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

matrixStats: Benchmark report

colWeightedMedians() and rowWeightedMedians() benchmarks

This report benchmark the performance of colWeightedMedians() and rowWeightedMedians() against alternative methods.

Alternative methods

  • apply() + weightedMedian()

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 3230545 172.6    5709258 305.0  5709258 305.0
Vcells 6863260  52.4   22345847 170.5 56666022 432.4
> colStats <- microbenchmark(colWeightedMedians = colWeightedMedians(X, w = w, na.rm = FALSE), `apply+weigthedMedian` = apply(X, 
+     MARGIN = 2L, FUN = weightedMedian, w = w, na.rm = FALSE), unit = "ms")
> X <- t(X)
> gc()
          used  (Mb) gc trigger  (Mb) max used  (Mb)
Ncells 3229068 172.5    5709258 305.0  5709258 305.0
Vcells 6859048  52.4   22345847 170.5 56666022 432.4
> rowStats <- microbenchmark(rowWeightedMedians = rowWeightedMedians(X, w = w, na.rm = FALSE), `apply+weigthedMedian` = apply(X, 
+     MARGIN = 1L, FUN = weightedMedian, w = w, na.rm = FALSE), unit = "ms")

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

expr min lq mean median uq max
2 apply+weigthedMedian 0.034414 0.0353165 0.036865 0.0356925 0.0361500 0.142468
1 colWeightedMedians 0.042417 0.0432755 0.044040 0.0437040 0.0441705 0.062775
expr min lq mean median uq max
2 apply+weigthedMedian 1.000000 1.000000 1.000000 1.000000 1.000000 1.0000000
1 colWeightedMedians 1.232551 1.225362 1.194627 1.224459 1.221867 0.4406253

Table: Benchmarking of rowWeightedMedians() and apply+weigthedMedian() 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
2 apply+weigthedMedian 0.033587 0.034515 0.0360039 0.0348925 0.0353215 0.136032
1 rowWeightedMedians 0.041822 0.042836 0.0434889 0.0431860 0.0434855 0.064376
expr min lq mean median uq max
2 apply+weigthedMedian 1.000000 1.000000 1.000000 1.000000 1.000000 1.0000000
1 rowWeightedMedians 1.245184 1.241084 1.207895 1.237687 1.231134 0.4732416

Figure: Benchmarking of colWeightedMedians() and apply+weigthedMedian() on 10x10 data as well as rowWeightedMedians() and apply+weigthedMedian() on the same data transposed. Outliers are displayed as crosses. Times are in milliseconds.

Table: Benchmarking of colWeightedMedians() and rowWeightedMedians() 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
2 rowWeightedMedians 41.822 42.8360 43.48893 43.186 43.4855 64.376
1 colWeightedMedians 42.417 43.2755 44.03998 43.704 44.1705 62.775
expr min lq mean median uq max
2 rowWeightedMedians 1.000000 1.00000 1.000000 1.000000 1.000000 1.0000000
1 colWeightedMedians 1.014227 1.01026 1.012671 1.011995 1.015752 0.9751305

Figure: Benchmarking of colWeightedMedians() and rowWeightedMedians() 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 3227620 172.4    5709258 305.0  5709258 305.0
Vcells 6473879  49.4   22345847 170.5 56666022 432.4
> colStats <- microbenchmark(colWeightedMedians = colWeightedMedians(X, w = w, na.rm = FALSE), `apply+weigthedMedian` = apply(X, 
+     MARGIN = 2L, FUN = weightedMedian, w = w, na.rm = FALSE), unit = "ms")
> X <- t(X)
> gc()
          used  (Mb) gc trigger  (Mb) max used  (Mb)
Ncells 3227611 172.4    5709258 305.0  5709258 305.0
Vcells 6483917  49.5   22345847 170.5 56666022 432.4
> rowStats <- microbenchmark(rowWeightedMedians = rowWeightedMedians(X, w = w, na.rm = FALSE), `apply+weigthedMedian` = apply(X, 
+     MARGIN = 1L, FUN = weightedMedian, w = w, na.rm = FALSE), unit = "ms")

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

expr min lq mean median uq max
2 apply+weigthedMedian 0.653854 0.6767645 0.6846320 0.6831130 0.6882005 0.805764
1 colWeightedMedians 0.704007 0.7319885 0.7369661 0.7388305 0.7430675 0.766118
expr min lq mean median uq max
2 apply+weigthedMedian 1.000000 1.0000 1.000000 1.000000 1.000000 1.000000
1 colWeightedMedians 1.076704 1.0816 1.076441 1.081564 1.079725 0.950797

