matrixStats.benchmarks
weightedMedian() benchmarks
This report benchmark the performance of weightedMedian() against alternative methods.
Alternative methods
- apply() + limma::weighted.median()
- apply() + cwhmisc::w.median()
- apply() + laeken::weightedMedian()
Data
> rvector <- function(n, mode = c("logical", "double", "integer"), range = c(-100, +100), na_prob = 0) {
+ mode <- match.arg(mode)
+ if (mode == "logical") {
+ x <- sample(c(FALSE, TRUE), size = n, replace = TRUE)
+ } 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
+ x
+ }
> rvectors <- function(scale = 10, seed = 1, ...) {
+ set.seed(seed)
+ data <- list()
+ data[[1]] <- rvector(n = scale * 100, ...)
+ data[[2]] <- rvector(n = scale * 1000, ...)
+ data[[3]] <- rvector(n = scale * 10000, ...)
+ data[[4]] <- rvector(n = scale * 1e+05, ...)
+ data[[5]] <- rvector(n = scale * 1e+06, ...)
+ names(data) <- sprintf("n = %d", sapply(data, FUN = length))
+ data
+ }
> data <- rvectors(mode = "double")
> data <- data[1:3]
Results
n = 1000 vector
> x <- data[["n = 1000"]]
> w <- runif(length(x))
> gc()
used (Mb) gc trigger (Mb) max used (Mb)
Ncells 5399067 288.4 8529671 455.6 8529671 455.6
Vcells 10888970 83.1 39910282 304.5 101881463 777.3
> stats <- microbenchmark(weightedMedian = weightedMedian(x, w = w, ties = "mean", na.rm = FALSE),
+ `limma::weighted.median` = limma_weighted.median(x, w = w, na.rm = FALSE), `cwhmisc::w.median` = cwhmisc_w.median(x,
+ w = w), `laeken::weightedMedian` = laeken_weightedMedian(x, w = w), unit = "ms")
Table: Benchmarking of weightedMedian(), limma::weighted.median(), cwhmisc::w.median() and laeken::weightedMedian() on n = 1000 data. The top panel shows times in milliseconds and the bottom panel shows relative times.
| expr | min | lq | mean | median | uq | max | |
|---|---|---|---|---|---|---|---|
| 1 | weightedMedian | 0.052821 | 0.0626815 | 0.0679817 | 0.0656180 | 0.0707465 | 0.104965 |
| 2 | limma::weighted.median | 0.079736 | 0.0883770 | 0.0975250 | 0.0939640 | 0.1042830 | 0.200039 |
| 3 | cwhmisc::w.median | 0.110723 | 0.1210920 | 0.1290402 | 0.1257495 | 0.1348620 | 0.173337 |
| 4 | laeken::weightedMedian | 0.155514 | 0.1722420 | 0.1917443 | 0.1816670 | 0.1953360 | 0.671221 |
| expr | min | lq | mean | median | uq | max | |
|---|---|---|---|---|---|---|---|
| 1 | weightedMedian | 1.000000 | 1.000000 | 1.000000 | 1.000000 | 1.000000 | 1.000000 |
| 2 | limma::weighted.median | 1.509551 | 1.409938 | 1.434577 | 1.431985 | 1.474038 | 1.905769 |
| 3 | cwhmisc::w.median | 2.096193 | 1.931862 | 1.898161 | 1.916387 | 1.906271 | 1.651379 |
| 4 | laeken::weightedMedian | 2.944170 | 2.747892 | 2.820528 | 2.768554 | 2.761070 | 6.394712 |
Figure: Benchmarking of weightedMedian(), limma::weighted.median(), cwhmisc::w.median() and laeken::weightedMedian() on n = 1000 data. Outliers are displayed as crosses. Times are in milliseconds.
