matrixStats.benchmarks
weightedMean() benchmarks
This report benchmark the performance of weightedMean() against alternative methods.
Alternative methods
- stats::weighted.mean()
- stats:::weighted.mean.default()
Data type “integer”
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 = mode)
> data <- data[1:4]
Results
n = 1000 vector
> x <- data[["n = 1000"]]
> w <- runif(length(x))
> gc()
used (Mb) gc trigger (Mb) max used (Mb)
Ncells 5352119 285.9 8529671 455.6 8529671 455.6
Vcells 14728821 112.4 39910282 304.5 101881463 777.3
> stats <- microbenchmark(weightedMean = weightedMean(x, w = w, na.rm = FALSE), `stats::weighted.mean` = weighted.mean(x,
+ w = w, na.rm = FALSE), `stats:::weighted.mean.default` = weighted.mean.default(x, w = w, na.rm = FALSE),
+ unit = "ms")
Table: Benchmarking of weightedMean(), stats::weighted.mean() and stats:::weighted.mean.default() on integer+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 | weightedMean | 0.003554 | 0.0038765 | 0.0042425 | 0.004086 | 0.0043210 | 0.014865 |
| 3 | stats:::weighted.mean.default | 0.012649 | 0.0133130 | 0.0141111 | 0.013756 | 0.0141995 | 0.022972 |
| 2 | stats::weighted.mean | 0.015332 | 0.0160075 | 0.0173936 | 0.016446 | 0.0170265 | 0.057782 |
| expr | min | lq | mean | median | uq | max | |
|---|---|---|---|---|---|---|---|
| 1 | weightedMean | 1.000000 | 1.000000 | 1.000000 | 1.000000 | 1.000000 | 1.000000 |
| 3 | stats:::weighted.mean.default | 3.559088 | 3.434283 | 3.326126 | 3.366618 | 3.286161 | 1.545375 |
| 2 | stats::weighted.mean | 4.314012 | 4.129369 | 4.099835 | 4.024963 | 3.940407 | 3.887117 |
Figure: Benchmarking of weightedMean(), stats::weighted.mean() and stats:::weighted.mean.default() on integer+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 5349894 285.8 8529671 455.6 8529671 455.6
Vcells 10852469 82.8 39910282 304.5 101881463 777.3
> stats <- microbenchmark(weightedMean = weightedMean(x, w = w, na.rm = FALSE), `stats::weighted.mean` = weighted.mean(x,
+ w = w, na.rm = FALSE), `stats:::weighted.mean.default` = weighted.mean.default(x, w = w, na.rm = FALSE),
+ unit = "ms")
Table: Benchmarking of weightedMean(), stats::weighted.mean() and stats:::weighted.mean.default() on integer+n = 10000 data. The top panel shows times in milliseconds and the bottom panel shows relative times.
| expr | min | lq | mean | median | uq | max | |
|---|---|---|---|---|---|---|---|
| 1 | weightedMean | 0.018325 | 0.0203285 | 0.0219793 | 0.0213275 | 0.0232075 | 0.041788 |
| 3 | stats:::weighted.mean.default | 0.077805 | 0.0840210 | 0.0942203 | 0.0918735 | 0.1006830 | 0.135779 |
| 2 | stats::weighted.mean | 0.080080 | 0.0867120 | 0.0953404 | 0.0939730 | 0.1028940 | 0.149423 |
| expr | min | lq | mean | median | uq | max | |
|---|---|---|---|---|---|---|---|
| 1 | weightedMean | 1.000000 | 1.000000 | 1.000000 | 1.000000 | 1.000000 | 1.000000 |
| 3 | stats:::weighted.mean.default | 4.245839 | 4.133163 | 4.286769 | 4.307748 | 4.338382 | 3.249234 |
| 2 | stats::weighted.mean | 4.369986 | 4.265538 | 4.337731 | 4.406189 | 4.433653 | 3.575739 |
Figure: Benchmarking of weightedMean(), stats::weighted.mean() and stats:::weighted.mean.default() on integer+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 5349966 285.8 8529671 455.6 8529671 455.6
Vcells 10943029 83.5 39910282 304.5 101881463 777.3
> stats <- microbenchmark(weightedMean = weightedMean(x, w = w, na.rm = FALSE), `stats::weighted.mean` = weighted.mean(x,
+ w = w, na.rm = FALSE), `stats:::weighted.mean.default` = weighted.mean.default(x, w = w, na.rm = FALSE),
+ unit = "ms")
Table: Benchmarking of weightedMean(), stats::weighted.mean() and stats:::weighted.mean.default() on integer+n = 100000 data. The top panel shows times in milliseconds and the bottom panel shows relative times.
