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Henrik Bengtsson edited this page Sep 11, 2019 · 6 revisions

matrixStats: Benchmark report

colWeightedMeans() and rowWeightedMeans() benchmarks

This report benchmark the performance of colWeightedMeans() and rowWeightedMeans() against alternative methods.

Alternative methods

  • apply() + weighted.mean()

Data

> rmatrix <- function(nrow, ncol, mode = c("logical", "double", "integer", "index"), range = c(-100, 
+     +100), na_prob = 0) {
+     mode <- match.arg(mode)
+     n <- nrow * ncol
+     if (mode == "logical") {
+         x <- sample(c(FALSE, TRUE), size = n, replace = TRUE)
+     }     else if (mode == "index") {
+         x <- seq_len(n)
+         mode <- "integer"
+     }     else {
+         x <- runif(n, min = range[1], max = range[2])
+     }
+     storage.mode(x) <- mode
+     if (na_prob > 0) 
+         x[sample(n, size = na_prob * n)] <- NA
+     dim(x) <- c(nrow, ncol)
+     x
+ }
> rmatrices <- function(scale = 10, seed = 1, ...) {
+     set.seed(seed)
+     data <- list()
+     data[[1]] <- rmatrix(nrow = scale * 1, ncol = scale * 1, ...)
+     data[[2]] <- rmatrix(nrow = scale * 10, ncol = scale * 10, ...)
+     data[[3]] <- rmatrix(nrow = scale * 100, ncol = scale * 1, ...)
+     data[[4]] <- t(data[[3]])
+     data[[5]] <- rmatrix(nrow = scale * 10, ncol = scale * 100, ...)
+     data[[6]] <- t(data[[5]])
+     names(data) <- sapply(data, FUN = function(x) paste(dim(x), collapse = "x"))
+     data
+ }
> data <- rmatrices(mode = "double")

Results

10x10 matrix

> X <- data[["10x10"]]
> w <- runif(nrow(X))
> gc()
          used  (Mb) gc trigger  (Mb) max used  (Mb)
Ncells 3060186 163.5    5723390 305.7  5723390 305.7
Vcells 5582772  42.6   12256490  93.6 12255936  93.6
> colStats <- microbenchmark(colWeightedMeans = colWeightedMeans(X, w = w, na.rm = FALSE), `apply+weigthed.mean` = apply(X, 
+     MARGIN = 2L, FUN = weighted.mean, w = w, na.rm = FALSE), unit = "ms")
> X <- t(X)
> gc()
          used  (Mb) gc trigger  (Mb) max used  (Mb)
Ncells 3059432 163.4    5723390 305.7  5723390 305.7
Vcells 5580819  42.6   12256490  93.6 12255936  93.6
> rowStats <- microbenchmark(rowWeightedMeans = rowWeightedMeans(X, w = w, na.rm = FALSE), `apply+weigthed.mean` = apply(X, 
+     MARGIN = 1L, FUN = weighted.mean, w = w, na.rm = FALSE), unit = "ms")

Table: Benchmarking of colWeightedMeans() and apply+weigthed.mean() on 10x10 data. The top panel shows times in milliseconds and the bottom panel shows relative times.

expr min lq mean median uq max
1 colWeightedMeans 0.009636 0.010553 0.0188809 0.0124755 0.0240735 0.070946
2 apply+weigthed.mean 0.060421 0.061921 0.0890214 0.0636535 0.1211720 0.319313
expr min lq mean median uq max
1 colWeightedMeans 1.00000 1.000000 1.000000 1.00000 1.000000 1.000000
2 apply+weigthed.mean 6.27034 5.867621 4.714888 5.10228 5.033418 4.500789
Table: Benchmarking of rowWeightedMeans() and apply+weigthed.mean() on 10x10 data (transposed). The top panel shows times in milliseconds and the bottom panel shows relative times.
expr min lq mean median uq max
1 rowWeightedMeans 0.011269 0.012091 0.0134532 0.0134335 0.0138740 0.046038
2 apply+weigthed.mean 0.055986 0.057190 0.0595293 0.0581220 0.0589225 0.160270
expr min lq mean median uq max
1 rowWeightedMeans 1.000000 1.000000 1.000000 1.000000 1.000000 1.000000
2 apply+weigthed.mean 4.968143 4.729964 4.424913 4.326646 4.246973 3.481255
Figure: Benchmarking of colWeightedMeans() and apply+weigthed.mean() on 10x10 data as well as rowWeightedMeans() and apply+weigthed.mean() on the same data transposed. Outliers are displayed as crosses. Times are in milliseconds.