Table: Benchmarking of rowWeightedMedians() and apply+weigthedMedian() 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
2 apply+weigthedMedian 0.613283 0.6326505 0.6433630 0.6416485 0.6508290 0.834961
1 rowWeightedMedians 0.662970 0.6789810 0.6977486 0.6984910 0.7112255 0.770928
expr min lq mean median uq max
2 apply+weigthedMedian 1.000000 1.000000 1.000000 1.000000 1.000000 1.0000000
1 rowWeightedMedians 1.081018 1.073232 1.084533 1.088588 1.092799 0.9233102

Figure: Benchmarking of colWeightedMedians() and apply+weigthedMedian() on 100x100 data as well as rowWeightedMedians() and apply+weigthedMedian() on the same data transposed. Outliers are displayed as crosses. Times are in milliseconds.

Table: Benchmarking of colWeightedMedians() and rowWeightedMedians() 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
2 rowWeightedMedians 662.970 678.9810 697.7486 698.4910 711.2255 770.928
1 colWeightedMedians 704.007 731.9885 736.9661 738.8305 743.0675 766.118
expr min lq mean median uq max
2 rowWeightedMedians 1.000000 1.000000 1.000000 1.000000 1.000000 1.0000000
1 colWeightedMedians 1.061899 1.078069 1.056206 1.057752 1.044771 0.9937608

Figure: Benchmarking of colWeightedMedians() and rowWeightedMedians() 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 3226997 172.4    5709258 305.0  5709258 305.0
Vcells 6451877  49.3   22345847 170.5 56666022 432.4
> colStats <- microbenchmark(colWeightedMedians = colWeightedMedians(X, w = w, na.rm = FALSE), `apply+weigthedMedian` = apply(X, 
+     MARGIN = 2L, FUN = weightedMedian, w = w, na.rm = FALSE), unit = "ms")
> X <- t(X)
> gc()
          used  (Mb) gc trigger  (Mb) max used  (Mb)
Ncells 3226988 172.4    5709258 305.0  5709258 305.0
Vcells 6461915  49.4   22345847 170.5 56666022 432.4
> rowStats <- microbenchmark(rowWeightedMedians = rowWeightedMedians(X, w = w, na.rm = FALSE), `apply+weigthedMedian` = apply(X, 
+     MARGIN = 1L, FUN = weightedMedian, w = w, na.rm = FALSE), unit = "ms")

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

expr min lq mean median uq max
2 apply+weigthedMedian 0.576738 0.5898190 0.5987584 0.596093 0.6094355 0.654816
1 colWeightedMedians 0.595121 0.6048425 0.6180627 0.616453 0.6304220 0.686339
expr min lq mean median uq max
2 apply+weigthedMedian 1.000000 1.000000 1.00000 1.000000 1.000000 1.00000
1 colWeightedMedians 1.031874 1.025471 1.03224 1.034156 1.034436 1.04814

Table: Benchmarking of rowWeightedMedians() and apply+weigthedMedian() 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
2 apply+weigthedMedian 0.535405 0.542933 0.5565937 0.5527315 0.5657045 0.665231
1 rowWeightedMedians 0.554885 0.562550 0.5782437 0.5743815 0.5869250 0.695733
expr min lq mean median uq max
2 apply+weigthedMedian 1.000000 1.000000 1.000000 1.000000 1.000000 1.000000
1 rowWeightedMedians 1.036384 1.036131 1.038897 1.039169 1.037512 1.045852

Figure: Benchmarking of colWeightedMedians() and apply+weigthedMedian() on 1000x10 data as well as rowWeightedMedians() and apply+weigthedMedian() on the same data transposed. Outliers are displayed as crosses. Times are in milliseconds.

