R Under development (unstable) (2024-09-02 r87090 ucrt) -- "Unsuffered Consequences"
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Platform: x86_64-w64-mingw32/x64

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> library("matrixStats")
> 
> ## Create isFALSE() if running on an old version of R
> if (!exists("isFALSE", mode="function")) {
+   isFALSE <- function(x) is.logical(x) && length(x) == 1L && !is.na(x) && !x
+ }
> 
> rowWeightedMeans_R <- function(x, w, na.rm = FALSE, ..., useNames = NA) {
+   res <- apply(x, MARGIN = 1L, FUN = weighted.mean, w = w, na.rm = na.rm, ...)
+   
+   # Keep naming support consistency same as rowWeightedMeans()
+   idxs <- which(is.na(w) | w != 0)
+   nw <- length(idxs)
+   if (na.rm) na.rm <- anyMissing(x)
+   if ((!is.null(w) && nw == 0L) || isFALSE(na.rm)) {
+     if (is.na(useNames) || !useNames) names(res) <- NULL
+   }
+   else if (isFALSE(useNames)) names(res) <- NULL
+   
+   res
+ }
> 
> colWeightedMeans_R <- function(x, w, na.rm = FALSE, ..., useNames = NA) {
+   res <- apply(x, MARGIN = 2L, FUN = weighted.mean, w = w, na.rm = na.rm, ...)
+   
+   # Keep naming support consistency same as colWeightedMeans()
+   idxs <- which(is.na(w) | w != 0)
+   nw <- length(idxs)
+   if (!is.null(w) && nw == 0L) {
+     if (is.na(useNames) || !useNames) names(res) <- NULL
+   }
+   else if (isFALSE(useNames)) names(res) <- NULL
+   
+   res
+ }
> 
> set.seed(1)
> 
> x <- matrix(rnorm(20), nrow = 5, ncol = 4)
> print(x)
           [,1]       [,2]       [,3]        [,4]
[1,] -0.6264538 -0.8204684  1.5117812 -0.04493361
[2,]  0.1836433  0.4874291  0.3898432 -0.01619026
[3,] -0.8356286  0.7383247 -0.6212406  0.94383621
[4,]  1.5952808  0.5757814 -2.2146999  0.82122120
[5,]  0.3295078 -0.3053884  1.1249309  0.59390132
> 
> # To check names attribute
> dimnames <- list(letters[1:5], LETTERS[1:4])
> 
> # Non-weighted row averages
> x_est0 <- rowMeans(x)
> x_est1 <- rowWeightedMeans(x)
> print(x_est1)
[1] 0.004981341 0.261181337 0.056322931 0.194395865 0.435737906
> stopifnot(all.equal(x_est1, x_est0))
> x_est2 <- colWeightedMeans(t(x))
> stopifnot(all.equal(x_est2, x_est0))
> # Check names attribute
> dimnames(x) <- dimnames
> x_est1 <- rowWeightedMeans(x, useNames = FALSE)
> x_est2 <- colWeightedMeans(t(x), useNames = FALSE)
> stopifnot(all.equal(x_est1, x_est0))
> stopifnot(all.equal(x_est2, x_est0))
> x_est0 <- rowMeans(x)
> x_est1 <- rowWeightedMeans(x, useNames = TRUE)
> x_est2 <- colWeightedMeans(t(x), useNames = TRUE)
> stopifnot(all.equal(x_est1, x_est0))
> stopifnot(all.equal(x_est2, x_est0))
> dimnames(x) <- NULL
> 
> 
> # Weighted row averages (uniform weights)
> w <- rep(2.5, times = ncol(x))
> x_est0 <- rowMeans(x)
> x_est1 <- rowWeightedMeans(x, w = w)
> print(x_est1)
[1] 0.004981341 0.261181337 0.056322931 0.194395865 0.435737906
> stopifnot(all.equal(x_est1, x_est0))
> x_est2 <- colWeightedMeans(t(x), w = w)
> stopifnot(all.equal(x_est2, x_est0))
> # Test with and without dimnames on x
> for (setDimnames in c(TRUE, FALSE)) {
+   if (setDimnames) dimnames(x) <- dimnames
+   else dimnames(x) <- NULL    
