Date: Tue, 12 May 2020 17:51:58 +0200 Subject: Update pkgdown --- docs/articles/web_only/benchmarks.html | 195 ++++++++++++++++++++++++++++----- 1 file changed, 165 insertions(+), 30 deletions(-) (limited to 'docs/articles/web_only') diff --git a/docs/articles/web_only/benchmarks.html b/docs/articles/web_only/benchmarks.html index b47df46f..b0bb5196 100644 --- a/docs/articles/web_only/benchmarks.html +++ b/docs/articles/web_only/benchmarks.html @@ -68,6 +68,9 @@

Example evaluation of NAFTA SOP Attachment examples

+ Some benchmark timings +

@@ -168,18 +171,18 @@ DFOP_par_c <- synthetic_data_for_UBA_2014[[12]]$data -t6 <- system.time(mmkin_bench(list(m_synth_SFO_lin), list(SFO_lin_a)))["elapsed"] -t7 <- system.time(mmkin_bench(list(m_synth_DFOP_par), list(DFOP_par_c)))["elapsed"] +t6 <- system.time(mmkin_bench(list(m_synth_SFO_lin), list(SFO_lin_a)))[["elapsed"]] +t7 <- system.time(mmkin_bench(list(m_synth_DFOP_par), list(DFOP_par_c)))[["elapsed"]] t8 <- system.time(mmkin_bench(list(m_synth_SFO_lin), list(SFO_lin_a), - error_model = "tc"))["elapsed"] + error_model = "tc"))[["elapsed"]] t9 <- system.time(mmkin_bench(list(m_synth_DFOP_par), list(DFOP_par_c), - error_model = "tc"))["elapsed"] + error_model = "tc"))[["elapsed"]] t10 <- system.time(mmkin_bench(list(m_synth_SFO_lin), list(SFO_lin_a), - error_model = "obs"))["elapsed"] + error_model = "obs"))[["elapsed"]] t11 <- system.time(mmkin_bench(list(m_synth_DFOP_par), list(DFOP_par_c), - error_model = "obs"))["elapsed"] + error_model = "obs"))[["elapsed"]]

mkin_benchmarks[system_string, paste0("t", 1:11)] <-
   c(t1, t2, t3, t4, t5, t6, t7, t8, t9, t10, t11)
 save(mkin_benchmarks, file = "~/git/mkin/vignettes/web_only/mkin_benchmarks.rda")

@@ -188,45 +191,177 @@

Results

Currently, we only have benchmark information on one system, therefore only the mkin version is shown with the results below. Timings are in seconds, shorter is better.

rownames(mkin_benchmarks) <- as.character(mkin_benchmarks$mkin)

Benchmarks for all available error models are shown.

Parent only

Constant variance and two-component error model:

print(mkin_benchmarks[, c("mkin", "t1", "t2")], row.names = FALSE)

##      mkin    t1     t2
-##  0.9.48.1 3.610 11.019
-##  0.9.49.1 8.184 22.889
-##  0.9.49.2 7.064 12.558
-##  0.9.49.3 7.296 21.239
-##  0.9.49.4 5.936 20.545
-##  0.9.50.2 1.697  3.895

kable(mkin_benchmarks[, c("t1", "t2")])

+ + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + +

	t1	t2
0.9.48.1	3.610	11.019
0.9.49.1	8.184	22.889
0.9.49.2	7.064	12.558
0.9.49.3	7.296	21.239
0.9.49.4	5.936	20.545
0.9.50.2	1.659	3.900

One metabolite

Constant variance, variance by variable and two-component error model:

print(mkin_benchmarks[, c("mkin", "t3", "t4", "t5")], row.names = FALSE)

##      mkin    t3     t4    t5
-##  0.9.48.1 3.764 14.347 9.495
-##  0.9.49.1 4.649 13.789 6.395
-##  0.9.49.2 4.786  8.461 5.675
-##  0.9.49.3 4.510 13.805 7.386
-##  0.9.49.4 4.446 15.335 6.002
-##  0.9.50.2 1.365  6.105 2.727

kable(mkin_benchmarks[, c("t3", "t4", "t5")])

+ + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + +

	t3	t4	t5
0.9.48.1	3.764	14.347	9.495
0.9.49.1	4.649	13.789	6.395
0.9.49.2	4.786	8.461	5.675
0.9.49.3	4.510	13.805	7.386
0.9.49.4	4.446	15.335	6.002
0.9.50.2	1.389	6.106	2.716

Two metabolites

Two different datasets, for each constant variance, variance by variable and two-component error model are shown:

print(mkin_benchmarks[, c("mkin", paste0("t", 6:11))], row.names = FALSE)

##      mkin    t6    t7    t8     t9   t10    t11
-##  0.9.48.1 2.623 4.587 7.525 16.621 8.576 31.267
-##  0.9.49.1 2.542 4.128 4.632  8.171 3.676  5.636
-##  0.9.49.2 2.723 4.478 4.862  7.618 3.579  5.574
-##  0.9.49.3 2.643 4.374  7.02 11.124 5.388  7.365
-##  0.9.49.4 2.635 4.259 4.737  7.763 3.427  5.626
-##  0.9.50.2 0.752 1.218 1.273  2.851 2.038  2.968

kable(mkin_benchmarks[, paste0("t", 6:11)])

+ + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + +

	t6	t7	t8	t9	t10	t11
0.9.48.1	2.623	4.587	7.525	16.621	8.576	31.267
0.9.49.1	2.542	4.128	4.632	8.171	3.676	5.636
0.9.49.2	2.723	4.478	4.862	7.618	3.579	5.574
0.9.49.3	2.643	4.374	7.020	11.124	5.388	7.365
0.9.49.4	2.635	4.259	4.737	7.763	3.427	5.626
0.9.50.2	0.751	1.209	1.270	2.859	2.026	2.963

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