Table: Benchmarking of colWeightedMeans() and rowWeightedMeans() on 10x10 data (original and transposed). The top panel shows times in milliseconds and the bottom panel shows relative times.

expr min lq mean median uq max
1 colWeightedMeans 9.636 10.553 18.88091 12.4755 24.0735 70.946
2 rowWeightedMeans 11.269 12.091 13.45321 13.4335 13.8740 46.038
expr min lq mean median uq max
1 colWeightedMeans 1.000000 1.000000 1.0000000 1.00000 1.0000000 1.0000000
2 rowWeightedMeans 1.169469 1.145741 0.7125297 1.07679 0.5763184 0.6489161
Figure: Benchmarking of colWeightedMeans() and rowWeightedMeans() on 10x10 data (original and transposed). Outliers are displayed as crosses. Times are in milliseconds.

100x100 matrix

> X <- data[["100x100"]]
> w <- runif(nrow(X))
> gc()
          used  (Mb) gc trigger  (Mb) max used  (Mb)
Ncells 3058041 163.4    5723390 305.7  5723390 305.7
Vcells 5195681  39.7   12256490  93.6 12255936  93.6
> colStats <- microbenchmark(colWeightedMeans = colWeightedMeans(X, w = w, na.rm = FALSE), `apply+weigthed.mean` = apply(X, 
+     MARGIN = 2L, FUN = weighted.mean, w = w, na.rm = FALSE), unit = "ms")
> X <- t(X)
> gc()
          used  (Mb) gc trigger  (Mb) max used  (Mb)
Ncells 3058035 163.4    5723390 305.7  5723390 305.7
Vcells 5205724  39.8   12256490  93.6 12256404  93.6
> rowStats <- microbenchmark(rowWeightedMeans = rowWeightedMeans(X, w = w, na.rm = FALSE), `apply+weigthed.mean` = apply(X, 
+     MARGIN = 1L, FUN = weighted.mean, w = w, na.rm = FALSE), unit = "ms")

Table: Benchmarking of colWeightedMeans() and apply+weigthed.mean() on 100x100 data. The top panel shows times in milliseconds and the bottom panel shows relative times.

expr min lq mean median uq max
1 colWeightedMeans 0.028383 0.033486 0.0478464 0.0410575 0.0549215 0.198953
2 apply+weigthed.mean 0.633513 0.719832 0.9739697 0.8252475 0.9051010 12.233552
expr min lq mean median uq max
1 colWeightedMeans 1.00000 1.00000 1.00000 1.0000 1.0000 1.00000
2 apply+weigthed.mean 22.32016 21.49651 20.35615 20.0998 16.4799 61.48966
Table: Benchmarking of rowWeightedMeans() and apply+weigthed.mean() on 100x100 data (transposed). The top panel shows times in milliseconds and the bottom panel shows relative times.
expr min lq mean median uq max
1 rowWeightedMeans 0.102932 0.1044455 0.1089756 0.1059725 0.107849 0.205329
2 apply+weigthed.mean 0.559603 0.5716445 0.6829289 0.5765340 0.591096 8.947814
expr min lq mean median uq max
1 rowWeightedMeans 1.000000 1.000000 1.000000 1.000000 1.000000 1.00000
2 apply+weigthed.mean 5.436628 5.473137 6.266807 5.440411 5.480774 43.57794
Figure: Benchmarking of colWeightedMeans() and apply+weigthed.mean() on 100x100 data as well as rowWeightedMeans() and apply+weigthed.mean() on the same data transposed. Outliers are displayed as crosses. Times are in milliseconds.

