madDiff_subset - HenrikBengtsson/matrixStats GitHub Wiki
matrixStats: Benchmark report
This report benchmark the performance of madDiff() on subsetted computation.
> 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)
> x <- data[["n = 1000"]]
> idxs <- sample.int(length(x), size = length(x) * 0.7)
> x_S <- x[idxs]
> gc()
used (Mb) gc trigger (Mb) max used (Mb)
Ncells 3234356 172.8 5709258 305.0 5709258 305.0
Vcells 12934169 98.7 24515964 187.1 57084605 435.6
> stats <- microbenchmark(madDiff_x_S = madDiff(x_S), `madDiff(x, idxs)` = madDiff(x, idxs = idxs),
+ `madDiff(x[idxs])` = madDiff(x[idxs]), unit = "ms")
Table: Benchmarking of madDiff_x_S(), madDiff(x, idxs)() and madDiff(x[idxs])() 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 | madDiff_x_S | 0.055190 | 0.0564065 | 0.0574278 | 0.0568090 | 0.0574705 | 0.077206 |
3 | madDiff(x[idxs]) | 0.057079 | 0.0582120 | 0.0618246 | 0.0588055 | 0.0595500 | 0.307727 |
2 | madDiff(x, idxs) | 0.057541 | 0.0586050 | 0.0598237 | 0.0589665 | 0.0597920 | 0.088428 |
expr | min | lq | mean | median | uq | max | |
---|---|---|---|---|---|---|---|
1 | madDiff_x_S | 1.000000 | 1.000000 | 1.000000 | 1.000000 | 1.000000 | 1.000000 |
3 | madDiff(x[idxs]) | 1.034227 | 1.032009 | 1.076562 | 1.035144 | 1.036184 | 3.985791 |
2 | madDiff(x, idxs) | 1.042598 | 1.038976 | 1.041720 | 1.037978 | 1.040395 | 1.145351 |
Figure: Benchmarking of madDiff_x_S(), madDiff(x, idxs)() and madDiff(x[idxs])() on integer+n = 1000 data. Outliers are displayed as crosses. Times are in milliseconds.
> x <- data[["n = 10000"]]
> idxs <- sample.int(length(x), size = length(x) * 0.7)
> x_S <- x[idxs]
> gc()
used (Mb) gc trigger (Mb) max used (Mb)
Ncells 3232097 172.7 5709258 305.0 5709258 305.0
Vcells 11805394 90.1 24515964 187.1 57084605 435.6
> stats <- microbenchmark(madDiff_x_S = madDiff(x_S), `madDiff(x, idxs)` = madDiff(x, idxs = idxs),
+ `madDiff(x[idxs])` = madDiff(x[idxs]), unit = "ms")
Table: Benchmarking of madDiff_x_S(), madDiff(x, idxs)() and madDiff(x[idxs])() 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 | madDiff_x_S | 0.224246 | 0.231654 | 0.2367327 | 0.2368285 | 0.2414330 | 0.276658 |
3 | madDiff(x[idxs]) | 0.235690 | 0.242172 | 0.2499910 | 0.2497330 | 0.2546955 | 0.346952 |
2 | madDiff(x, idxs) | 0.236465 | 0.242802 | 0.2500311 | 0.2499390 | 0.2556420 | 0.275142 |
expr | min | lq | mean | median | uq | max | |
---|---|---|---|---|---|---|---|
1 | madDiff_x_S | 1.000000 | 1.000000 | 1.000000 | 1.000000 | 1.000000 | 1.0000000 |
3 | madDiff(x[idxs]) | 1.051033 | 1.045404 | 1.056005 | 1.054489 | 1.054932 | 1.2540827 |
2 | madDiff(x, idxs) | 1.054489 | 1.048123 | 1.056175 | 1.055359 | 1.058853 | 0.9945203 |
Figure: Benchmarking of madDiff_x_S(), madDiff(x, idxs)() and madDiff(x[idxs])() on integer+n = 10000 data. Outliers are displayed as crosses. Times are in milliseconds.
