matrixStats.benchmarks
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 5338373 285.1 7916910 422.9 7916910 422.9
Vcells 10912022 83.3 33191153 253.3 53339345 407.0
> 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 5337341 285.1 7916910 422.9 7916910 422.9
Vcells 10909019 83.3 33191153 253.3 53339345 407.0
> 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 | 0.058618 | 0.0649075 | 0.066852 | 0.0656505 | 0.0663995 | 0.155059 |
| 1 | colWeightedMedians | 0.069945 | 0.0793620 | 0.080739 | 0.0805175 | 0.0817440 | 0.114797 |
| expr | min | lq | mean | median | uq | max | |
|---|---|---|---|---|---|---|---|
| 2 | apply+weigthedMedian | 1.000000 | 1.000000 | 1.000000 | 1.000000 | 1.000000 | 1.000000 |
| 1 | colWeightedMedians | 1.193234 | 1.222694 | 1.207728 | 1.226457 | 1.231094 | 0.740344 |
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 | 0.057798 | 0.0626545 | 0.0657444 | 0.0651835 | 0.0657595 | 0.149053 |
| 1 | rowWeightedMedians | 0.071850 | 0.0772715 | 0.0803987 | 0.0796075 | 0.0811340 | 0.113690 |
| expr | min | lq | mean | median | uq | max | |
|---|---|---|---|---|---|---|---|
| 2 | apply+weigthedMedian | 1.000000 | 1.000000 | 1.000000 | 1.000000 | 1.000000 | 1.0000000 |
| 1 | rowWeightedMedians | 1.243123 | 1.233295 | 1.222897 | 1.221283 | 1.233799 | 0.7627488 |
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 | 71.850 | 77.2715 | 80.39870 | 79.6075 | 81.134 | 113.690 |
| 1 | colWeightedMedians | 69.945 | 79.3620 | 80.73899 | 80.5175 | 81.744 | 114.797 |
| expr | min | lq | mean | median | uq | max | |
|---|---|---|---|---|---|---|---|
| 2 | rowWeightedMedians | 1.0000000 | 1.000000 | 1.000000 | 1.000000 | 1.000000 | 1.000000 |
| 1 | colWeightedMedians | 0.9734864 | 1.027054 | 1.004233 | 1.011431 | 1.007518 | 1.009737 |
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 5335902 285.0 7916910 422.9 7916910 422.9
Vcells 10523851 80.3 33191153 253.3 53339345 407.0
> 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 5335896 285.0 7916910 422.9 7916910 422.9
Vcells 10533894 80.4 33191153 253.3 53339345 407.0
> 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 | 0.666541 | 0.6816655 | 0.7668511 | 0.7063770 | 0.8279125 | 1.268180 |
| 1 | colWeightedMedians | 0.723570 | 0.7469600 | 0.8814077 | 0.8105625 | 0.9538670 | 1.617107 |
| expr | min | lq | mean | median | uq | max | |
|---|---|---|---|---|---|---|---|
| 2 | apply+weigthedMedian | 1.00000 | 1.000000 | 1.000000 | 1.000000 | 1.000000 | 1.00000 |
| 1 | colWeightedMedians | 1.08556 | 1.095787 | 1.149386 | 1.147493 | 1.152135 | 1.27514 |
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 | 0.662943 | 0.6722950 | 0.7338995 | 0.679953 | 0.737635 | 1.254176 |
| 1 | rowWeightedMedians | 0.721027 | 0.7318395 | 0.8291074 | 0.743418 | 0.885740 | 1.276855 |
| expr | min | lq | mean | median | uq | max | |
|---|---|---|---|---|---|---|---|
| 2 | apply+weigthedMedian | 1.000000 | 1.000000 | 1.000000 | 1.000000 | 1.000000 | 1.000000 |
| 1 | rowWeightedMedians | 1.087615 | 1.088569 | 1.129729 | 1.093337 | 1.200784 | 1.018083 |
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 | 721.027 | 731.8395 | 829.1074 | 743.4180 | 885.740 | 1276.855 |
| 1 | colWeightedMedians | 723.570 | 746.9600 | 881.4077 | 810.5625 | 953.867 | 1617.107 |
| expr | min | lq | mean | median | uq | max | |
|---|---|---|---|---|---|---|---|
| 2 | rowWeightedMedians | 1.000000 | 1.000000 | 1.00000 | 1.000000 | 1.000000 | 1.000000 |
| 1 | colWeightedMedians | 1.003527 | 1.020661 | 1.06308 | 1.090319 | 1.076915 | 1.266477 |
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 5336627 285.1 7916910 422.9 7916910 422.9
Vcells 10528265 80.4 33191153 253.3 53339345 407.0
