Hist2

R/average_loss.R

@@ -93,7 +93,7 @@ average_loss.default <- function(object, X, y,
 
   # Real work
   L <- as.matrix(loss(y, pred_fun(object, X, ...)))
-  M <- gwColMeans(L, g = BY, w = w)
+  M <- gwColMeans(L, g = BY, w = w)[["M"]]
 
   if (agg_cols && ncol(M) > 1L) {
     M <- cbind(rowSums(M))

R/utils_calculate.R

@@ -77,39 +77,36 @@ wcolMeans <- function(x, w = NULL) {
 
 #' Grouped wcolMeans()
 #' 
-#' Internal function used to calculate grouped column-wise weighted means.
-#' 
+#' Internal function used to calculate grouped column-wise weighted means along with
+#' corresponding (weighted) counts.
+#'
 #' @noRd
 #' @keywords internal
 #' 
 #' @param x A matrix-like object.
 #' @param g Optional grouping variable.
 #' @param w Optional case weights.
 #' @param reorder Should groups be ordered, see [rowsum()]. Default is `TRUE`.
-#' @param mean_only If `TRUE`, only the means are returned. If `FALSE`, a list
-#'   with "mean", "num" and "denom" is returned.
-#' @returns A matrix with one row per group (if `mean_only = TRUE`), or a list
-#'   with slots "mean", "num", and "denom".
+#' @returns A list with two elements: "M" represents a matrix of grouped (column) 
+#'   means, and "w" is a vector of corresponding group counts/weights.
 #' @examples 
 #' with(iris, gwColMeans(Sepal.Width, g = Species, w = Sepal.Length))
-#' with(iris, gwColMeans(Sepal.Width, g = Species, w = Sepal.Length, mean_only = FALSE))
-gwColMeans <- function(x, g = NULL, w = NULL, reorder = TRUE, mean_only = TRUE) {
+gwColMeans <- function(x, g = NULL, w = NULL, reorder = TRUE) {
   if (is.null(g)) {
-    return(rbind(wcolMeans(x, w = w)))
+    M <- rbind(wcolMeans(x, w = w))
+    denom <- if (is.null(w)) NROW(x) else sum(w)
+    return(list(M = M, w = denom))
   }
+
+  # Now the interesting case
   if (is.null(w)) {
-    num <- rowsum(x, group = g, reorder = reorder)
-    denom <- rowsum(rep.int(1, NROW(x)), group = g, reorder = reorder)
+    w <- rep.int(1, NROW(x))
   } else {
-    num <- rowsum(x * w, group = g, reorder = reorder)
-    denom <- rowsum(w, group = g, reorder = reorder)
-  }
-  out <- num / matrix(denom, nrow = nrow(num), ncol = ncol(num), byrow = FALSE)
-
-  if (mean_only) {
-    return(out)
+    x <- x * w  # w is correctly recycled over columns
   }
-  list(mean = out, num = num, denom = denom)
+  num <- rowsum(x, group = g, reorder = reorder)
+  denom <- as.numeric(rowsum(w, group = g, reorder = reorder))
+  list(M = num / denom, w = denom)
 }
 
 #' Weighted Mean Centering

R/utils_grid.R

@@ -14,7 +14,7 @@
 #' If `strategy = "quantile"`, the evaluation points are quantiles over a regular grid 
 #' of probabilities from `trim[1]` to `trim[2]`.
 #' 
-#' All quantiles are calculated via the inverse of the ECDF, i.e., via
+#' Quantiles are calculated via the inverse of the ECDF, i.e., via
 #' `stats::quantile(..., type = 1`).
 #' 
 #' @param z A vector or factor.
@@ -24,7 +24,7 @@
 #'   of grid values. Set to `0:1` for no trimming.
 #' @param strategy How to find grid values of non-discrete numeric columns? 
 #'   Either "uniform" or "quantile", see description of [univariate_grid()].
-#' @param na.rm Should missing values be dropped from grid? Default is `TRUE`.
+#' @param na.rm Should missing values be dropped from the grid? Default is `TRUE`.
 #' @returns A vector or factor of evaluation points.
 #' @seealso [multivariate_grid()]
 #' @export
@@ -33,8 +33,8 @@
 #' univariate_grid(rev(iris$Species))                       # Same
 #' 
 #' x <- iris$Sepal.Width
-#' univariate_grid(x, grid_size = 5)                        # Quantile binning
-#' univariate_grid(x, grid_size = 5, strategy = "uniform")  # Uniform
+#' univariate_grid(x, grid_size = 5)                        # Uniform binning
+#' univariate_grid(x, grid_size = 5, strategy = "quantile")  # Quantile
 univariate_grid <- function(z, grid_size = 49L, trim = c(0.01, 0.99), 
                             strategy = c("uniform", "quantile"), na.rm = TRUE) {
   strategy <- match.arg(strategy)
@@ -50,9 +50,12 @@ univariate_grid <- function(z, grid_size = 49L, trim = c(0.01, 0.99),
     g <- stats::quantile(z, probs = p, names = FALSE, type = 1L, na.rm = TRUE)
     out <- unique(g)
   } else {
-    # strategy = "uniform" (could use range() if trim = 0:1)
-    r <- stats::quantile(z, probs = trim, names = FALSE, type = 1L, na.rm = TRUE)
-    # pretty(r, n = grid_size)  # Until version 0.2.0
+    # strategy = "uniform"
+    if (trim[1L] == 0 && trim[2L] == 1) {
+      r <- range(z, na.rm = TRUE)
+    } else {
+      r <- stats::quantile(z, probs = trim, names = FALSE, type = 1L, na.rm = TRUE)
+    }
     out <- seq(r[1L], r[2L], length.out = grid_size)  
   }
   if (!na.rm && anyNA(z)) {

