consistent behavior for ordered variables (#19)

* consistent behavior for ordered variables * add unit tests
tnagler · May 3, 2018 · ea6cf31 · ea6cf31
1 parent 0e3d439
commit ea6cf31
Show file tree

Hide file tree

Showing 2 changed files with 24 additions and 15 deletions.
diff --git a/R/kde1d_methods.R b/R/kde1d_methods.R
@@ -37,14 +37,14 @@ dkde1d <- function(x, obj) {
         x <- x[[1]]
     if (!is.ordered(x))
         stopifnot(!is.factor(x))
+    if (length(obj$jitter_info$i_disc) == 1 & !is.ordered(x))
+        x <- ordered(x, obj$jitter_info$levels$x)
 
-    x <- expand_as_numeric(x)
     fhat <- dkde1d_cpp(x, obj)
 
     if (length(obj$jitter_info$i_disc) == 1) {
         # for discrete variables we can normalize
-        x_all_num <- expand_as_numeric(as.ordered(obj$jitter_info$levels$x))
-        f_all <- dkde1d_cpp(x_all_num, obj)
+        f_all <- dkde1d_cpp(as.ordered(obj$jitter_info$levels$x), obj)
         fhat <- fhat / sum(f_all)
     }
 
@@ -60,15 +60,14 @@ pkde1d <- function(q, obj) {
     if (!is.ordered(q))
         stopifnot(!is.factor(q))
 
-    q <- expand_as_numeric(q)
-
     if (length(obj$jitter_info$i_disc) != 1) {
         p <- pkde1d_cpp(q, obj)
     } else {
-        # for discrete variables we have to add the missing probability mass
-        x_all_num <- expand_as_numeric(as.ordered(obj$jitter_info$levels$x))
-        f_all <- dkde1d(x_all_num, obj)
-        p <- sapply(q, function(y) sum(f_all[x_all_num <= y]))
+        if (!is.ordered(q))
+            q <- ordered(q, obj$jitter_info$levels$x)
+        x_all <- as.ordered(obj$jitter_info$levels$x)
+        p_all <- dkde1d(x_all, obj)
+        p <- sapply(q, function(y) sum(p_all[x_all <= y]))
         p <- pmin(pmax(p, 0), 1)
     }
 
@@ -81,19 +80,17 @@ pkde1d <- function(q, obj) {
 qkde1d <- function(p, obj) {
     stopifnot(all(na.omit(p) > 0.0) & all(na.omit(p) < 1.0))
     if (length(obj$jitter_info$i_disc) != 1) {
+        ## qkde1d_cpp for continuous variables
         q <- qkde1d_cpp(p, obj)
     } else {
         ## for discrete variables compute quantile from the density
-        x_all_num <- expand_as_numeric(as.ordered(obj$jitter_info$levels$x))
+        x_all <- as.ordered(obj$jitter_info$levels$x)
 
         # pdf at all possible values of x
-        dd <- dkde1d(x_all_num, obj)
-        pp <- c(cumsum(dd)) / sum(dd)
+        pp <- pkde1d(x_all, obj)
 
         # generalized inverse
-        q <- x_all_num[vapply(p, function(y) which(y <= pp)[1], integer(1))]
-        q <- ordered(obj$jitter_info$levels$x[q + 1],
-                     levels = obj$jitter_info$levels$x)
+        q <- x_all[vapply(p, function(y) which(y <= pp)[1], integer(1))]
     }
 
     q

diff --git a/tests/testthat/test_kde1d.R b/tests/testthat/test_kde1d.R
@@ -108,4 +108,16 @@ test_that("other generics work", {
     }
 
     lapply(fits, test_other_generics)
+})
+
+test_that("behavior for discrete data is consistent", {
+    n <- 1e3
+    x <- ordered(sample(5, n, TRUE), 1:5)
+    fit <- kde1d(x)
+    xx <- ordered(1:5, 1:5)
+    expect_equal(dkde1d(1:5, fit), dkde1d(xx, fit))
+    expect_equal(pkde1d(1:5, fit), pkde1d(xx, fit))
+    expect_true(all(is.na(dkde1d(c(0, 6), fit))))
+    expect_true(all(is.na(pkde1d(c(0, 6), fit))))
+    expect_true(all(rkde1d(n, fit) %in% x))
 })