Table: Benchmarking of colWeightedMeans() and rowWeightedMeans() on 100x100 data (original and transposed). The top panel shows times in milliseconds and the bottom panel shows relative times.

expr min lq mean median uq max
1 colWeightedMeans 28.383 33.4860 47.84645 41.0575 54.9215 198.953
2 rowWeightedMeans 102.932 104.4455 108.97558 105.9725 107.8490 205.329
expr min lq mean median uq max
1 colWeightedMeans 1.000000 1.00000 1.000000 1.000000 1.000000 1.000000
2 rowWeightedMeans 3.626537 3.11908 2.277611 2.581075 1.963694 1.032048
Figure: Benchmarking of colWeightedMeans() and rowWeightedMeans() on 100x100 data (original and transposed). Outliers are displayed as crosses. Times are in milliseconds.

1000x10 matrix

> X <- data[["1000x10"]]
> w <- runif(nrow(X))
> gc()
          used  (Mb) gc trigger  (Mb) max used  (Mb)
Ncells 3058772 163.4    5723390 305.7  5723390 305.7
Vcells 5200094  39.7   12256490  93.6 12256404  93.6
> colStats <- microbenchmark(colWeightedMeans = colWeightedMeans(X, w = w, na.rm = FALSE), `apply+weigthed.mean` = apply(X, 
+     MARGIN = 2L, FUN = weighted.mean, w = w, na.rm = FALSE), unit = "ms")
> X <- t(X)
> gc()
          used  (Mb) gc trigger  (Mb) max used  (Mb)
Ncells 3058760 163.4    5723390 305.7  5723390 305.7
Vcells 5210127  39.8   12256490  93.6 12256404  93.6
> rowStats <- microbenchmark(rowWeightedMeans = rowWeightedMeans(X, w = w, na.rm = FALSE), `apply+weigthed.mean` = apply(X, 
+     MARGIN = 1L, FUN = weighted.mean, w = w, na.rm = FALSE), unit = "ms")

Table: Benchmarking of colWeightedMeans() and apply+weigthed.mean() on 1000x10 data. The top panel shows times in milliseconds and the bottom panel shows relative times.

expr min lq mean median uq max
1 colWeightedMeans 0.039137 0.0413845 0.044809 0.0431425 0.0444280 0.127886
2 apply+weigthed.mean 0.203658 0.2095140 0.265785 0.2203085 0.2248705 4.553261
expr min lq mean median uq max
1 colWeightedMeans 1.00000 1.00000 1.000000 1.000000 1.000000 1.00000
2 apply+weigthed.mean 5.20372 5.06262 5.931511 5.106531 5.061459 35.60406
Table: Benchmarking of rowWeightedMeans() and apply+weigthed.mean() on 1000x10 data (transposed). The top panel shows times in milliseconds and the bottom panel shows relative times.
expr min lq mean median uq max
1 rowWeightedMeans 0.127435 0.1350745 0.1486614 0.1405780 0.152598 0.270186
2 apply+weigthed.mean 0.186238 0.1958935 0.2993938 0.2019265 0.239312 7.297000
expr min lq mean median uq max
1 rowWeightedMeans 1.000000 1.000000 1.000000 1.000000 1.000000 1.00000
2 apply+weigthed.mean 1.461435 1.450263 2.013932 1.436402 1.568251 27.00732
Figure: Benchmarking of colWeightedMeans() and apply+weigthed.mean() on 1000x10 data as well as rowWeightedMeans() and apply+weigthed.mean() on the same data transposed. Outliers are displayed as crosses. Times are in milliseconds.