| expr | min | lq | mean | median | uq | max | |
|---|---|---|---|---|---|---|---|
| 1 | weightedMean | 0.131054 | 0.1460465 | 0.1605186 | 0.1601670 | 0.1688735 | 0.259953 |
| 3 | stats:::weighted.mean.default | 0.618991 | 0.6735105 | 0.7964450 | 0.7319125 | 0.7765990 | 6.914641 |
| 2 | stats::weighted.mean | 0.609983 | 0.6664820 | 0.7999479 | 0.7393500 | 0.7878000 | 6.785375 |
| expr | min | lq | mean | median | uq | max | |
|---|---|---|---|---|---|---|---|
| 1 | weightedMean | 1.000000 | 1.000000 | 1.000000 | 1.000000 | 1.000000 | 1.00000 |
| 3 | stats:::weighted.mean.default | 4.723175 | 4.611617 | 4.961701 | 4.569684 | 4.598703 | 26.59958 |
| 2 | stats::weighted.mean | 4.654440 | 4.563492 | 4.983523 | 4.616119 | 4.665030 | 26.10231 |
Figure: Benchmarking of weightedMean(), stats::weighted.mean() and stats:::weighted.mean.default() on integer+n = 100000 data. Outliers are displayed as crosses. Times are in milliseconds.
n = 1000000 vector
> x <- data[["n = 1000000"]]
> w <- runif(length(x))
> gc()
used (Mb) gc trigger (Mb) max used (Mb)
Ncells 5350038 285.8 8529671 455.6 8529671 455.6
Vcells 11843078 90.4 39910282 304.5 101881463 777.3
> stats <- microbenchmark(weightedMean = weightedMean(x, w = w, na.rm = FALSE), `stats::weighted.mean` = weighted.mean(x,
+ w = w, na.rm = FALSE), `stats:::weighted.mean.default` = weighted.mean.default(x, w = w, na.rm = FALSE),
+ unit = "ms")
Table: Benchmarking of weightedMean(), stats::weighted.mean() and stats:::weighted.mean.default() on integer+n = 1000000 data. The top panel shows times in milliseconds and the bottom panel shows relative times.
| expr | min | lq | mean | median | uq | max | |
|---|---|---|---|---|---|---|---|
| 1 | weightedMean | 1.467324 | 1.633701 | 1.777074 | 1.762264 | 1.878858 | 2.634377 |
| 2 | stats::weighted.mean | 8.134872 | 8.667870 | 13.391080 | 14.656375 | 15.778118 | 20.657118 |
| 3 | stats:::weighted.mean.default | 8.155833 | 11.982577 | 17.988249 | 14.795355 | 16.281689 | 388.108153 |
| expr | min | lq | mean | median | uq | max | |
|---|---|---|---|---|---|---|---|
| 1 | weightedMean | 1.000000 | 1.000000 | 1.000000 | 1.000000 | 1.000000 | 1.000000 |
| 2 | stats::weighted.mean | 5.544019 | 5.305665 | 7.535465 | 8.316785 | 8.397715 | 7.841367 |
| 3 | stats:::weighted.mean.default | 5.558304 | 7.334621 | 10.122396 | 8.395649 | 8.665735 | 147.324454 |
Figure: Benchmarking of weightedMean(), stats::weighted.mean() and stats:::weighted.mean.default() on integer+n = 1000000 data. Outliers are displayed as crosses. Times are in milliseconds.