> x <- data[["n = 100000"]]
> idxs <- sample.int(length(x), size = length(x) * 0.7)
> x_S <- x[idxs]
> gc()
used (Mb) gc trigger (Mb) max used (Mb)
Ncells 3232169 172.7 5709258 305.0 5709258 305.0
Vcells 11868954 90.6 24515964 187.1 57084605 435.6
> stats <- microbenchmark(madDiff_x_S = madDiff(x_S), `madDiff(x, idxs)` = madDiff(x, idxs = idxs),
+ `madDiff(x[idxs])` = madDiff(x[idxs]), unit = "ms")
Table: Benchmarking of madDiff_x_S(), madDiff(x, idxs)() and madDiff(x[idxs])() 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 | madDiff_x_S | 2.095817 | 2.243695 | 2.389170 | 2.282126 | 2.348625 | 8.611197 |
2 | madDiff(x, idxs) | 2.230470 | 2.363672 | 2.647164 | 2.423176 | 2.512975 | 8.659484 |
3 | madDiff(x[idxs]) | 2.215638 | 2.397924 | 2.642880 | 2.446655 | 2.492496 | 8.566503 |
expr | min | lq | mean | median | uq | max | |
---|---|---|---|---|---|---|---|
1 | madDiff_x_S | 1.000000 | 1.000000 | 1.000000 | 1.000000 | 1.000000 | 1.0000000 |
2 | madDiff(x, idxs) | 1.064248 | 1.053473 | 1.107985 | 1.061806 | 1.069977 | 1.0056075 |
3 | madDiff(x[idxs]) | 1.057171 | 1.068738 | 1.106192 | 1.072095 | 1.061257 | 0.9948098 |
Figure: Benchmarking of madDiff_x_S(), madDiff(x, idxs)() and madDiff(x[idxs])() on integer+n = 100000 data. Outliers are displayed as crosses. Times are in milliseconds.
> x <- data[["n = 1000000"]]
> idxs <- sample.int(length(x), size = length(x) * 0.7)
> x_S <- x[idxs]
> gc()
used (Mb) gc trigger (Mb) max used (Mb)
Ncells 3232241 172.7 5709258 305.0 5709258 305.0
Vcells 12499003 95.4 24515964 187.1 57084605 435.6
> stats <- microbenchmark(madDiff_x_S = madDiff(x_S), `madDiff(x, idxs)` = madDiff(x, idxs = idxs),
+ `madDiff(x[idxs])` = madDiff(x[idxs]), unit = "ms")
Table: Benchmarking of madDiff_x_S(), madDiff(x, idxs)() and madDiff(x[idxs])() 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 | madDiff_x_S | 18.44959 | 18.95174 | 24.86004 | 20.37979 | 25.57697 | 292.22811 |
2 | madDiff(x, idxs) | 21.57473 | 22.66015 | 31.62577 | 24.88276 | 29.63818 | 288.79574 |
3 | madDiff(x[idxs]) | 21.43227 | 22.80355 | 27.10503 | 27.99392 | 29.61907 | 49.22115 |
expr | min | lq | mean | median | uq | max | |
---|---|---|---|---|---|---|---|
1 | madDiff_x_S | 1.000000 | 1.000000 | 1.000000 | 1.000000 | 1.000000 | 1.0000000 |
2 | madDiff(x, idxs) | 1.169388 | 1.195676 | 1.272153 | 1.220953 | 1.158784 | 0.9882545 |
3 | madDiff(x[idxs]) | 1.161666 | 1.203243 | 1.090305 | 1.373612 | 1.158037 | 0.1684340 |
Figure: Benchmarking of madDiff_x_S(), madDiff(x, idxs)() and madDiff(x[idxs])() on integer+n = 1000000 data. Outliers are displayed as crosses. Times are in milliseconds.