Table: Benchmarking of colWeightedMeans() and rowWeightedMeans() on 1000x10 data (original and transposed). The top panel shows times in milliseconds and the bottom panel shows relative times.

expr min lq mean median uq max
1 colWeightedMeans 39.137 41.3845 44.80899 43.1425 44.428 127.886
2 rowWeightedMeans 127.435 135.0745 148.66137 140.5780 152.598 270.186
expr min lq mean median uq max
1 colWeightedMeans 1.000000 1.000000 1.000000 1.000000 1.000000 1.00000
2 rowWeightedMeans 3.256126 3.263891 3.317668 3.258457 3.434726 2.11271
Figure: Benchmarking of colWeightedMeans() and rowWeightedMeans() on 1000x10 data (original and transposed). Outliers are displayed as crosses. Times are in milliseconds.

10x1000 matrix

> X <- data[["10x1000"]]
> w <- runif(nrow(X))
> gc()
          used  (Mb) gc trigger  (Mb) max used  (Mb)
Ncells 3058964 163.4    5723390 305.7  5723390 305.7
Vcells 5199884  39.7   12256490  93.6 12256404  93.6
> colStats <- microbenchmark(colWeightedMeans = colWeightedMeans(X, w = w, na.rm = FALSE), `apply+weigthed.mean` = apply(X, 
+     MARGIN = 2L, FUN = weighted.mean, w = w, na.rm = FALSE), unit = "ms")
> X <- t(X)
> gc()
          used  (Mb) gc trigger  (Mb) max used  (Mb)
Ncells 3058958 163.4    5723390 305.7  5723390 305.7
Vcells 5209927  39.8   12256490  93.6 12256404  93.6
> rowStats <- microbenchmark(rowWeightedMeans = rowWeightedMeans(X, w = w, na.rm = FALSE), `apply+weigthed.mean` = apply(X, 
+     MARGIN = 1L, FUN = weighted.mean, w = w, na.rm = FALSE), unit = "ms")

Table: Benchmarking of colWeightedMeans() and apply+weigthed.mean() on 10x1000 data. The top panel shows times in milliseconds and the bottom panel shows relative times.

expr min lq mean median uq max
1 colWeightedMeans 0.027348 0.029787 0.0382364 0.033169 0.042577 0.077716
2 apply+weigthed.mean 4.284069 4.532933 4.7666648 4.698467 4.802924 8.797192
expr min lq mean median uq max
1 colWeightedMeans 1.0000 1.0000 1.0000 1.0000 1.0000 1.0000
2 apply+weigthed.mean 156.6502 152.1782 124.6631 141.6524 112.8056 113.1967
Table: Benchmarking of rowWeightedMeans() and apply+weigthed.mean() on 10x1000 data (transposed). The top panel shows times in milliseconds and the bottom panel shows relative times.
expr min lq mean median uq max
1 rowWeightedMeans 0.100331 0.105250 0.1215451 0.114495 0.1278195 0.253728
2 apply+weigthed.mean 4.315044 4.649039 5.2149345 5.032358 5.4836465 10.441042
expr min lq mean median uq max
1 rowWeightedMeans 1.00000 1.00000 1.00000 1.00000 1.00000 1.00000
2 apply+weigthed.mean 43.00808 44.17139 42.90534 43.95265 42.90149 41.15053
Figure: Benchmarking of colWeightedMeans() and apply+weigthed.mean() on 10x1000 data as well as rowWeightedMeans() and apply+weigthed.mean() on the same data transposed. Outliers are displayed as crosses. Times are in milliseconds.