Data type “double”
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 = mode)
> data <- data[1:4]
Results
n = 1000 vector
> x <- data[["n = 1000"]]
> w <- runif(length(x))
> gc()
used (Mb) gc trigger (Mb) max used (Mb)
Ncells 5350110 285.8 8529671 455.6 8529671 455.6
Vcells 11400115 87.0 39910282 304.5 101881463 777.3
> stats <- microbenchmark(weightedMean = weightedMean(x, w = w, na.rm = FALSE), `stats::weighted.mean` = weighted.mean(x,
+ w = w, na.rm = FALSE), `stats:::weighted.mean.default` = weighted.mean.default(x, w = w, na.rm = FALSE),
+ unit = "ms")
Table: Benchmarking of weightedMean(), stats::weighted.mean() and stats:::weighted.mean.default() on double+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 | weightedMean | 0.004253 | 0.0046000 | 0.0048706 | 0.0047805 | 0.0049660 | 0.014688 |
| 3 | stats:::weighted.mean.default | 0.011548 | 0.0122840 | 0.0129203 | 0.0126800 | 0.0130725 | 0.019308 |
| 2 | stats::weighted.mean | 0.014597 | 0.0151665 | 0.0161945 | 0.0156825 | 0.0161095 | 0.054553 |
| expr | min | lq | mean | median | uq | max | |
|---|---|---|---|---|---|---|---|
| 1 | weightedMean | 1.000000 | 1.000000 | 1.000000 | 1.000000 | 1.000000 | 1.000000 |
| 3 | stats:::weighted.mean.default | 2.715260 | 2.670435 | 2.652700 | 2.652442 | 2.632400 | 1.314542 |
| 2 | stats::weighted.mean | 3.432165 | 3.297065 | 3.324927 | 3.280515 | 3.243959 | 3.714120 |
Figure: Benchmarking of weightedMean(), stats::weighted.mean() and stats:::weighted.mean.default() on double+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 5350182 285.8 8529671 455.6 8529671 455.6
Vcells 11409162 87.1 39910282 304.5 101881463 777.3
> stats <- microbenchmark(weightedMean = weightedMean(x, w = w, na.rm = FALSE), `stats::weighted.mean` = weighted.mean(x,
+ w = w, na.rm = FALSE), `stats:::weighted.mean.default` = weighted.mean.default(x, w = w, na.rm = FALSE),
+ unit = "ms")
Table: Benchmarking of weightedMean(), stats::weighted.mean() and stats:::weighted.mean.default() on double+n = 10000 data. The top panel shows times in milliseconds and the bottom panel shows relative times.
| expr | min | lq | mean | median | uq | max | |
|---|---|---|---|---|---|---|---|
| 1 | weightedMean | 0.022961 | 0.0251280 | 0.0272974 | 0.0269420 | 0.0290120 | 0.045982 |
| 3 | stats:::weighted.mean.default | 0.068556 | 0.0778715 | 0.0845626 | 0.0836435 | 0.0914995 | 0.116382 |
| 2 | stats::weighted.mean | 0.072660 | 0.0790480 | 0.0869074 | 0.0847945 | 0.0947230 | 0.115657 |
| expr | min | lq | mean | median | uq | max | |
|---|---|---|---|---|---|---|---|
| 1 | weightedMean | 1.000000 | 1.000000 | 1.000000 | 1.000000 | 1.000000 | 1.000000 |
| 3 | stats:::weighted.mean.default | 2.985759 | 3.098993 | 3.097827 | 3.104576 | 3.153850 | 2.531034 |
| 2 | stats::weighted.mean | 3.164496 | 3.145813 | 3.183726 | 3.147298 | 3.264959 | 2.515267 |
Figure: Benchmarking of weightedMean(), stats::weighted.mean() and stats:::weighted.mean.default() on double+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 5350254 285.8 8529671 455.6 8529671 455.6
Vcells 11499582 87.8 39910282 304.5 101881463 777.3
> stats <- microbenchmark(weightedMean = weightedMean(x, w = w, na.rm = FALSE), `stats::weighted.mean` = weighted.mean(x,
+ w = w, na.rm = FALSE), `stats:::weighted.mean.default` = weighted.mean.default(x, w = w, na.rm = FALSE),
+ unit = "ms")
Table: Benchmarking of weightedMean(), stats::weighted.mean() and stats:::weighted.mean.default() on double+n = 100000 data. The top panel shows times in milliseconds and the bottom panel shows relative times.
| expr | min | lq | mean | median | uq | max | |
|---|---|---|---|---|---|---|---|
| 1 | weightedMean | 0.164742 | 0.1798885 | 0.2042444 | 0.1983825 | 0.218504 | 0.328262 |
| 3 | stats:::weighted.mean.default | 0.671135 | 0.7336850 | 1.1221700 | 1.3418750 | 1.433450 | 1.648322 |
| 2 | stats::weighted.mean | 0.671305 | 0.7460795 | 1.4172483 | 1.3650350 | 1.457627 | 14.172368 |
| expr | min | lq | mean | median | uq | max | |
|---|---|---|---|---|---|---|---|
| 1 | weightedMean | 1.000000 | 1.000000 | 1.000000 | 1.000000 | 1.000000 | 1.000000 |
| 3 | stats:::weighted.mean.default | 4.073855 | 4.078554 | 5.494252 | 6.764080 | 6.560294 | 5.021361 |
| 2 | stats::weighted.mean | 4.074887 | 4.147455 | 6.938984 | 6.880824 | 6.670940 | 43.173952 |
Figure: Benchmarking of weightedMean(), stats::weighted.mean() and stats:::weighted.mean.default() on double+n = 100000 data. Outliers are displayed as crosses. Times are in milliseconds.
