madDiff

matrixStats: Benchmark report

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madDiff() benchmarks

This report benchmark the performance of madDiff() against alternative methods.

Alternative methods

• N/A

Data type "integer"

Data

> rvector <- function(n, mode = c("logical", "double", "integer"), range = c(-100, +100), naProb = 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 (naProb > 0) 
+         x[sample(n, size = naProb * 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

All elements

> x <- data[["n=1000"]]
> stats <- microbenchmark(madDiff = madDiff(x), mad = mad(x), diff = diff(x), unit = "ms")

Table: Benchmarking of madDiff(), mad() and diff() 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
3	diff	0.0320	0.0375	0.0541	0.0549	0.0602	0.1548
1	madDiff	0.1136	0.1272	0.2120	0.1879	0.2019	2.0433
2	mad	0.1632	0.1698	0.2458	0.2597	0.2820	0.5586

	expr	min	lq	mean	median	uq	max
3	diff	1.000	1.000	1.000	1.000	1.000	1.000
1	madDiff	3.554	3.390	3.917	3.425	3.351	13.204
2	mad	5.108	4.523	4.543	4.733	4.680	3.609
Figure: Benchmarking of madDiff(), mad() and diff() on integer+n=1000 data. Outliers are displayed as crosses. Times are in milliseconds.

n=10000 vector

All elements

> x <- data[["n=10000"]]
> stats <- microbenchmark(madDiff = madDiff(x), mad = mad(x), diff = diff(x), unit = "ms")

Table: Benchmarking of madDiff(), mad() and diff() 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
3	diff	0.2086	0.2131	0.2315	0.2171	0.2258	0.4215
1	madDiff	0.4804	0.4874	0.5105	0.4933	0.5018	0.8003
2	mad	0.8327	0.8404	0.8803	0.8450	0.8571	1.5783

	expr	min	lq	mean	median	uq	max
3	diff	1.000	1.000	1.000	1.000	1.000	1.000
1	madDiff	2.303	2.287	2.205	2.272	2.223	1.899
2	mad	3.991	3.944	3.802	3.892	3.796	3.744
Figure: Benchmarking of madDiff(), mad() and diff() on integer+n=10000 data. Outliers are displayed as crosses. Times are in milliseconds.

n=100000 vector

All elements

> x <- data[["n=100000"]]
> stats <- microbenchmark(madDiff = madDiff(x), mad = mad(x), diff = diff(x), unit = "ms")

Table: Benchmarking of madDiff(), mad() and diff() 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
3	diff	1.981	2.458	3.103	2.813	3.283	24.41
1	madDiff	3.914	4.623	5.566	5.062	5.952	23.02
2	mad	7.120	8.308	9.807	9.266	10.783	26.76

	expr	min	lq	mean	median	uq	max
3	diff	1.000	1.000	1.000	1.000	1.000	1.000
1	madDiff	1.976	1.881	1.794	1.800	1.813	0.943
2	mad	3.595	3.380	3.160	3.295	3.285	1.096
Figure: Benchmarking of madDiff(), mad() and diff() on integer+n=100000 data. Outliers are displayed as crosses. Times are in milliseconds.

n=1000000 vector

All elements

> x <- data[["n=1000000"]]
> stats <- microbenchmark(madDiff = madDiff(x), mad = mad(x), diff = diff(x), unit = "ms")

Table: Benchmarking of madDiff(), mad() and diff() 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
3	diff	20.45	25.31	30.39	27.47	34.82	58.20
1	madDiff	50.91	58.14	67.17	65.20	73.52	96.57
2	mad	78.32	88.83	101.15	97.60	110.25	151.38

	expr	min	lq	mean	median	uq	max
3	diff	1.000	1.000	1.000	1.000	1.000	1.000
1	madDiff	2.489	2.297	2.210	2.374	2.111	1.659
2	mad	3.829	3.510	3.328	3.554	3.166	2.601
Figure: Benchmarking of madDiff(), mad() and diff() 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), naProb = 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 (naProb > 0) 
+         x[sample(n, size = naProb * 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

All elements

> x <- data[["n=1000"]]
> stats <- microbenchmark(madDiff = madDiff(x), mad = mad(x), diff = diff(x), unit = "ms")

Table: Benchmarking of madDiff(), mad() and diff() 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
3	diff	0.0250	0.0293	0.0345	0.0312	0.0335	0.0793
1	madDiff	0.1621	0.1663	0.1898	0.1702	0.1875	0.4415
2	mad	0.1886	0.1921	0.2247	0.1948	0.2533	0.3707

	expr	min	lq	mean	median	uq	max
3	diff	1.000	1.000	1.000	1.000	1.000	1.000
1	madDiff	6.477	5.684	5.497	5.457	5.598	5.568
2	mad	7.538	6.566	6.508	6.247	7.563	4.675
Figure: Benchmarking of madDiff(), mad() and diff() on double+n=1000 data. Outliers are displayed as crosses. Times are in milliseconds.

n=10000 vector

All elements

> x <- data[["n=10000"]]
> stats <- microbenchmark(madDiff = madDiff(x), mad = mad(x), diff = diff(x), unit = "ms")