Table: Benchmarking of colWeightedMeans() and rowWeightedMeans() on 10x1000 data (original and transposed). The top panel shows times in milliseconds and the bottom panel shows relative times.

expr min lq mean median uq max
1 colWeightedMeans 27.348 29.787 38.23638 33.169 42.5770 77.716
2 rowWeightedMeans 100.331 105.250 121.54512 114.495 127.8195 253.728
expr min lq mean median uq max
1 colWeightedMeans 1.000000 1.000000 1.000000 1.000000 1.000000 1.00000
2 rowWeightedMeans 3.668678 3.533421 3.178782 3.451868 3.002079 3.26481
Figure: Benchmarking of colWeightedMeans() and rowWeightedMeans() on 10x1000 data (original and transposed). Outliers are displayed as crosses. Times are in milliseconds.

100x1000 matrix

> X <- data[["100x1000"]]
> w <- runif(nrow(X))
> gc()
          used  (Mb) gc trigger  (Mb) max used  (Mb)
Ncells 3059155 163.4    5723390 305.7  5723390 305.7
Vcells 5200482  39.7   12256490  93.6 12256404  93.6
> colStats <- microbenchmark(colWeightedMeans = colWeightedMeans(X, w = w, na.rm = FALSE), `apply+weigthed.mean` = apply(X, 
+     MARGIN = 2L, FUN = weighted.mean, w = w, na.rm = FALSE), unit = "ms")
> X <- t(X)
> gc()
          used  (Mb) gc trigger  (Mb) max used  (Mb)
Ncells 3059143 163.4    5723390 305.7  5723390 305.7
Vcells 5300515  40.5   12256490  93.6 12256483  93.6
> rowStats <- microbenchmark(rowWeightedMeans = rowWeightedMeans(X, w = w, na.rm = FALSE), `apply+weigthed.mean` = apply(X, 
+     MARGIN = 1L, FUN = weighted.mean, w = w, na.rm = FALSE), unit = "ms")

Table: Benchmarking of colWeightedMeans() and apply+weigthed.mean() on 100x1000 data. The top panel shows times in milliseconds and the bottom panel shows relative times.

expr min lq mean median uq max
1 colWeightedMeans 0.169048 0.1936865 0.3126021 0.2303125 0.249248 8.918898
2 apply+weigthed.mean 5.891669 6.0341635 7.2968635 6.2800045 7.139492 16.419671
expr min lq mean median uq max
1 colWeightedMeans 1.00000 1.00000 1.00000 1.00000 1.00000 1.000000
2 apply+weigthed.mean 34.85205 31.15428 23.34234 27.26732 28.64413 1.840998
Table: Benchmarking of rowWeightedMeans() and apply+weigthed.mean() on 100x1000 data (transposed). The top panel shows times in milliseconds and the bottom panel shows relative times.
expr min lq mean median uq max
1 rowWeightedMeans 0.899577 0.922818 1.146632 1.064101 1.160436 9.403608
2 apply+weigthed.mean 5.627461 5.917683 7.582343 6.826339 7.660190 17.345934
expr min lq mean median uq max
1 rowWeightedMeans 1.000000 1.000000 1.000000 1.000000 1.000000 1.000000
2 apply+weigthed.mean 6.255675 6.412622 6.612706 6.415124 6.601131 1.844604
Figure: Benchmarking of colWeightedMeans() and apply+weigthed.mean() on 100x1000 data as well as rowWeightedMeans() and apply+weigthed.mean() on the same data transposed. Outliers are displayed as crosses. Times are in milliseconds.

Table: Benchmarking of colWeightedMeans() and rowWeightedMeans() on 100x1000 data (original and transposed). The top panel shows times in milliseconds and the bottom panel shows relative times.

expr min lq mean median uq max
1 colWeightedMeans 169.048 193.6865 312.6021 230.3125 249.248 8918.898
2 rowWeightedMeans 899.577 922.8180 1146.6325 1064.1010 1160.436 9403.608
expr min lq mean median uq max
1 colWeightedMeans 1.000000 1.000000 1.000000 1.000000 1.000000 1.000000
2 rowWeightedMeans 5.321429 4.764493 3.668025 4.620249 4.655748 1.054346
Figure: Benchmarking of colWeightedMeans() and rowWeightedMeans() on 100x1000 data (original and transposed). Outliers are displayed as crosses. Times are in milliseconds.