n = 1000000 vector
> x <- data[["n = 1000000"]]
> w <- runif(length(x))
> gc()
used (Mb) gc trigger (Mb) max used (Mb)
Ncells 5350326 285.8 8529671 455.6 8529671 455.6
Vcells 12400020 94.7 39910282 304.5 101881463 777.3
> stats <- microbenchmark(weightedMean = weightedMean(x, w = w, na.rm = FALSE), `stats::weighted.mean` = weighted.mean(x,
+ w = w, na.rm = FALSE), `stats:::weighted.mean.default` = weighted.mean.default(x, w = w, na.rm = FALSE),
+ unit = "ms")
Table: Benchmarking of weightedMean(), stats::weighted.mean() and stats:::weighted.mean.default() on double+n = 1000000 data. The top panel shows times in milliseconds and the bottom panel shows relative times.
| expr | min | lq | mean | median | uq | max | |
|---|---|---|---|---|---|---|---|
| 1 | weightedMean | 2.248108 | 2.385636 | 2.531837 | 2.461687 | 2.634478 | 3.161734 |
| 2 | stats::weighted.mean | 7.958758 | 8.346032 | 10.276995 | 8.802524 | 9.724305 | 22.125856 |
| 3 | stats:::weighted.mean.default | 7.975445 | 8.356922 | 11.019651 | 8.969589 | 15.185637 | 29.311026 |
| expr | min | lq | mean | median | uq | max | |
|---|---|---|---|---|---|---|---|
| 1 | weightedMean | 1.000000 | 1.000000 | 1.000000 | 1.000000 | 1.000000 | 1.000000 |
| 2 | stats::weighted.mean | 3.540203 | 3.498452 | 4.059105 | 3.575810 | 3.691170 | 6.998013 |
| 3 | stats:::weighted.mean.default | 3.547625 | 3.503016 | 4.352432 | 3.643676 | 5.764192 | 9.270554 |
Figure: Benchmarking of weightedMean(), stats::weighted.mean() and stats:::weighted.mean.default() on double+n = 1000000 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 glue_1.4.2
[16] digest_0.6.27 XVector_0.32.0 colorspace_2.0-2
[19] Matrix_1.3-4 XML_3.99-0.7 pkgconfig_2.0.3
[22] zlibbioc_1.38.0 genefilter_1.74.0 purrr_0.3.4
[25] ergm_4.1.2 xtable_1.8-4 scales_1.1.1
[28] tibble_3.1.4 annotate_1.70.0 KEGGREST_1.32.0
[31] farver_2.1.0 generics_0.1.0 IRanges_2.26.0
[34] ellipsis_0.3.2 cachem_1.0.6 withr_2.4.2
[37] BiocGenerics_0.38.0 mime_0.11 survival_3.2-13
[40] magrittr_2.0.1 crayon_1.4.1 statnet.common_4.5.0
[43] memoise_2.0.0 laeken_0.5.1 fansi_0.5.0
[46] R.cache_0.15.0 MASS_7.3-54 R.rsp_0.44.0
[49] progressr_0.8.0 tools_4.1.1 lifecycle_1.0.0
[52] S4Vectors_0.30.0 trust_0.1-8 munsell_0.5.0
[55] tabby_0.0.1-9001 AnnotationDbi_1.54.1 Biostrings_2.60.2
[58] compiler_4.1.1 GenomeInfoDb_1.28.1 rlang_0.4.11
[61] grid_4.1.1 RCurl_1.98-1.4 cwhmisc_6.6
[64] rappdirs_0.3.3 startup_0.15.0 labeling_0.4.2
[67] bitops_1.0-7 base64enc_0.1-3 boot_1.3-28
[70] gtable_0.3.0 DBI_1.1.1 markdown_1.1
[73] R6_2.5.1 lpSolveAPI_5.5.2.0-17.7 rle_0.9.2
[76] dplyr_1.0.7 fastmap_1.1.0 bit_4.0.4
[79] utf8_1.2.2 parallel_4.1.1 Rcpp_1.0.7
[82] vctrs_0.3.8 png_0.1-7 DEoptimR_1.0-9
[85] tidyselect_1.1.1 xfun_0.25 coda_0.19-4
Total processing time was 17.49 secs.
Reproducibility
To reproduce this report, do:
html <- matrixStats:::benchmark('weightedMean')
Copyright Henrik Bengtsson. Last updated on 2021-08-25 19:32:14 (+0200 UTC). Powered by RSP.