> x <- data[["n = 10000000"]]
> idxs <- sample.int(length(x), size = length(x) * 0.7)
> x_S <- x[idxs]
> gc()
used (Mb) gc trigger (Mb) max used (Mb)
Ncells 3232310 172.7 5709258 305.0 5709258 305.0
Vcells 18799046 143.5 29499156 225.1 57084605 435.6
> stats <- microbenchmark(madDiff_x_S = madDiff(x_S), `madDiff(x, idxs)` = madDiff(x, idxs = idxs),
+ `madDiff(x[idxs])` = madDiff(x[idxs]), unit = "ms")
Table: Benchmarking of madDiff_x_S(), madDiff(x, idxs)() and madDiff(x[idxs])() on integer+n = 10000000 data. The top panel shows times in milliseconds and the bottom panel shows relative times.
expr | min | lq | mean | median | uq | max | |
---|---|---|---|---|---|---|---|
1 | madDiff_x_S | 173.4395 | 192.0317 | 233.7796 | 200.0665 | 221.1509 | 524.2280 |
2 | madDiff(x, idxs) | 290.4968 | 320.6389 | 387.9749 | 334.1310 | 364.1433 | 668.4249 |
3 | madDiff(x[idxs]) | 303.8622 | 325.1975 | 387.9358 | 335.7142 | 368.6222 | 651.9459 |
expr | min | lq | mean | median | uq | max | |
---|---|---|---|---|---|---|---|
1 | madDiff_x_S | 1.000000 | 1.000000 | 1.000000 | 1.000000 | 1.000000 | 1.000000 |
2 | madDiff(x, idxs) | 1.674917 | 1.669719 | 1.659576 | 1.670099 | 1.646583 | 1.275065 |
3 | madDiff(x[idxs]) | 1.751978 | 1.693458 | 1.659409 | 1.678013 | 1.666836 | 1.243631 |
Figure: Benchmarking of madDiff_x_S(), madDiff(x, idxs)() and madDiff(x[idxs])() on integer+n = 10000000 data. Outliers are displayed as crosses. Times are in milliseconds.
> 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)
> x <- data[["n = 1000"]]
> idxs <- sample.int(length(x), size = length(x) * 0.7)
> x_S <- x[idxs]
> gc()
used (Mb) gc trigger (Mb) max used (Mb)
Ncells 3232391 172.7 5709258 305 5709258 305
Vcells 17356258 132.5 61598888 470 61463574 469
> stats <- microbenchmark(madDiff_x_S = madDiff(x_S), `madDiff(x, idxs)` = madDiff(x, idxs = idxs),
+ `madDiff(x[idxs])` = madDiff(x[idxs]), unit = "ms")
Table: Benchmarking of madDiff_x_S(), madDiff(x, idxs)() and madDiff(x[idxs])() 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 | madDiff_x_S | 0.070199 | 0.0715985 | 0.0727082 | 0.0722565 | 0.0730440 | 0.094207 |
3 | madDiff(x[idxs]) | 0.072466 | 0.0737975 | 0.0762348 | 0.0743830 | 0.0751275 | 0.218112 |
2 | madDiff(x, idxs) | 0.072472 | 0.0740670 | 0.0748416 | 0.0745635 | 0.0752430 | 0.084222 |
expr | min | lq | mean | median | uq | max | |
---|---|---|---|---|---|---|---|
1 | madDiff_x_S | 1.000000 | 1.000000 | 1.000000 | 1.000000 | 1.000000 | 1.000000 |
3 | madDiff(x[idxs]) | 1.032294 | 1.030713 | 1.048503 | 1.029430 | 1.028524 | 2.315242 |
2 | madDiff(x, idxs) | 1.032379 | 1.034477 | 1.029341 | 1.031928 | 1.030105 | 0.894010 |
Figure: Benchmarking of madDiff_x_S(), madDiff(x, idxs)() and madDiff(x[idxs])() on double+n = 1000 data. Outliers are displayed as crosses. Times are in milliseconds.