1000x100 matrix

> X <- data[["1000x100"]]
> w <- runif(nrow(X))
> gc()
          used  (Mb) gc trigger  (Mb) max used  (Mb)
Ncells 3059334 163.4    5723390 305.7  5723390 305.7
Vcells 5202008  39.7   12256490  93.6 12256483  93.6
> colStats <- microbenchmark(colWeightedMeans = colWeightedMeans(X, w = w, na.rm = FALSE), `apply+weigthed.mean` = apply(X, 
+     MARGIN = 2L, FUN = weighted.mean, w = w, na.rm = FALSE), unit = "ms")
> X <- t(X)
> gc()
          used  (Mb) gc trigger  (Mb) max used  (Mb)
Ncells 3059328 163.4    5723390 305.7  5723390 305.7
Vcells 5302051  40.5   12256490  93.6 12256483  93.6
> rowStats <- microbenchmark(rowWeightedMeans = rowWeightedMeans(X, w = w, na.rm = FALSE), `apply+weigthed.mean` = apply(X, 
+     MARGIN = 1L, FUN = weighted.mean, w = w, na.rm = FALSE), unit = "ms")

Table: Benchmarking of colWeightedMeans() and apply+weigthed.mean() on 1000x100 data. The top panel shows times in milliseconds and the bottom panel shows relative times.

expr min lq mean median uq max
1 colWeightedMeans 0.199028 0.247153 0.3341222 0.262264 0.2901365 5.84241
2 apply+weigthed.mean 1.560997 1.906229 4.7343526 2.022565 2.2959090 231.17986
expr min lq mean median uq max
1 colWeightedMeans 1.000000 1.000000 1.00000 1.000000 1.000000 1.00000
2 apply+weigthed.mean 7.843102 7.712747 14.16952 7.711943 7.913203 39.56926
Table: Benchmarking of rowWeightedMeans() and apply+weigthed.mean() on 1000x100 data (transposed). The top panel shows times in milliseconds and the bottom panel shows relative times.
expr min lq mean median uq max
1 rowWeightedMeans 0.923806 1.001466 1.101040 1.057818 1.106354 5.408107
2 apply+weigthed.mean 1.534959 1.665787 2.140142 1.748613 1.873480 6.275241
expr min lq mean median uq max
1 rowWeightedMeans 1.00000 1.000000 1.000000 1.000000 1.000000 1.00000
2 apply+weigthed.mean 1.66156 1.663348 1.943746 1.653037 1.693381 1.16034
Figure: Benchmarking of colWeightedMeans() and apply+weigthed.mean() on 1000x100 data as well as rowWeightedMeans() and apply+weigthed.mean() on the same data transposed. Outliers are displayed as crosses. Times are in milliseconds.

Table: Benchmarking of colWeightedMeans() and rowWeightedMeans() on 1000x100 data (original and transposed). The top panel shows times in milliseconds and the bottom panel shows relative times.

expr min lq mean median uq max
1 colWeightedMeans 199.028 247.153 334.1222 262.264 290.1365 5842.410
2 rowWeightedMeans 923.806 1001.466 1101.0402 1057.819 1106.3545 5408.107
expr min lq mean median uq max
1 colWeightedMeans 1.000000 1.000000 1.000000 1.000000 1.000000 1.0000000
2 rowWeightedMeans 4.641588 4.052008 3.295321 4.033411 3.813221 0.9256637
Figure: Benchmarking of colWeightedMeans() and rowWeightedMeans() on 1000x100 data (original and transposed). Outliers are displayed as crosses. Times are in milliseconds.