Table: Benchmarking of colWeightedMedians() and rowWeightedMedians() 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
2 rowWeightedMedians 554.885 562.5500 578.2437 574.3815 586.925 695.733
1 colWeightedMedians 595.121 604.8425 618.0626 616.4530 630.422 686.339
expr min lq mean median uq max
2 rowWeightedMedians 1.000000 1.00000 1.000000 1.000000 1.00000 1.0000000
1 colWeightedMedians 1.072512 1.07518 1.068862 1.073247 1.07411 0.9864977

Figure: Benchmarking of colWeightedMedians() and rowWeightedMedians() 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 3227195 172.4    5709258 305.0  5709258 305.0
Vcells 6451702  49.3   22345847 170.5 56666022 432.4
> colStats <- microbenchmark(colWeightedMedians = colWeightedMedians(X, w = w, na.rm = FALSE), `apply+weigthedMedian` = apply(X, 
+     MARGIN = 2L, FUN = weightedMedian, w = w, na.rm = FALSE), unit = "ms")
> X <- t(X)
> gc()
          used  (Mb) gc trigger  (Mb) max used  (Mb)
Ncells 3227186 172.4    5709258 305.0  5709258 305.0
Vcells 6461740  49.3   22345847 170.5 56666022 432.4
> rowStats <- microbenchmark(rowWeightedMedians = rowWeightedMedians(X, w = w, na.rm = FALSE), `apply+weigthedMedian` = apply(X, 
+     MARGIN = 1L, FUN = weightedMedian, w = w, na.rm = FALSE), unit = "ms")

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

expr min lq mean median uq max
2 apply+weigthedMedian 2.172776 2.408167 2.541653 2.471104 2.544382 8.466055
1 colWeightedMedians 2.643734 2.912365 3.051955 2.985746 3.057475 9.222259
expr min lq mean median uq max
2 apply+weigthedMedian 1.000000 1.00000 1.000000 1.000000 1.000000 1.000000
1 colWeightedMedians 1.216754 1.20937 1.200775 1.208264 1.201658 1.089322

Table: Benchmarking of rowWeightedMedians() and apply+weigthedMedian() 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
2 apply+weigthedMedian 2.159425 2.401065 2.476082 2.474407 2.532453 3.375131
1 rowWeightedMedians 2.618450 2.898156 3.155197 2.966784 3.045298 9.044380
expr min lq mean median uq max
2 apply+weigthedMedian 1.000000 1.000000 1.00000 1.000000 1.000000 1.000000
1 rowWeightedMedians 1.212568 1.207029 1.27427 1.198988 1.202509 2.679712

Figure: Benchmarking of colWeightedMedians() and apply+weigthedMedian() on 10x1000 data as well as rowWeightedMedians() and apply+weigthedMedian() on the same data transposed. Outliers are displayed as crosses. Times are in milliseconds.

Table: Benchmarking of colWeightedMedians() and rowWeightedMedians() 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
2 rowWeightedMedians 2.618450 2.898156 3.155197 2.966784 3.045298 9.044380
1 colWeightedMedians 2.643734 2.912365 3.051955 2.985746 3.057475 9.222259
expr min lq mean median uq max
2 rowWeightedMedians 1.000000 1.000000 1.0000000 1.000000 1.000000 1.000000
1 colWeightedMedians 1.009656 1.004903 0.9672786 1.006392 1.003999 1.019667

Figure: Benchmarking of colWeightedMedians() and rowWeightedMedians() 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 3227382 172.4    5709258 305.0  5709258 305.0
Vcells 6452296  49.3   22345847 170.5 56666022 432.4
> colStats <- microbenchmark(colWeightedMedians = colWeightedMedians(X, w = w, na.rm = FALSE), `apply+weigthedMedian` = apply(X, 
+     MARGIN = 2L, FUN = weightedMedian, w = w, na.rm = FALSE), unit = "ms")
> X <- t(X)
> gc()
          used  (Mb) gc trigger  (Mb) max used  (Mb)
Ncells 3227376 172.4    5709258 305.0  5709258 305.0
Vcells 6552339  50.0   22345847 170.5 56666022 432.4
> rowStats <- microbenchmark(rowWeightedMedians = rowWeightedMedians(X, w = w, na.rm = FALSE), `apply+weigthedMedian` = apply(X, 
+     MARGIN = 1L, FUN = weightedMedian, w = w, na.rm = FALSE), unit = "ms")

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

expr min lq mean median uq max
2 apply+weigthedMedian 6.335133 6.735972 6.968173 6.795899 6.859356 22.81473
1 colWeightedMedians 6.894953 7.324806 7.795424 7.393520 7.537198 23.18486
expr min lq mean median uq max
2 apply+weigthedMedian 1.000000 1.000000 1.000000 1.000000 1.00000 1.000000
1 colWeightedMedians 1.088367 1.087416 1.118719 1.087938 1.09882 1.016223