> x <- data[["n = 10000"]]
> idxs <- sample.int(length(x), size = length(x) * 0.7)
> x_S <- x[idxs]
> gc()
used (Mb) gc trigger (Mb) max used (Mb)
Ncells 3232457 172.7 5709258 305 5709258 305
Vcells 17365745 132.5 61598888 470 61463574 469
> stats <- microbenchmark(madDiff_x_S = madDiff(x_S), `madDiff(x, idxs)` = madDiff(x, idxs = idxs),
+ `madDiff(x[idxs])` = madDiff(x[idxs]), unit = "ms")
Table: Benchmarking of madDiff_x_S(), madDiff(x, idxs)() and madDiff(x[idxs])() 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 | madDiff_x_S | 0.317826 | 0.3262430 | 0.3311348 | 0.3301385 | 0.3354795 | 0.353511 |
2 | madDiff(x, idxs) | 0.331684 | 0.3391555 | 0.3455184 | 0.3444140 | 0.3505610 | 0.370233 |
3 | madDiff(x[idxs]) | 0.332179 | 0.3394500 | 0.3471538 | 0.3448690 | 0.3507025 | 0.464242 |
expr | min | lq | mean | median | uq | max | |
---|---|---|---|---|---|---|---|
1 | madDiff_x_S | 1.000000 | 1.000000 | 1.000000 | 1.000000 | 1.000000 | 1.000000 |
2 | madDiff(x, idxs) | 1.043602 | 1.039579 | 1.043437 | 1.043241 | 1.044955 | 1.047303 |
3 | madDiff(x[idxs]) | 1.045160 | 1.040482 | 1.048376 | 1.044619 | 1.045377 | 1.313232 |
Figure: Benchmarking of madDiff_x_S(), madDiff(x, idxs)() and madDiff(x[idxs])() on double+n = 10000 data. Outliers are displayed as crosses. Times are in milliseconds.
> x <- data[["n = 100000"]]
> idxs <- sample.int(length(x), size = length(x) * 0.7)
> x_S <- x[idxs]
> gc()
used (Mb) gc trigger (Mb) max used (Mb)
Ncells 3232529 172.7 5709258 305 5709258 305
Vcells 17460624 133.3 61598888 470 61463574 469
> stats <- microbenchmark(madDiff_x_S = madDiff(x_S), `madDiff(x, idxs)` = madDiff(x, idxs = idxs),
+ `madDiff(x[idxs])` = madDiff(x[idxs]), unit = "ms")
Table: Benchmarking of madDiff_x_S(), madDiff(x, idxs)() and madDiff(x[idxs])() 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 | madDiff_x_S | 2.950452 | 3.116595 | 3.321669 | 3.177805 | 3.455529 | 4.418886 |
3 | madDiff(x[idxs]) | 3.139160 | 3.289204 | 3.657241 | 3.368537 | 3.667559 | 10.142200 |
2 | madDiff(x, idxs) | 3.098777 | 3.283048 | 3.520841 | 3.370732 | 3.456164 | 8.919457 |
expr | min | lq | mean | median | uq | max | |
---|---|---|---|---|---|---|---|
1 | madDiff_x_S | 1.000000 | 1.000000 | 1.000000 | 1.000000 | 1.000000 | 1.000000 |
3 | madDiff(x[idxs]) | 1.063959 | 1.055384 | 1.101025 | 1.060020 | 1.061360 | 2.295194 |
2 | madDiff(x, idxs) | 1.050272 | 1.053409 | 1.059962 | 1.060711 | 1.000184 | 2.018485 |
Figure: Benchmarking of madDiff_x_S(), madDiff(x, idxs)() and madDiff(x[idxs])() on double+n = 100000 data. Outliers are displayed as crosses. Times are in milliseconds.
