
-#' b <- nice_plot_breaks(x)
-#' @export
-
-nice_plot_breaks <- function(vec) {
-  upp <- max(vec)
-  low <- min(vec)
-  d_range <- upp - low
-  v <- 10^(-3:6)
-
-  max(abs(vec))
-  #compare abs(u) to abs(l)
-  #max(abs(vec))
-  # use just the interval for the larger one??
-
-  indr <- findInterval(d_range, v)
-
-  indu <- findInterval(abs(upp), v)
-  new_upp <- upp / (10^(indu - 5))
-  round_upp <- ceiling(new_upp / 10) * 10
-
-  indl <- findInterval(abs(low), v)
-  new_low <- low / (10^(indl - 5))
-  round_low <- floor(new_low / 10) * 10
-
-  # indl <- findInterval(abs(low), v)
-  # new_low <- low / (10^(indu - 5))
-  # round_low <- floor(new_low / 10) * 10
-
-  new_range <- new_upp - new_low
-  round_range <- round_upp - round_low
-
-  if (any(na.omit(c(new_range / round_range,
-                    round_low / new_low, new_upp / round_upp)) < 3 / 4)) {
-    round_upp <- ceiling(new_upp / 4) * 4
-    round_low <- floor(new_low / 4) * 4
-  }
-
-  new_upp <- round_upp * (10^(indu - 5))
-  new_low <- round_low * (10^(indl - 5))
-  new_range <- new_upp - new_low
-
-  div <- case_when(
-    round_range %% 5 == 0 ~ 5,
-    round_range %% 4 == 0 ~ 4,
-    round_range %% 3 == 0 ~ 3,
-    .default = 4
-  )
-
-  l <- list()
-  l$min <- new_low
-  l$max <- new_upp
-  l$breaks <- seq(new_low, new_upp, by = new_range / div)
-  l
-}
-# n <- nice_plot_breaks(vec = c(-13, 117))
-# n
-
-################################################################################
-# vec = c(-117, 6)
-nice_plot_breaks <- function(vec) {
-  vec = c(min(vec), max(vec))
-  d_range <- vec[2] - vec[1]
-  v <- 10^(-3:6)
-
-  #compare abs(u) to abs(l)
-  #max(abs(vec))
-  # use just the interval for the larger one??
-
-  indr <- findInterval(d_range, v)
-
-  ind <- findInterval(max(abs(vec)), v)
-
-  i = which.max(abs(vec))
-
-  new_vec <- vec / (10^(ind - 5))
-
-  round_vec <- c(floor(new_vec[which.min(new_vec)] / 10) * 10,
-                 ceiling(new_vec[which.max(new_vec)] / 10) * 10)
-
-  new_range <- max(new_vec) - min(new_vec)
-  round_range <- max(round_vec) - min(round_vec)
-
-  if (any(na.omit(c(new_range / round_range,
-                    new_vec[i]/round_vec[i])) < 3 / 4)) {
-    round_vec <- c(floor(new_vec[which.min(new_vec)] / 4) * 4,
-                   ceiling(new_vec[which.max(new_vec)] / 4) * 4)
-  }
-
-  new_upp <- round_upp * (10^(indu - 5))
-  new_low <- round_low * (10^(indl - 5))
-  new_range <- new_upp - new_low
-
-  div <- case_when(
-    round_range %% 5 == 0 ~ 5,
-    round_range %% 4 == 0 ~ 4,
-    round_range %% 3 == 0 ~ 3,
-    .default = 4
-  )
-
-  l <- list()
-  l$min <- new_low
-  l$max <- new_upp
-  l$breaks <- seq(new_low, new_upp, by = new_range / div)
-  l
-}
-# n <- nice_plot_breaks(vec = c(-13, 117))
-# n
diff --git a/man/nice_plot_breaks.Rd b/man/nice_plot_breaks.Rd
deleted file mode 100644
index 7f00aa2..0000000
--- a/man/nice_plot_breaks.Rd
+++ /dev/null
@@ -1,27 +0,0 @@
-% Generated by roxygen2: do not edit by hand
-% Please edit documentation in R/nice_plot_breaks.R
-\name{nice_plot_breaks}
-\alias{nice_plot_breaks}
-\title{Generates Nice Plot Breaks and Limits from Data Range}
-\usage{
-nice_plot_breaks(vec)
-}
-\arguments{
-\item{x}{An object of class train.}
-
-\item{sqrt}{Boolean value indicating whether assessment metrics should be
-adjusted via a square root transformation. Default is FALSE.}
-}
-\value{
-A list of caret training performance data.frames, heatmaps, and
-  plots.
-}
-\description{
-This function uses caret grid training results to generate
-  performance data.frames, heatmaps, and other plots for comparing the
-  performance of models across their hyper-parameters and evaluating
-  interactions between different model hyper-parameters.
-}
-\examples{
-b <- nice_plot_breaks(x)
-}
diff --git a/man/pdp_compare.Rd b/man/pdp_compare.Rd
index 43a696c..97b9977 100644
--- a/man/pdp_compare.Rd
+++ b/man/pdp_compare.Rd
@@ -1,21 +1,9 @@
 % Generated by roxygen2: do not edit by hand
-% Please edit documentation in R/pdp_compare.R, R/pdp_compare_bivariate.R
+% Please edit documentation in R/pdp_compare.R
 \name{pdp_compare}
 \alias{pdp_compare}
 \title{Small Multiple PDPs and Importance Metrics}
 \usage{
-pdp_compare(
-  x,
-  var_vec,
-  scale = FALSE,
-  sqrt = TRUE,
-  trim = 0.1,
-  trellis = TRUE,
-  which_class = 2L,
-  prob = TRUE,
-  ...
-)
-
 pdp_compare(
   x,
   var_vec,
@@ -61,21 +49,11 @@ function is on a scale similar to the logit. Default is TRUE.}
 \item{...}{Other parameters to pass to the partial function.}
 }
 \value{
-A list of partial dependence plots with adjusted y-axes so all
-  are on an identical scale. This list includes a comparative facet plot and
-  pdp importance values for assessing true affect of predictors on response.
-
 A list of partial dependence plots with adjusted y-axes so all
   are on an identical scale. This list includes a comparative facet plot and
   pdp importance values for assessing true affect of predictors on response.
 }
 \description{
-This function takes a randomForest object, generates partial
-  dependence plots for predictors and converts them to small multiples for
-  appropriate comparison. Output is a list containing a comparative grid
-  of PDPs, individual partial dependence plots, and PDP-derived
-  importance values for assessing effect of predictors on response.
-
 This function takes a randomForest object, generates partial
   dependence plots for predictors and converts them to small multiples for
   appropriate comparison. Output is a list containing a comparative grid
@@ -87,12 +65,6 @@ mtcars.rf <- randomForest::randomForest(formula = mpg ~ ., data = mtcars)
 car_pd <- pdp_compare(x = mtcars.rf)
 car_pd$full
 car_pd$imp
-gridExtra::grid.arrange(car_pd$wt, car_pd$disp,
-  car_pd$hp, car_pd$cyl, nrow = 2)
-mtcars.rf <- randomForest::randomForest(formula = mpg ~ ., data = mtcars)
-car_pd <- pdp_compare(x = mtcars.rf)
-car_pd$full
-car_pd$imp
 gridExtra::grid.arrange(car_pd$wt, car_pd$disp,
   car_pd$hp, car_pd$cyl, nrow = 2)
 }