Appendix

Session information

R version 3.6.1 Patched (2019-08-27 r77078)
Platform: x86_64-pc-linux-gnu (64-bit)
Running under: Ubuntu 18.04.3 LTS

Matrix products: default
BLAS:   /home/hb/software/R-devel/R-3-6-branch/lib/R/lib/libRblas.so
LAPACK: /home/hb/software/R-devel/R-3-6-branch/lib/R/lib/libRlapack.so

locale:
 [1] LC_CTYPE=en_US.UTF-8       LC_NUMERIC=C              
 [3] LC_TIME=en_US.UTF-8        LC_COLLATE=en_US.UTF-8    
 [5] LC_MONETARY=en_US.UTF-8    LC_MESSAGES=en_US.UTF-8   
 [7] LC_PAPER=en_US.UTF-8       LC_NAME=C                 
 [9] LC_ADDRESS=C               LC_TELEPHONE=C            
[11] LC_MEASUREMENT=en_US.UTF-8 LC_IDENTIFICATION=C       

attached base packages:
[1] stats     graphics  grDevices utils     datasets  methods   base     

other attached packages:
[1] microbenchmark_1.4-6    matrixStats_0.55.0-9000 ggplot2_3.2.1          
[4] knitr_1.24              R.devices_2.16.0        R.utils_2.9.0          
[7] R.oo_1.22.0             R.methodsS3_1.7.1       history_0.0.0-9002     

loaded via a namespace (and not attached):
 [1] Biobase_2.45.0       bit64_0.9-7          splines_3.6.1       
 [4] network_1.15         assertthat_0.2.1     highr_0.8           
 [7] stats4_3.6.1         blob_1.2.0           robustbase_0.93-5   
[10] pillar_1.4.2         RSQLite_2.1.2        backports_1.1.4     
[13] lattice_0.20-38      glue_1.3.1           digest_0.6.20       
[16] colorspace_1.4-1     sandwich_2.5-1       Matrix_1.2-17       
[19] XML_3.98-1.20        lpSolve_5.6.13.3     pkgconfig_2.0.2     
[22] genefilter_1.66.0    purrr_0.3.2          ergm_3.10.4         
[25] xtable_1.8-4         mvtnorm_1.0-11       scales_1.0.0        
[28] tibble_2.1.3         annotate_1.62.0      IRanges_2.18.2      
[31] TH.data_1.0-10       withr_2.1.2          BiocGenerics_0.30.0 
[34] lazyeval_0.2.2       mime_0.7             survival_2.44-1.1   
[37] magrittr_1.5         crayon_1.3.4         statnet.common_4.3.0
[40] memoise_1.1.0        laeken_0.5.0         R.cache_0.13.0      
[43] MASS_7.3-51.4        R.rsp_0.43.1         tools_3.6.1         
[46] multcomp_1.4-10      S4Vectors_0.22.1     trust_0.1-7         
[49] munsell_0.5.0        AnnotationDbi_1.46.1 compiler_3.6.1      
[52] rlang_0.4.0          grid_3.6.1           RCurl_1.95-4.12     
[55] cwhmisc_6.6          rappdirs_0.3.1       labeling_0.3        
[58] bitops_1.0-6         base64enc_0.1-3      boot_1.3-23         
[61] gtable_0.3.0         codetools_0.2-16     DBI_1.0.0           
[64] markdown_1.1         R6_2.4.0             zoo_1.8-6           
[67] dplyr_0.8.3          bit_1.1-14           zeallot_0.1.0       
[70] parallel_3.6.1       Rcpp_1.0.2           vctrs_0.2.0         
[73] DEoptimR_1.0-8       tidyselect_0.2.5     xfun_0.9            
[76] coda_0.19-3

Total processing time was 14.99 secs.

Reproducibility

To reproduce this report, do:

html <- matrixStats:::benchmark('colWeightedMeans')

Copyright Henrik Bengtsson. Last updated on 2019-09-10 18:36:13 (-0700 UTC). Powered by RSP.

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