+   # Check names attribute
+   for (useNames in c(if (!matrixStats:::isUseNamesNADefunct()) NA, TRUE, FALSE)) {
+     x_est0 <- rowWeightedMeans_R(x, w = w, useNames = useNames)
+     x_est1 <- rowWeightedMeans(x, w = w, useNames = useNames)
+     stopifnot(all.equal(x_est1, x_est0))
+     x_est0 <- colWeightedMeans_R(t(x), w = w, useNames = useNames)
+     x_est1 <- colWeightedMeans(t(x), w = w, useNames = useNames)
+     stopifnot(all.equal(x_est1, x_est0))
+   }
+ }
> 
> 
> # Weighted row averages (excluding some columns)
> w <- c(1, 1, 0, 1)
> x_est0 <- rowMeans(x[, (w == 1), drop = FALSE])
> x_est1 <- rowWeightedMeans(x, w = w)
> print(x_est1)
[1] -0.4972853  0.2182940  0.2821774  0.9974278  0.2060069
> stopifnot(all.equal(x_est1, x_est0))
> x_est2 <- colWeightedMeans(t(x), w = w)
> stopifnot(all.equal(x_est2, x_est0))
> # Test with and without dimnames on x
> for (setDimnames in c(TRUE, FALSE)) {
+   if (setDimnames) dimnames(x) <- dimnames
+   else dimnames(x) <- NULL    
+   # Check names attribute
+   for (useNames in c(if (!matrixStats:::isUseNamesNADefunct()) NA, TRUE, FALSE)) {
+     x_est0 <- rowWeightedMeans_R(x, w = w, useNames = useNames)
+     x_est1 <- rowWeightedMeans(x, w = w, useNames = useNames)
+     stopifnot(all.equal(x_est1, x_est0))
+     x_est0 <- colWeightedMeans_R(t(x), w = w, useNames = useNames)
+     x_est1 <- colWeightedMeans(t(x), w = w, useNames = useNames)
+     stopifnot(all.equal(x_est1, x_est0))
+   }
+ }
> 
> 
> # Weighted row averages (excluding some columns)
> w <- c(0, 1, 0, 0)
> x_est0 <- rowMeans(x[, (w == 1), drop = FALSE])
> x_est1 <- rowWeightedMeans(x, w = w)
> stopifnot(all.equal(x_est1, x_est0))
> x_est2 <- colWeightedMeans(t(x), w = w)
> stopifnot(all.equal(x_est2, x_est0))
> # Test with and without dimnames on x
> for (setDimnames in c(TRUE, FALSE)) {
+   if (setDimnames) dimnames(x) <- dimnames
+   else dimnames(x) <- NULL    
+   # Check names attribute
+   for (useNames in c(if (!matrixStats:::isUseNamesNADefunct()) NA, TRUE, FALSE)) {
+     x_est0 <- rowWeightedMeans_R(x, w = w, useNames = useNames)
+     x_est1 <- rowWeightedMeans(x, w = w, useNames = useNames)
+     stopifnot(all.equal(x_est1, x_est0))
+     x_est0 <- colWeightedMeans_R(t(x), w = w, useNames = useNames)
+     x_est1 <- colWeightedMeans(t(x), w = w, useNames = useNames)
+     stopifnot(all.equal(x_est1, x_est0))
+   }
+ }
> 
> 
> # Weighted row averages (all zero weights)
> w <- c(0, 0, 0, 0)
> x_est0 <- rowMeans(x[, (w == 1), drop = FALSE])
> x_est1 <- rowWeightedMeans(x, w = w)
> stopifnot(all.equal(x_est1, x_est0))
> x_est2 <- colWeightedMeans(t(x), w = w)
> stopifnot(all.equal(x_est2, x_est0))
> # Test with and without dimnames on x
> for (setDimnames in c(TRUE, FALSE)) {
+   if (setDimnames) dimnames(x) <- dimnames
+   else dimnames(x) <- NULL    
+   # Check names attribute
+   for (useNames in c(if (!matrixStats:::isUseNamesNADefunct()) NA, TRUE, FALSE)) {
+     x_est0 <- rowWeightedMeans_R(x, w = w, useNames = useNames)
+     x_est1 <- rowWeightedMeans(x, w = w, useNames = useNames)
