colRowWeightedMeans - HenrikBengtsson/matrixStats GitHub Wiki

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 3224474 172.3    5709258 305.0  5709258 305.0
Vcells 6809017  52.0   22345847 170.5 56666022 432.4
> 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 3222908 172.2    5709258 305.0  5709258 305.0
Vcells 6804464  52.0   22345847 170.5 56666022 432.4
> 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 0.008693 0.009417 0.0106235 0.0101925 0.0113220 0.035141
2 apply+weigthed.mean 0.056830 0.058049 0.0606144 0.0587155 0.0596145 0.225416
expr min lq mean median uq max
1 colWeightedMeans 1.000000 1.000000 1.000000 1.000000 1.000000 1.000000
2 apply+weigthed.mean 6.537444 6.164277 5.705701 5.760657 5.265368 6.414615

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 0.010868 0.0119150 0.0133688 0.0134600 0.0139455 0.044320
2 apply+weigthed.mean 0.056178 0.0575745 0.0591443 0.0583855 0.0588660 0.131455
expr min lq mean median uq max
1 rowWeightedMeans 1.00000 1.000000 1.000000 1.000000 1.000000 1.000000
2 apply+weigthed.mean 5.16912 4.832102 4.424048 4.337704 4.221147 2.966042

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 8.693 9.417 10.62348 10.1925 11.3220 35.141
2 rowWeightedMeans 10.868 11.915 13.36883 13.4600 13.9455 44.320
expr min lq mean median uq max
1 colWeightedMeans 1.000000 1.000000 1.000000 1.000000 1.000000 1.000000
2 rowWeightedMeans 1.250201 1.265265 1.258423 1.320579 1.231717 1.261205

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 3221457 172.1    5709258 305.0  5709258 305.0
Vcells 6419290  49.0   22345847 170.5 56666022 432.4
> 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 3221451 172.1    5709258 305.0  5709258 305.0
Vcells 6429333  49.1   22345847 170.5 56666022 432.4
> 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 0.027920 0.0299280 0.0316412 0.0308905 0.0322100 0.059950
2 apply+weigthed.mean 0.607759 0.6153645 0.6292381 0.6225935 0.6315155 0.898593
expr min lq mean median uq max
1 colWeightedMeans 1.00000 1.0000 1.00000 1.00000 1.00000 1.00000
2 apply+weigthed.mean 21.76787 20.5615 19.88664 20.15485 19.60619 14.98904

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 0.103243 0.104445 0.1064423 0.1056270 0.1071515 0.139339
2 apply+weigthed.mean 0.564707 0.572189 0.5869633 0.5776505 0.5892865 0.800169
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 5.469688 5.478376 5.514382 5.468777 5.499564 5.742606

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 27.920 29.928 31.64124 30.8905 32.2100 59.950
2 rowWeightedMeans 103.243 104.445 106.44226 105.6270 107.1515 139.339
expr min lq mean median uq max
1 colWeightedMeans 1.000000 1.000000 1.000000 1.000000 1.000000 1.000000
2 rowWeightedMeans 3.697815 3.489876 3.364036 3.419401 3.326653 2.324254

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 3222179 172.1    5709258 305.0  5709258 305.0
Vcells 6423694  49.1   22345847 170.5 56666022 432.4
> 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 3222170 172.1    5709258 305.0  5709258 305.0
Vcells 6433732  49.1   22345847 170.5 56666022 432.4
> 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 0.038679 0.0414205 0.0440360 0.043397 0.044463 0.127149
2 apply+weigthed.mean 0.206058 0.2166725 0.2233923 0.223155 0.228157 0.290184
expr min lq mean median uq max
1 colWeightedMeans 1.000000 1.000000 1.000000 1.000000 1.00000 1.000000
2 apply+weigthed.mean 5.327387 5.231045 5.072946 5.142176 5.13139 2.282236

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 0.114099 0.1158545 0.1182368 0.117336 0.1186590 0.180816
2 apply+weigthed.mean 0.161699 0.1643335 0.1751542 0.169834 0.1829515 0.233958
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 1.417182 1.418447 1.481385 1.447416 1.541826 1.293901

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 38.679 41.4205 44.03601 43.397 44.463 127.149
2 rowWeightedMeans 114.099 115.8545 118.23683 117.336 118.659 180.816
expr min lq mean median uq max
1 colWeightedMeans 1.000000 1.000000 1.000000 1.000000 1.000000 1.00000
2 rowWeightedMeans 2.949895 2.797033 2.685003 2.703781 2.668713 1.42208