backlog/calibration.R

@@ -281,3 +281,42 @@ plot.calibration <- function(x,
   }
   p
 }
+
+#' Histogram Bin Construction
+#' 
+#' Creates histogram of vector/factor `x`. In the discrete case, no binning is done.
+#' Otherwise, the values are optionally trimmed and then passed to [hist()]. Compared
+#' with [hist()], the function also returns the binned values of `x`.
+#' 
+#' @param x A vector or factor to be binned.
+#' @inheritParams hist
+#' @inheritParams univariate_grid
+#' @returns A list with binned "x", vector of "breaks", bin midpoints "grid", and a
+#'   logical flag "discrete" indicating whether the values have not been binned.
+#' @seealso See [calibration()] for examples.
+hist2 <- function(x, breaks = 17L, trim = c(0.01, 0.99), 
+                  include.lowest = TRUE, right = TRUE, na.rm = TRUE) {
+  g <- unique(x)
+  if (!is.numeric(x) || (length(breaks) == 1L && is.numeric(breaks) && length(g) <= breaks)) {
+    g <- sort(g, na.last = if (na.rm) NA else TRUE)
+    return(list(x = x, breaks = g, grid = g, discrete = TRUE))
+  }
+
+  # Trim outliers before histogram construction?
+  if (trim[1L] == 0 && trim[2L] == 1) {
+    xx <- x
+  } else {
+    r <- stats::quantile(x, probs = trim, names = FALSE, type = 1L, na.rm = TRUE)
+    xx <- x[x >= r[1L] & x <= r[2L]]
+  }
+  h <- hist(xx, breaks = breaks, include.lowest = include.lowest, right = right)
+  b <- h$breaks
+  ix <- findInterval(
+    x, vec = b, left.open = right, rightmost.closed = include.lowest, all.inside = TRUE
+  )
+  g <- h$mids
+  if (!na.rm && anyNA(x)) {
+    g <- c(g, NA)
+  }
+  list(x = g[ix], breaks = b, grid = g, discrete = FALSE)
+}

tests/testthat/test_calculate.R

@@ -71,31 +71,31 @@ test_that("gwcolMeans() works", {
   w2 <- 1:6
 
   # Ungrouped
-  expect_equal(gwColMeans(x), rbind(wcolMeans(x)))
-  expect_equal(gwColMeans(x, w = w1), rbind(wcolMeans(x, w = w1)))
-  expect_equal(gwColMeans(x, w = w2), rbind(wcolMeans(x, w = w2)))
+  r <- gwColMeans(x)
+  expect_equal(r$M, rbind(wcolMeans(x)))
+  expect_equal(r$w, nrow(x))
 
-  # Grouped
-  expect_equal(gwColMeans(x, g = g)[2L, ], wcolMeans(x[g == 2, ]))
-  expect_equal(gwColMeans(x, g = g, reorder = FALSE)[2L, ], wcolMeans(x[g == 1, ]))
+  r <- gwColMeans(x, w = w1)
+  expect_equal(r$M, rbind(wcolMeans(x, w = w1)))
+  expect_equal(r$w, sum(w1))
 
-  g1 <- gwColMeans(x, g = g)
-  g2 <- gwColMeans(x, g = g, mean_only = FALSE)
-  g2_d <- matrix(g2$denom, nrow = 2, ncol = 2, byrow = FALSE)
-  expect_equal(g1, g2$num / g2_d)
-  expect_equal(g2$mean, g1)
+  r <- gwColMeans(x, w = w2)
+  expect_equal(r$M, rbind(wcolMeans(x, w = w2)))
+  expect_equal(r$w, sum(w2))
 
-  # Grouped and weighted
-  expect_equal(
-    gwColMeans(x, g = g, w = w2)[2L, ], 
-    wcolMeans(x[g == 2, ], w = w2[g == 2])
-  )
+  # Grouped
+  r <- gwColMeans(x, g = g)
+  expect_equal(r$M[2L, ], wcolMeans(x[g == 2, ]))
+  expect_equal(r$w, c(4, 2))
 
-  g1 <- gwColMeans(x, g = g, w = w2)
-  g2 <- gwColMeans(x, g = g, w = w2, mean_only = FALSE)
-  g2_d <- matrix(g2$denom, nrow = 2, ncol = 2, byrow = FALSE)
-  expect_equal(g1, g2$num / g2_d)
-  expect_equal(g2$mean, g1)
+  r <- gwColMeans(x, g = g, reorder = FALSE)
+  expect_equal(r$M[2L, ], wcolMeans(x[g == 1, ]))
+  expect_equal(r$w, c(2, 4))
+
+  # Grouped and weighted
+  r <- gwColMeans(x, g = g, w = w2)
+  expect_equal(r$M[2L, ], wcolMeans(x[g == 2, ], w = w2[g == 2]))
+  expect_equal(r$w, c(sum(3:6), sum(1:2)))
 })
 
 test_that("wcenter() works for matrices with > 1 columns", {