+     stopifnot(all.equal(x_est1, x_est0))
+     x_est0 <- colWeightedMeans_R(t(x), w = w, useNames = useNames)
+     x_est1 <- colWeightedMeans(t(x), w = w, useNames = useNames)
+     stopifnot(all.equal(x_est1, x_est0))
+   }
+ }
> 
> 
> # Weighted averages by rows and columns
> w <- 1:4
> x_est1 <- rowWeightedMeans(x, w = w)
> print(x_est1)
[1]  0.20882185  0.22632701  0.25526439 -0.06123714  0.54691290
> x_est2 <- colWeightedMeans(t(x), w = w)
> stopifnot(all.equal(x_est2, x_est1))
> # Test with and without dimnames on x
> for (setDimnames in c(TRUE, FALSE)) {
+   if (setDimnames) dimnames(x) <- dimnames
+   else dimnames(x) <- NULL    
+   # Check names attribute
+   for (useNames in c(if (!matrixStats:::isUseNamesNADefunct()) NA, TRUE, FALSE)) {
+     x_est0 <- rowWeightedMeans_R(x, w = w, useNames = useNames)
+     x_est1 <- rowWeightedMeans(x, w = w, useNames = useNames)
+     stopifnot(all.equal(x_est1, x_est0))
+     x_est0 <- colWeightedMeans_R(t(x), w = w, useNames = useNames)
+     x_est1 <- colWeightedMeans(t(x), w = w, useNames = useNames)
+     stopifnot(all.equal(x_est1, x_est0))
+   }
+ }
> 
> 
> x[sample(length(x), size = 0.3 * length(x))] <- NA
> print(x)
           [,1]      [,2]       [,3]        [,4]
[1,] -0.6264538        NA  1.5117812 -0.04493361
[2,]  0.1836433        NA         NA -0.01619026
[3,] -0.8356286 0.7383247 -0.6212406  0.94383621
[4,]  1.5952808        NA -2.2146999  0.82122120
[5,]  0.3295078        NA         NA  0.59390132
> 
> # Non-weighted row averages with missing values
> x_est0 <- rowMeans(x, na.rm = TRUE)
> x_est1 <- rowWeightedMeans(x, na.rm = TRUE)
> print(x_est1)
[1] 0.28013125 0.08372653 0.05632293 0.06726737 0.46170455
> stopifnot(all.equal(x_est1, x_est0))
> x_est2 <- colWeightedMeans(t(x), na.rm = TRUE)
> stopifnot(all.equal(x_est2, x_est0))
> # Check names attribute
> dimnames(x) <- dimnames
> x_est1 <- rowWeightedMeans(x, na.rm = TRUE, useNames = FALSE)
> x_est2 <- colWeightedMeans(t(x), na.rm = TRUE, useNames = FALSE)
> stopifnot(all.equal(x_est1, x_est0))
> stopifnot(all.equal(x_est2, x_est0))
> x_est0 <- rowMeans(x, na.rm = TRUE)
> x_est1 <- rowWeightedMeans(x, na.rm = TRUE, useNames = TRUE)
> x_est2 <- colWeightedMeans(t(x), na.rm = TRUE, useNames = TRUE)
> stopifnot(all.equal(x_est1, x_est0))
> stopifnot(all.equal(x_est2, x_est0))
> dimnames(x) <- NULL
> 
> 
> # Weighted row averages with missing values
> x_est0 <- apply(x, MARGIN = 1L, FUN = weighted.mean, w = w, na.rm = TRUE)
> print(x_est0)
[1]  0.46614441  0.02377645  0.25526439 -0.22049176  0.54102261
> x_est1 <- rowWeightedMeans(x, w = w, na.rm = TRUE)
> print(x_est1)
[1]  0.46614441  0.02377645  0.25526439 -0.22049176  0.54102261
> stopifnot(all.equal(x_est1, x_est0))
> x_est2 <- colWeightedMeans(t(x), w = w, na.rm = TRUE)
> stopifnot(all.equal(x_est2, x_est0))
> # Test with and without dimnames on x
> for (setDimnames in c(TRUE, FALSE)) {
+   if (setDimnames) dimnames(x) <- dimnames
+   else dimnames(x) <- NULL    
+   # Check names attribute
+   for (useNames in c(if (!matrixStats:::isUseNamesNADefunct()) NA, TRUE, FALSE)) {