> 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 5336615 285.1 7916910 422.9 7916910 422.9
Vcells 10538298 80.5 33191153 253.3 53339345 407.0
> 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 | 0.558770 | 0.6110875 | 0.6854559 | 0.684275 | 0.7321420 | 0.964007 |
| 1 | colWeightedMedians | 0.574385 | 0.6210990 | 0.7070122 | 0.699900 | 0.7542415 | 1.130088 |
| expr | min | lq | mean | median | uq | max | |
|---|---|---|---|---|---|---|---|
| 2 | apply+weigthedMedian | 1.000000 | 1.000000 | 1.000000 | 1.000000 | 1.000000 | 1.000000 |
| 1 | colWeightedMedians | 1.027945 | 1.016383 | 1.031448 | 1.022834 | 1.030185 | 1.172282 |
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 | 0.557595 | 0.5607195 | 0.6269260 | 0.5652330 | 0.6619825 | 0.937099 |
| 1 | rowWeightedMedians | 0.575496 | 0.5814740 | 0.6520714 | 0.5866085 | 0.7192995 | 1.051900 |
| expr | min | lq | mean | median | uq | max | |
|---|---|---|---|---|---|---|---|
| 2 | apply+weigthedMedian | 1.000000 | 1.000000 | 1.000000 | 1.000000 | 1.000000 | 1.000000 |
| 1 | rowWeightedMedians | 1.032104 | 1.037014 | 1.040109 | 1.037817 | 1.086584 | 1.122507 |
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 | 575.496 | 581.474 | 652.0714 | 586.6085 | 719.2995 | 1051.900 |
| 1 | colWeightedMedians | 574.385 | 621.099 | 707.0122 | 699.9000 | 754.2415 | 1130.088 |
| expr | min | lq | mean | median | uq | max | |
|---|---|---|---|---|---|---|---|
| 2 | rowWeightedMedians | 1.0000000 | 1.000000 | 1.000000 | 1.00000 | 1.000000 | 1.00000 |
| 1 | colWeightedMedians | 0.9980695 | 1.068146 | 1.084256 | 1.19313 | 1.048578 | 1.07433 |
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 5336819 285.1 7916910 422.9 7916910 422.9
Vcells 10528074 80.4 33191153 253.3 53339345 407.0
> 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 5336813 285.1 7916910 422.9 7916910 422.9
Vcells 10538117 80.4 33191153 253.3 53339345 407.0
> 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 | 2.638999 | 2.811646 | 3.145791 | 2.922781 | 3.055063 | 9.457205 |
| 1 | colWeightedMedians | 3.171310 | 3.395751 | 3.607023 | 3.513560 | 3.689402 | 5.259681 |
| expr | min | lq | mean | median | uq | max | |
|---|---|---|---|---|---|---|---|
| 2 | apply+weigthedMedian | 1.000000 | 1.000000 | 1.000000 | 1.000000 | 1.000000 | 1.000000 |
| 1 | colWeightedMedians | 1.201709 | 1.207745 | 1.146619 | 1.202129 | 1.207636 | 0.556156 |
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 | 2.632101 | 2.873942 | 3.086753 | 2.957736 | 3.065488 | 9.281187 |
| 1 | rowWeightedMedians | 3.181512 | 3.416350 | 3.716918 | 3.491299 | 3.641147 | 10.598747 |
| expr | min | lq | mean | median | uq | max | |
|---|---|---|---|---|---|---|---|
| 2 | apply+weigthedMedian | 1.000000 | 1.000000 | 1.000000 | 1.000000 | 1.000000 | 1.00000 |
| 1 | rowWeightedMedians | 1.208735 | 1.188733 | 1.204151 | 1.180396 | 1.187787 | 1.14196 |
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 | 3.181512 | 3.416350 | 3.716918 | 3.491299 | 3.641147 | 10.598747 |
| 1 | colWeightedMedians | 3.171310 | 3.395751 | 3.607023 | 3.513560 | 3.689402 | 5.259681 |
| expr | min | lq | mean | median | uq | max | |
|---|---|---|---|---|---|---|---|
| 2 | rowWeightedMedians | 1.0000000 | 1.0000000 | 1.0000000 | 1.000000 | 1.000000 | 1.000000 |
| 1 | colWeightedMedians | 0.9967933 | 0.9939703 | 0.9704339 | 1.006376 | 1.013253 | 0.496255 |
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 5337009 285.1 7916910 422.9 7916910 422.9
Vcells 10528669 80.4 33191153 253.3 53339345 407.0
> 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 5337003 285.1 7916910 422.9 7916910 422.9
Vcells 10628712 81.1 33191153 253.3 53339345 407.0
