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 5364173 286.5 7916910 422.9 7916910 422.9
Vcells 14781498 112.8 39038428 297.9 94934136 724.3
> 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 | 0.003274 | 0.0036255 | 0.0040263 | 0.0039425 | 0.0041750 | 0.014959 |
| 3 | stats:::weighted.mean.default | 0.013181 | 0.0138400 | 0.0143914 | 0.0142245 | 0.0146580 | 0.017979 |
| 2 | stats::weighted.mean | 0.015994 | 0.0166495 | 0.0178758 | 0.0170365 | 0.0176105 | 0.056818 |
| 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 | 4.025962 | 3.817404 | 3.574391 | 3.607990 | 3.510898 | 1.201885 |
| 2 | stats::weighted.mean | 4.885156 | 4.592332 | 4.439814 | 4.321243 | 4.218084 | 3.798249 |
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 5361948 286.4 7916910 422.9 7916910 422.9
Vcells 10905146 83.2 39038428 297.9 94934136 724.3
> 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 | 0.016096 | 0.0179555 | 0.0191847 | 0.0190670 | 0.0197740 | 0.038700 |
| 3 | stats:::weighted.mean.default | 0.082614 | 0.0898050 | 0.0975589 | 0.1001015 | 0.1031745 | 0.123594 |
| 2 | stats::weighted.mean | 0.085836 | 0.0925640 | 0.1008329 | 0.1024425 | 0.1054555 | 0.132351 |
| 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 | 5.132580 | 5.001532 | 5.085243 | 5.249987 | 5.217685 | 3.193643 |
| 2 | stats::weighted.mean | 5.332753 | 5.155189 | 5.255901 | 5.372764 | 5.333038 | 3.419923 |
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 5362020 286.4 7916910 422.9 7916910 422.9
Vcells 10995706 83.9 39038428 297.9 94934136 724.3
> 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 | 0.114370 | 0.1327835 | 0.1441276 | 0.1381935 | 0.1535385 | 0.218476 |
| 2 | stats::weighted.mean | 0.729577 | 0.7686755 | 0.9965166 | 0.8337565 | 1.0613950 | 1.684599 |
| 3 | stats:::weighted.mean.default | 0.705196 | 0.7826270 | 1.2020826 | 0.8345250 | 0.9889665 | 12.022602 |
| 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 | 6.379094 | 5.788938 | 6.914127 | 6.033254 | 6.912892 | 7.710682 |
| 3 | stats:::weighted.mean.default | 6.165918 | 5.894008 | 8.340404 | 6.038815 | 6.441163 | 55.029395 |
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 5362092 286.4 7916910 422.9 7916910 422.9
Vcells 11895755 90.8 39038428 297.9 94934136 724.3
> 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 | 1.406970 | 1.603526 | 1.722735 | 1.670384 | 1.825589 | 3.217925 |
| 3 | stats:::weighted.mean.default | 8.335154 | 8.778807 | 15.285335 | 9.616243 | 10.902740 | 413.538342 |
| 2 | stats::weighted.mean | 8.467202 | 9.058531 | 12.420607 | 10.031783 | 16.389574 | 32.838298 |
| 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 | 5.924187 | 5.474690 | 8.872715 | 5.756904 | 5.972175 | 128.51087 |
| 2 | stats::weighted.mean | 6.018040 | 5.649133 | 7.209820 | 6.005673 | 8.977688 | 10.20481 |
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 5362164 286.4 7916910 422.9 7916910 422.9
Vcells 11452790 87.4 39038428 297.9 94934136 724.3
> 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 | 0.003280 | 0.0035030 | 0.0039449 | 0.0038605 | 0.0041225 | 0.014271 |
| 3 | stats:::weighted.mean.default | 0.011650 | 0.0121230 | 0.0125530 | 0.0123735 | 0.0127780 | 0.015866 |
| 2 | stats::weighted.mean | 0.014216 | 0.0148785 | 0.0162031 | 0.0153355 | 0.0157695 | 0.069540 |
| 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 | 3.551829 | 3.460748 | 3.182062 | 3.205155 | 3.099575 | 1.111765 |
| 2 | stats::weighted.mean | 4.334146 | 4.247359 | 4.107338 | 3.972413 | 3.825227 | 4.872819 |
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 5362236 286.4 7916910 422.9 7916910 422.9
Vcells 11461837 87.5 39038428 297.9 94934136 724.3
> 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 | 0.016451 | 0.0183110 | 0.0194631 | 0.0193665 | 0.0198590 | 0.045951 |
| 3 | stats:::weighted.mean.default | 0.076367 | 0.0817225 | 0.0874114 | 0.0874640 | 0.0897700 | 0.121269 |
| 2 | stats::weighted.mean | 0.077437 | 0.0846850 | 0.0903972 | 0.0912690 | 0.0938265 | 0.109977 |
| 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 | 4.642089 | 4.463028 | 4.491125 | 4.516252 | 4.520369 | 2.639094 |
| 2 | stats::weighted.mean | 4.707130 | 4.624816 | 4.644533 | 4.712726 | 4.724634 | 2.393354 |
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 5362308 286.4 7916910 422.9 7916910 422.9
Vcells 11552257 88.2 39038428 297.9 94934136 724.3
> 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 | 0.115135 | 0.1317750 | 0.1427569 | 0.138079 | 0.147869 | 0.249905 |
| 3 | stats:::weighted.mean.default | 0.608082 | 0.6768860 | 0.9011484 | 0.704362 | 0.780388 | 8.066233 |
| 2 | stats::weighted.mean | 0.596462 | 0.6850695 | 0.8050036 | 0.719753 | 0.799573 | 1.533540 |
| 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 | 5.281470 | 5.136680 | 6.312470 | 5.101152 | 5.277563 | 32.277197 |
| 2 | stats::weighted.mean | 5.180545 | 5.198782 | 5.638984 | 5.212617 | 5.407306 | 6.136492 |
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 5362380 286.4 7916910 422.9 7916910 422.9
Vcells 12452692 95.1 39038428 297.9 94934136 724.3
> 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 | 1.565088 | 1.716951 | 1.815459 | 1.782803 | 1.900802 | 2.440462 |
| 2 | stats::weighted.mean | 8.077002 | 8.489081 | 15.000146 | 9.015730 | 15.796859 | 417.681015 |
| 3 | stats:::weighted.mean.default | 8.077910 | 8.561706 | 10.497486 | 9.104963 | 10.068560 | 20.628094 |
| 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 | 5.160734 | 4.944275 | 8.262454 | 5.057052 | 8.310628 | 171.148338 |
| 3 | stats:::weighted.mean.default | 5.161314 | 4.986574 | 5.782277 | 5.107104 | 5.297006 | 8.452536 |
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_0.60.0 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] rstudioapi_0.13 rappdirs_0.3.3 startup_0.15.0-9000
[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 18.03 secs.
Reproducibility
To reproduce this report, do:
html <- matrixStats:::benchmark('weightedMean')
Copyright Henrik Bengtsson. Last updated on 2021-08-25 22:52:44 (+0200 UTC). Powered by RSP.