n = 10000 vector
> x <- data[["n = 10000"]]
> w <- runif(length(x))
> gc()
used (Mb) gc trigger (Mb) max used (Mb)
Ncells 5397380 288.3 8529671 455.6 8529671 455.6
Vcells 10489485 80.1 39910282 304.5 101881463 777.3
> stats <- microbenchmark(weightedMedian = weightedMedian(x, w = w, ties = "mean", na.rm = FALSE),
+ `limma::weighted.median` = limma_weighted.median(x, w = w, na.rm = FALSE), `cwhmisc::w.median` = cwhmisc_w.median(x,
+ w = w), `laeken::weightedMedian` = laeken_weightedMedian(x, w = w), unit = "ms")
Table: Benchmarking of weightedMedian(), limma::weighted.median(), cwhmisc::w.median() and laeken::weightedMedian() on n = 10000 data. The top panel shows times in milliseconds and the bottom panel shows relative times.
| expr | min | lq | mean | median | uq | max | |
|---|---|---|---|---|---|---|---|
| 2 | limma::weighted.median | 0.545187 | 0.6093825 | 0.6492610 | 0.619089 | 0.6740665 | 0.978052 |
| 1 | weightedMedian | 0.578573 | 0.6433315 | 0.6766489 | 0.659578 | 0.6857380 | 0.992166 |
| 4 | laeken::weightedMedian | 0.664967 | 0.7302895 | 0.7902390 | 0.750556 | 0.8371090 | 1.129962 |
| 3 | cwhmisc::w.median | 0.699658 | 0.7634875 | 0.8327451 | 0.785022 | 0.8535205 | 1.143068 |
| expr | min | lq | mean | median | uq | max | |
|---|---|---|---|---|---|---|---|
| 2 | limma::weighted.median | 1.000000 | 1.000000 | 1.000000 | 1.000000 | 1.000000 | 1.000000 |
| 1 | weightedMedian | 1.061238 | 1.055710 | 1.042183 | 1.065401 | 1.017315 | 1.014431 |
| 4 | laeken::weightedMedian | 1.219704 | 1.198409 | 1.217136 | 1.212356 | 1.241879 | 1.155319 |
| 3 | cwhmisc::w.median | 1.283336 | 1.252887 | 1.282605 | 1.268028 | 1.266226 | 1.168719 |
Figure: Benchmarking of weightedMedian(), limma::weighted.median(), cwhmisc::w.median() and laeken::weightedMedian() on n = 10000 data. Outliers are displayed as crosses. Times are in milliseconds.
n = 100000 vector
> x <- data[["n = 100000"]]
> w <- runif(length(x))
> gc()
used (Mb) gc trigger (Mb) max used (Mb)
Ncells 5397461 288.3 8529671 455.6 8529671 455.6
Vcells 10580051 80.8 39910282 304.5 101881463 777.3
> stats <- microbenchmark(weightedMedian = weightedMedian(x, w = w, ties = "mean", na.rm = FALSE),
+ `limma::weighted.median` = limma_weighted.median(x, w = w, na.rm = FALSE), `cwhmisc::w.median` = cwhmisc_w.median(x,
+ w = w), `laeken::weightedMedian` = laeken_weightedMedian(x, w = w), unit = "ms")
Table: Benchmarking of weightedMedian(), limma::weighted.median(), cwhmisc::w.median() and laeken::weightedMedian() on n = 100000 data. The top panel shows times in milliseconds and the bottom panel shows relative times.
| expr | min | lq | mean | median | uq | max | |
|---|---|---|---|---|---|---|---|
| 2 | limma::weighted.median | 4.733150 | 5.277121 | 5.913677 | 5.347941 | 5.499855 | 27.941903 |
| 4 | laeken::weightedMedian | 5.125208 | 5.901288 | 6.582535 | 6.083902 | 7.258841 | 14.344677 |
| 3 | cwhmisc::w.median | 6.130579 | 6.908249 | 7.574134 | 7.038625 | 7.916936 | 13.601538 |
| 1 | weightedMedian | 7.185838 | 7.728294 | 7.940226 | 7.854243 | 8.290318 | 8.848144 |
| expr | min | lq | mean | median | uq | max | |
|---|---|---|---|---|---|---|---|
| 2 | limma::weighted.median | 1.000000 | 1.000000 | 1.000000 | 1.000000 | 1.000000 | 1.0000000 |
| 4 | laeken::weightedMedian | 1.082832 | 1.118278 | 1.113104 | 1.137616 | 1.319824 | 0.5133751 |
| 3 | cwhmisc::w.median | 1.295243 | 1.309094 | 1.280782 | 1.316137 | 1.439481 | 0.4867792 |
| 1 | weightedMedian | 1.518194 | 1.464491 | 1.342689 | 1.468648 | 1.507370 | 0.3166622 |
Figure: Benchmarking of weightedMedian(), limma::weighted.median(), cwhmisc::w.median() and laeken::weightedMedian() on n = 100000 data. Outliers are displayed as crosses. Times are in milliseconds.