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 3222374 172.1    5709258 305.0  5709258 305.0
Vcells 6423490  49.1   22345847 170.5 56666022 432.4
> 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 3222368 172.1    5709258 305.0  5709258 305.0
Vcells 6433533  49.1   22345847 170.5 56666022 432.4
> 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 0.028433 0.030165 0.0359748 0.033409 0.038258 0.10123
2 apply+weigthed.mean 4.349560 4.494647 4.7989474 4.656507 4.792277 11.09634
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 152.9758 149.0021 133.3976 139.3788 125.2621 109.6151

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 0.099616 0.1016885 0.1137857 0.1069615 0.119232 0.173575
2 apply+weigthed.mean 4.433739 4.5614075 4.9078075 4.6794500 4.797594 10.898394
expr min lq mean median uq max
1 rowWeightedMeans 1.0000 1.00000 1.00000 1.00000 1.00000 1.00000
2 apply+weigthed.mean 44.5083 44.85667 43.13202 43.74892 40.23747 62.78781

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 28.433 30.1650 35.97476 33.4090 38.258 101.230
2 rowWeightedMeans 99.616 101.6885 113.78570 106.9615 119.232 173.575
expr min lq mean median uq max
1 colWeightedMeans 1.000000 1.000000 1.000000 1.000000 1.000000 1.00000
2 rowWeightedMeans 3.503535 3.371076 3.162932 3.201577 3.116525 1.71466

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 3222567 172.2    5709258 305.0  5709258 305.0
Vcells 6424088  49.1   22345847 170.5 56666022 432.4
> 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 3222558 172.2    5709258 305.0  5709258 305.0
Vcells 6524126  49.8   22345847 170.5 56666022 432.4
> 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 0.168430 0.214824 0.5719912 0.2336885 0.2628565 16.14153
2 apply+weigthed.mean 5.864435 5.987965 6.7585538 6.0624905 6.4554550 27.67489
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 34.81823 27.87382 11.81584 25.94261 24.55886 1.714515

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 0.899402 0.908582 0.9750369 0.9174375 0.983048 1.508171
2 apply+weigthed.mean 5.551639 5.648465 6.6217834 5.6926085 6.169376 23.063148
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 6.172589 6.216792 6.791315 6.204901 6.275763 15.29213

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 168.430 214.824 571.9911 233.6885 262.8565 16141.528
2 rowWeightedMeans 899.402 908.582 975.0369 917.4375 983.0480 1508.171
expr min lq mean median uq max
1 colWeightedMeans 1.000000 1.000000 1.000000 1.000000 1.000000 1.0000000
2 rowWeightedMeans 5.339916 4.229425 1.704636 3.925899 3.739866 0.0934342

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 3222754 172.2    5709258 305.0  5709258 305.0
Vcells 6425627  49.1   22345847 170.5 56666022 432.4
> 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 3222745 172.2    5709258 305.0  5709258 305.0
Vcells 6525665  49.8   22345847 170.5 56666022 432.4
> 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 0.193951 0.209496 0.3010527 0.227436 0.2476485 7.450469
2 apply+weigthed.mean 1.477644 1.540457 1.9163656 1.674757 1.8800335 9.139492
expr min lq mean median uq max
1 colWeightedMeans 1.000000 1.000000 1.000000 1.000000 1.00000 1.0000
2 apply+weigthed.mean 7.618646 7.353157 6.365549 7.363643 7.59154 1.2267

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 0.922334 0.930577 1.003068 0.956406 1.044642 1.227156
2 apply+weigthed.mean 1.504050 1.524069 1.948271 1.593894 1.827955 8.985222
expr min lq mean median uq max
1 rowWeightedMeans 1.0000 1.000000 1.000000 1.000000 1.000000 1.000000
2 apply+weigthed.mean 1.6307 1.637768 1.942313 1.666546 1.749839 7.321988

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 193.951 209.496 301.0527 227.436 247.6485 7450.469
2 rowWeightedMeans 922.334 930.577 1003.0679 956.406 1044.6420 1227.156
expr min lq mean median uq max
1 colWeightedMeans 1.0000 1.00000 1.000000 1.000000 1.000000 1.0000000
2 rowWeightedMeans 4.7555 4.44198 3.331869 4.205165 4.218245 0.1647086

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 14.37 secs.

Reproducibility

To reproduce this report, do:

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

Copyright Henrik Bengtsson. Last updated on 2019-09-10 20:55:36 (-0700 UTC). Powered by RSP.

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