> x <- data[["n = 1000000"]]
> idxs <- sample.int(length(x), size = length(x) * 0.7)
> x_S <- x[idxs]
> gc()
used (Mb) gc trigger (Mb) max used (Mb)
Ncells 3232601 172.7 5709258 305 5709258 305.0
Vcells 18406067 140.5 61598888 470 61572274 469.8
> stats <- microbenchmark(madDiff_x_S = madDiff(x_S), `madDiff(x, idxs)` = madDiff(x, idxs = idxs),
+ `madDiff(x[idxs])` = madDiff(x[idxs]), unit = "ms")
Table: Benchmarking of madDiff_x_S(), madDiff(x, idxs)() and madDiff(x[idxs])() 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 | madDiff_x_S | 25.79942 | 27.48665 | 30.40725 | 28.45360 | 32.35795 | 57.97113 |
3 | madDiff(x[idxs]) | 34.84448 | 37.06804 | 42.82830 | 38.03059 | 43.09852 | 321.29250 |
2 | madDiff(x, idxs) | 35.60745 | 37.15369 | 40.41188 | 38.27959 | 43.03884 | 58.87006 |
expr | min | lq | mean | median | uq | max | |
---|---|---|---|---|---|---|---|
1 | madDiff_x_S | 1.000000 | 1.000000 | 1.000000 | 1.000000 | 1.000000 | 1.000000 |
3 | madDiff(x[idxs]) | 1.350591 | 1.348583 | 1.408490 | 1.336583 | 1.331930 | 5.542284 |
2 | madDiff(x, idxs) | 1.380165 | 1.351699 | 1.329021 | 1.345334 | 1.330085 | 1.015506 |
Figure: Benchmarking of madDiff_x_S(), madDiff(x, idxs)() and madDiff(x[idxs])() on double+n = 1000000 data. Outliers are displayed as crosses. Times are in milliseconds.
> x <- data[["n = 10000000"]]
> idxs <- sample.int(length(x), size = length(x) * 0.7)
> x_S <- x[idxs]
> gc()
used (Mb) gc trigger (Mb) max used (Mb)
Ncells 3232673 172.7 5709258 305 5709258 305.0
Vcells 27856115 212.6 61598888 470 61572274 469.8
> stats <- microbenchmark(madDiff_x_S = madDiff(x_S), `madDiff(x, idxs)` = madDiff(x, idxs = idxs),
+ `madDiff(x[idxs])` = madDiff(x[idxs]), unit = "ms")
Table: Benchmarking of madDiff_x_S(), madDiff(x, idxs)() and madDiff(x[idxs])() on double+n = 10000000 data. The top panel shows times in milliseconds and the bottom panel shows relative times.
expr | min | lq | mean | median | uq | max | |
---|---|---|---|---|---|---|---|
1 | madDiff_x_S | 302.1222 | 330.8240 | 395.3541 | 347.1689 | 370.4564 | 618.6347 |
2 | madDiff(x, idxs) | 466.7321 | 496.3730 | 546.1398 | 506.1658 | 522.6195 | 786.5121 |
3 | madDiff(x[idxs]) | 475.4246 | 500.2915 | 597.8230 | 516.8004 | 755.2356 | 793.2776 |
expr | min | lq | mean | median | uq | max | |
---|---|---|---|---|---|---|---|
1 | madDiff_x_S | 1.000000 | 1.000000 | 1.000000 | 1.000000 | 1.000000 | 1.000000 |
2 | madDiff(x, idxs) | 1.544846 | 1.500414 | 1.381394 | 1.457981 | 1.410745 | 1.271367 |
3 | madDiff(x[idxs]) | 1.573617 | 1.512259 | 1.512120 | 1.488614 | 2.038663 | 1.282304 |
Figure: Benchmarking of madDiff_x_S(), madDiff(x, idxs)() and madDiff(x[idxs])() on double+n = 10000000 data. Outliers are displayed as crosses. Times are in milliseconds.
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 4.82 mins.
To reproduce this report, do:
html <- matrixStats:::benchmark('madDiff_subset')
Copyright Dongcan Jiang. Last updated on 2019-09-10 21:03:27 (-0700 UTC). Powered by RSP.
<script> var link = document.createElement('link'); link.rel = 'icon'; link.href = "data:image/png;base64,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" document.getElementsByTagName('head')[0].appendChild(link); </script>