Appendix
Session information
R version 4.1.1 Patched (2021-08-10 r80727)
Platform: x86_64-pc-linux-gnu (64-bit)
Running under: Ubuntu 18.04.5 LTS
Matrix products: default
BLAS: /home/hb/software/R-devel/R-4-1-branch/lib/R/lib/libRblas.so
LAPACK: /home/hb/software/R-devel/R-4-1-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_1.4-7 matrixStats_0.60.1 ggplot2_3.3.5
[4] knitr_1.33 R.devices_2.17.0 R.utils_2.10.1
[7] R.oo_1.24.0 R.methodsS3_1.8.1-9001 history_0.0.1-9000
loaded via a namespace (and not attached):
[1] Biobase_2.52.0 httr_1.4.2 splines_4.1.1
[4] bit64_4.0.5 network_1.17.1 assertthat_0.2.1
[7] highr_0.9 stats4_4.1.1 blob_1.2.2
[10] GenomeInfoDbData_1.2.6 robustbase_0.93-8 pillar_1.6.2
[13] RSQLite_2.2.8 lattice_0.20-44 limma_3.48.3
[16] glue_1.4.2 digest_0.6.27 XVector_0.32.0
[19] colorspace_2.0-2 Matrix_1.3-4 XML_3.99-0.7
[22] pkgconfig_2.0.3 zlibbioc_1.38.0 genefilter_1.74.0
[25] purrr_0.3.4 ergm_4.1.2 xtable_1.8-4
[28] scales_1.1.1 tibble_3.1.4 annotate_1.70.0
[31] KEGGREST_1.32.0 farver_2.1.0 generics_0.1.0
[34] IRanges_2.26.0 ellipsis_0.3.2 cachem_1.0.6
[37] withr_2.4.2 BiocGenerics_0.38.0 mime_0.11
[40] survival_3.2-13 magrittr_2.0.1 crayon_1.4.1
[43] statnet.common_4.5.0 memoise_2.0.0 laeken_0.5.1
[46] fansi_0.5.0 R.cache_0.15.0 MASS_7.3-54
[49] R.rsp_0.44.0 progressr_0.8.0 tools_4.1.1
[52] lifecycle_1.0.0 S4Vectors_0.30.0 trust_0.1-8
[55] munsell_0.5.0 tabby_0.0.1-9001 AnnotationDbi_1.54.1
[58] Biostrings_2.60.2 compiler_4.1.1 GenomeInfoDb_1.28.1
[61] rlang_0.4.11 grid_4.1.1 RCurl_1.98-1.4
[64] cwhmisc_6.6 rappdirs_0.3.3 startup_0.15.0
[67] labeling_0.4.2 bitops_1.0-7 base64enc_0.1-3
[70] boot_1.3-28 gtable_0.3.0 DBI_1.1.1
[73] markdown_1.1 R6_2.5.1 lpSolveAPI_5.5.2.0-17.7
[76] rle_0.9.2 dplyr_1.0.7 fastmap_1.1.0
[79] bit_4.0.4 utf8_1.2.2 parallel_4.1.1
[82] Rcpp_1.0.7 vctrs_0.3.8 png_0.1-7
[85] DEoptimR_1.0-9 tidyselect_1.1.1 xfun_0.25
[88] coda_0.19-4
Total processing time was 7.65 secs.
Reproducibility
To reproduce this report, do:
html <- matrixStats:::benchmark('weightedMedian')
Copyright Henrik Bengtsson. Last updated on 2021-08-25 19:32:29 (+0200 UTC). Powered by RSP.