> 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 | 6.520031 | 6.851844 | 7.541683 | 7.009149 | 7.251044 | 24.63903 |
| 1 | colWeightedMedians | 7.072074 | 7.387505 | 7.805397 | 7.489342 | 7.701308 | 24.78461 |
| expr | min | lq | mean | median | uq | max | |
|---|---|---|---|---|---|---|---|
| 2 | apply+weigthedMedian | 1.000000 | 1.000000 | 1.000000 | 1.00000 | 1.000000 | 1.000000 |
| 1 | colWeightedMedians | 1.084669 | 1.078178 | 1.034968 | 1.06851 | 1.062096 | 1.005908 |
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 | 6.601629 | 6.941376 | 7.357366 | 7.084605 | 7.260865 | 22.26618 |
| 1 | rowWeightedMedians | 7.124855 | 7.522170 | 8.146047 | 7.674877 | 7.864806 | 25.21561 |
| expr | min | lq | mean | median | uq | max | |
|---|---|---|---|---|---|---|---|
| 2 | apply+weigthedMedian | 1.000000 | 1.000000 | 1.000000 | 1.000000 | 1.000000 | 1.000000 |
| 1 | rowWeightedMedians | 1.079257 | 1.083671 | 1.107196 | 1.083318 | 1.083178 | 1.132462 |
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 | |
|---|---|---|---|---|---|---|---|
| 1 | colWeightedMedians | 7.072074 | 7.387505 | 7.805397 | 7.489342 | 7.701308 | 24.78461 |
| 2 | rowWeightedMedians | 7.124855 | 7.522170 | 8.146047 | 7.674877 | 7.864806 | 25.21561 |
| expr | min | lq | mean | median | uq | max | |
|---|---|---|---|---|---|---|---|
| 1 | colWeightedMedians | 1.000000 | 1.000000 | 1.000000 | 1.000000 | 1.00000 | 1.00000 |
| 2 | rowWeightedMedians | 1.007463 | 1.018229 | 1.043643 | 1.024773 | 1.02123 | 1.01739 |
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 5337196 285.1 7916910 422.9 7916910 422.9
Vcells 10530235 80.4 33191153 253.3 53339345 407.0
> 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 5337190 285.1 7916910 422.9 7916910 422.9
Vcells 10630278 81.2 33191153 253.3 53339345 407.0
> 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 | 5.402375 | 5.643393 | 5.845511 | 5.670750 | 5.723949 | 15.49405 |
| 1 | colWeightedMedians | 5.484320 | 5.708277 | 5.979079 | 5.750507 | 5.839806 | 14.18323 |
| expr | min | lq | mean | median | uq | max | |
|---|---|---|---|---|---|---|---|
| 2 | apply+weigthedMedian | 1.000000 | 1.000000 | 1.00000 | 1.000000 | 1.000000 | 1.0000000 |
| 1 | colWeightedMedians | 1.015168 | 1.011497 | 1.02285 | 1.014065 | 1.020241 | 0.9153984 |
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 | 5.409707 | 5.660353 | 5.975777 | 5.687883 | 5.770017 | 15.738789 |
| 1 | rowWeightedMedians | 5.479955 | 5.721429 | 5.949951 | 5.765739 | 5.817060 | 9.323769 |
| expr | min | lq | mean | median | uq | max | |
|---|---|---|---|---|---|---|---|
| 2 | apply+weigthedMedian | 1.000000 | 1.00000 | 1.0000000 | 1.000000 | 1.000000 | 1.000000 |
| 1 | rowWeightedMedians | 1.012986 | 1.01079 | 0.9956781 | 1.013688 | 1.008153 | 0.592407 |
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 | 5.484320 | 5.708277 | 5.979079 | 5.750507 | 5.839806 | 14.183225 |
| 2 | rowWeightedMedians | 5.479955 | 5.721429 | 5.949951 | 5.765739 | 5.817060 | 9.323769 |
| expr | min | lq | mean | median | uq | max | |
|---|---|---|---|---|---|---|---|
| 1 | colWeightedMedians | 1.0000000 | 1.000000 | 1.0000000 | 1.000000 | 1.0000000 | 1.00000 |
| 2 | rowWeightedMedians | 0.9992041 | 1.002304 | 0.9951283 | 1.002649 | 0.9961049 | 0.65738 |
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 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
[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 19.7 secs.
Reproducibility
To reproduce this report, do:
html <- matrixStats:::benchmark('colWeightedMedians')
Copyright Henrik Bengtsson. Last updated on 2021-08-25 22:32:51 (+0200 UTC). Powered by RSP.