Table: Benchmarking of rowWeightedMedians() and apply+weigthedMedian() 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
2 apply+weigthedMedian 5.956803 6.321299 6.941218 6.359146 6.425687 23.89361
1 rowWeightedMedians 6.487969 6.814432 6.997135 6.872865 6.939459 10.44708
expr min lq mean median uq max
2 apply+weigthedMedian 1.00000 1.000000 1.000000 1.000000 1.000000 1.0000000
1 rowWeightedMedians 1.08917 1.078011 1.008056 1.080784 1.079956 0.4372332

Figure: Benchmarking of colWeightedMedians() and apply+weigthedMedian() on 100x1000 data as well as rowWeightedMedians() and apply+weigthedMedian() on the same data transposed. Outliers are displayed as crosses. Times are in milliseconds.

Table: Benchmarking of colWeightedMedians() and rowWeightedMedians() 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
2 rowWeightedMedians 6.487969 6.814432 6.997135 6.872865 6.939459 10.44708
1 colWeightedMedians 6.894953 7.324806 7.795424 7.393520 7.537198 23.18486
expr min lq mean median uq max
2 rowWeightedMedians 1.000000 1.000000 1.000000 1.000000 1.000000 1.000000
1 colWeightedMedians 1.062729 1.074896 1.114088 1.075755 1.086136 2.219267

Figure: Benchmarking of colWeightedMedians() and rowWeightedMedians() 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 3227572 172.4    5709258 305.0  5709258 305.0
Vcells 6453850  49.3   22345847 170.5 56666022 432.4
> colStats <- microbenchmark(colWeightedMedians = colWeightedMedians(X, w = w, na.rm = FALSE), `apply+weigthedMedian` = apply(X, 
+     MARGIN = 2L, FUN = weightedMedian, w = w, na.rm = FALSE), unit = "ms")
> X <- t(X)
> gc()
          used  (Mb) gc trigger  (Mb) max used  (Mb)
Ncells 3227563 172.4    5709258 305.0  5709258 305.0
Vcells 6553888  50.1   22345847 170.5 56666022 432.4
> rowStats <- microbenchmark(rowWeightedMedians = rowWeightedMedians(X, w = w, na.rm = FALSE), `apply+weigthedMedian` = apply(X, 
+     MARGIN = 1L, FUN = weightedMedian, w = w, na.rm = FALSE), unit = "ms")

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

expr min lq mean median uq max
2 apply+weigthedMedian 5.174672 5.421680 8.084785 5.450757 5.519330 255.12654
1 colWeightedMedians 5.253931 5.485418 5.658828 5.512492 5.590269 14.12993
expr min lq mean median uq max
2 apply+weigthedMedian 1.000000 1.000000 1.0000000 1.000000 1.000000 1.000000
1 colWeightedMedians 1.015317 1.011756 0.6999355 1.011326 1.012853 0.055384

Table: Benchmarking of rowWeightedMedians() and apply+weigthedMedian() 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
2 apply+weigthedMedian 5.271997 5.455296 5.707209 5.493001 5.540624 14.16726
1 rowWeightedMedians 5.296073 5.529117 5.656510 5.551160 5.603319 13.57193
expr min lq mean median uq max
2 apply+weigthedMedian 1.000000 1.000000 1.0000000 1.000000 1.000000 1.0000000
1 rowWeightedMedians 1.004567 1.013532 0.9911167 1.010588 1.011316 0.9579784

Figure: Benchmarking of colWeightedMedians() and apply+weigthedMedian() on 1000x100 data as well as rowWeightedMedians() and apply+weigthedMedian() on the same data transposed. Outliers are displayed as crosses. Times are in milliseconds.

Table: Benchmarking of colWeightedMedians() and rowWeightedMedians() 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 colWeightedMedians 5.253931 5.485418 5.658828 5.512492 5.590269 14.12993
2 rowWeightedMedians 5.296073 5.529117 5.656510 5.551160 5.603319 13.57193
expr min lq mean median uq max
1 colWeightedMedians 1.000000 1.000000 1.0000000 1.000000 1.000000 1.0000000
2 rowWeightedMedians 1.008021 1.007966 0.9995903 1.007015 1.002334 0.9605098

Figure: Benchmarking of colWeightedMedians() and rowWeightedMedians() 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 18.39 secs.

Reproducibility

To reproduce this report, do:

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

Copyright Henrik Bengtsson. Last updated on 2019-09-10 20:56:15 (-0700 UTC). Powered by RSP.

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