+     x_est0 <- rowWeightedMeans_R(x, w = w, na.rm = TRUE, useNames = useNames)
+     x_est1 <- rowWeightedMeans(x, w = w, na.rm = TRUE, useNames = useNames)
+     stopifnot(all.equal(x_est1, x_est0))
+     x_est0 <- colWeightedMeans_R(t(x), w = w, na.rm = TRUE, useNames = useNames)
+     x_est1 <- colWeightedMeans(t(x), w = w, na.rm = TRUE, useNames = useNames)
+     stopifnot(all.equal(x_est1, x_est0))
+   }
+ }
> 
> 
> # Weighted averages by rows and columns
> w <- 1:4
> x_est1 <- rowWeightedMeans(x, w = w, na.rm = TRUE)
> x_est2 <- colWeightedMeans(t(x), w = w, na.rm = TRUE)
> stopifnot(all.equal(x_est2, x_est1))
> # Test with and without dimnames on x
> for (setDimnames in c(TRUE, FALSE)) {
+   if (setDimnames) dimnames(x) <- dimnames
+   else dimnames(x) <- NULL    
+   # Check names attribute
+   for (useNames in c(if (!matrixStats:::isUseNamesNADefunct()) NA, TRUE, FALSE)) {
+     x_est0 <- rowWeightedMeans_R(x, w = w, na.rm = TRUE, useNames = useNames)
+     x_est1 <- rowWeightedMeans(x, w = w, na.rm = TRUE, useNames = useNames)
+     stopifnot(all.equal(x_est1, x_est0))
+     x_est0 <- colWeightedMeans_R(t(x), w = w, na.rm = TRUE, useNames = useNames)
+     x_est1 <- colWeightedMeans(t(x), w = w, na.rm = TRUE, useNames = useNames)
+     stopifnot(all.equal(x_est1, x_est0))
+   }
+ }
> 
> 
> # w contains missing value
> w[1] <- NA_integer_
> x_est1 <- rowWeightedMeans(x, w = w, na.rm = TRUE)
> x_est2 <- colWeightedMeans(t(x), w = w, na.rm = TRUE)
> stopifnot(all.equal(x_est2, x_est1))
> # Test with and without dimnames on x
> for (setDimnames in c(TRUE, FALSE)) {
+   if (setDimnames) dimnames(x) <- dimnames
+   else dimnames(x) <- NULL    
+   # Check names attribute
+   for (useNames in c(if (!matrixStats:::isUseNamesNADefunct()) NA, TRUE, FALSE)) {
+     x_est0 <- rowWeightedMeans_R(x, w = w, na.rm = TRUE, useNames = useNames)
+     x_est1 <- rowWeightedMeans(x, w = w, na.rm = TRUE, useNames = useNames)
+     stopifnot(all.equal(x_est1, x_est0))
+     x_est0 <- colWeightedMeans_R(t(x), w = w, na.rm = TRUE, useNames = useNames)
+     x_est1 <- colWeightedMeans(t(x), w = w, na.rm = TRUE, useNames = useNames)
+     stopifnot(all.equal(x_est1, x_est0))
+   }
+ }
> 
> x_est1 <- rowWeightedMeans(x, w = w, na.rm = FALSE)
> x_est2 <- colWeightedMeans(t(x), w = w, na.rm = FALSE)
> stopifnot(all.equal(x_est2, x_est1))
> # Test with and without dimnames on x
> for (setDimnames in c(TRUE, FALSE)) {
+   if (setDimnames) dimnames(x) <- dimnames
+   else dimnames(x) <- NULL    
+   # Check names attribute
+   for (useNames in c(if (!matrixStats:::isUseNamesNADefunct()) NA, TRUE, FALSE)) {
+     x_est0 <- rowWeightedMeans_R(x, w = w, na.rm = FALSE, useNames = useNames)
+     x_est1 <- rowWeightedMeans(x, w = w, na.rm = FALSE, useNames = useNames)
+     stopifnot(all.equal(x_est1, x_est0))
+     x_est0 <- colWeightedMeans_R(t(x), w = w, na.rm = FALSE, useNames = useNames)
+     x_est1 <- colWeightedMeans(t(x), w = w, na.rm = FALSE, useNames = useNames)
+     stopifnot(all.equal(x_est1, x_est0))
+   }
+ }
> 
> proc.time()
   user  system elapsed 
   0.46    0.01    0.46