Table: Benchmarking of madDiff(), mad() and diff() 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
3	diff	0.1544	0.1821	0.2385	0.2466	0.2762	0.3391
2	mad	1.0725	1.4882	1.5265	1.5887	1.6630	2.1692
1	madDiff	1.2157	1.2800	1.7161	1.7614	1.8509	6.5377

	expr	min	lq	mean	median	uq	max
3	diff	1.000	1.000	1.000	1.000	1.000	1.000
2	mad	6.948	8.173	6.399	6.443	6.021	6.396
1	madDiff	7.875	7.030	7.194	7.144	6.701	19.277
Figure: Benchmarking of madDiff(), mad() and diff() on double+n=10000 data. Outliers are displayed as crosses. Times are in milliseconds.

n=100000 vector

All elements

> x <- data[["n=100000"]]
> stats <- microbenchmark(madDiff = madDiff(x), mad = mad(x), diff = diff(x), unit = "ms")

Table: Benchmarking of madDiff(), mad() and diff() 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
3	diff	1.554	2.167	2.905	2.635	3.408	19.64
1	madDiff	8.938	12.120	14.145	13.881	15.037	34.53
2	mad	11.645	14.376	17.339	17.292	18.811	48.10

	expr	min	lq	mean	median	uq	max
3	diff	1.000	1.000	1.000	1.000	1.000	1.000
1	madDiff	5.753	5.594	4.870	5.268	4.413	1.758
2	mad	7.495	6.636	5.969	6.562	5.520	2.449
Figure: Benchmarking of madDiff(), mad() and diff() on double+n=100000 data. Outliers are displayed as crosses. Times are in milliseconds.

n=1000000 vector

All elements

> x <- data[["n=1000000"]]
> stats <- microbenchmark(madDiff = madDiff(x), mad = mad(x), diff = diff(x), unit = "ms")

Table: Benchmarking of madDiff(), mad() and diff() 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
3	diff	23.46	27.85	33.77	30.45	33.92	73.98
2	mad	95.18	106.58	121.81	114.99	126.52	417.49
1	madDiff	98.11	110.41	124.72	120.01	131.82	405.31

	expr	min	lq	mean	median	uq	max
3	diff	1.000	1.000	1.000	1.000	1.000	1.000
2	mad	4.058	3.827	3.607	3.776	3.730	5.643
1	madDiff	4.183	3.965	3.693	3.941	3.886	5.478
Figure: Benchmarking of madDiff(), mad() and diff() on double+n=1000000 data. Outliers are displayed as crosses. Times are in milliseconds.

Appendix

Session information

R Under development (unstable) (2015-02-27 r67909)
Platform: x86_64-w64-mingw32/x64 (64-bit)
Running under: Windows 7 x64 (build 7601) Service Pack 1

locale:
[1] LC_COLLATE=English_United States.1252 
[2] LC_CTYPE=English_United States.1252   
[3] LC_MONETARY=English_United States.1252
[4] LC_NUMERIC=C                          
[5] LC_TIME=English_United States.1252    

attached base packages:
[1] stats     graphics  grDevices utils     datasets  methods   base     

other attached packages:
[1] markdown_0.7.7          microbenchmark_1.4-2    matrixStats_0.14.0-9000
[4] ggplot2_1.0.0           knitr_1.9.3             R.devices_2.13.0       
[7] R.utils_2.0.0           R.oo_1.19.0             R.methodsS3_1.7.0      

loaded via a namespace (and not attached):
 [1] Rcpp_0.11.4           GenomeInfoDb_1.3.13   formatR_1.0.3        
 [4] plyr_1.8.1            base64enc_0.1-3       tools_3.2.0          
 [7] digest_0.6.8          RSQLite_1.0.0         annotate_1.45.2      
[10] evaluate_0.5.7        gtable_0.1.2          R.cache_0.11.1-9000  
[13] lattice_0.20-30       DBI_0.3.1             parallel_3.2.0       
[16] mvtnorm_1.0-2         proto_0.3-10          R.rsp_0.20.0         
[19] genefilter_1.49.2     stringr_0.6.2         IRanges_2.1.41       
[22] S4Vectors_0.5.21      stats4_3.2.0          grid_3.2.0           
[25] Biobase_2.27.2        AnnotationDbi_1.29.17 XML_3.98-1.1         
[28] survival_2.38-1       multcomp_1.3-9        TH.data_1.0-6        
[31] reshape2_1.4.1        scales_0.2.4          MASS_7.3-39          
[34] splines_3.2.0         BiocGenerics_0.13.6   xtable_1.8-0         
[37] mime_0.2.1            colorspace_1.2-4      labeling_0.3         
[40] sandwich_2.3-2        munsell_0.4.2         Cairo_1.5-6          
[43] zoo_1.7-12           

Total processing time was 1.09 mins.

Reproducibility

To reproduce this report, do:

html <- matrixStats:::benchmark('madDiff')

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Copyright Henrik Bengtsson. Last updated on 2015-03-02 17:29:21 (-0800 UTC). Powered by RSP.

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