---
title: "3. Performance Benchmark with other packages"
output: rmarkdown::html_vignette
bibliography: performance.bib
vignette: >
%\VignetteIndexEntry{3. Performance Benchmark with other packages}
%\VignetteEngine{knitr::rmarkdown}
%\VignetteEncoding{UTF-8}
---
This vignette presents some performance tests ran between `fundiversity` and other functional diversity packages. Note that to avoid the dependency on other packages, this vignette is [**pre-computed**](https://ropensci.org/technotes/2019/12/08/precompute-vignettes/).
# Other packages to compute FD indices
## Main functions
Here is a table that summarizes the comparable functions (and their arguments) for functions included in `fundiversity`. Note that the package name is indicated before `::` followed by the function name.
| Index Type | Index Name | Source | `fundiversity` function | Equivalent Functions |
|:-----------------|:--------------------------------------------------|:------------------------------|:------------------------|----------------------|
| α-diversity | Functional Dispersion (FDis) | @Laliberte_distancebased_2010 | `fd_fdis()` | ~~`BAT::dispersion()`~~ (doesn't compute FDis)
`FD::fdisp()`
~~`hillR::hill_func()`~~ (actually rely on `FD` for FDis computations and standardize the traits without telling the user)
`mFD::alpha.fd.multidim(..., ind_vect = "fdis")` |
| α-diversity | Functional Divergence (FDiv) | @Villeger_New_2008 | `fd_fdiv()` | `mFD::alpha.fd.multidim(..., ind_vect = "fdiv")` |
| α-diversity | Functional Evenness (FEve) | @Villeger_New_2008 | `fd_feve()` | `mFD::alpha.fd.multidim(..., ind_vect = "feve")` |
| α-diversity | Functional Richness (FRic) | @Villeger_New_2008 | `fd_fric()` | `BAT::alpha()` (tree, not strictly equal)
`BAT::hull.alpha()` (hull)
`mFD::alpha.fd.multidim(..., ind_vect = "fric")` |
| α-diversity | Rao's Quadratic Entropy (Q) | @Villeger_Decomposing_2013 | `fd_raoq()` | `adiv::QE()`
`BAT::rao()`
`hillR::hill_func()` (standardize traits without warning)
`mFD::alpha.fd.hill(..., q = 2, tau = "max")` (returns a slightly modified version of Q according to @Ricotta_Diversity_2009) |
| β-diversity | Functional Richness Intersection (FRic_intersect) | @Rao_Diversity_1982 | `fd_fric_intersect()` | `betapart::functional.beta.pair()`
`hillR::hill_func_parti_pairwise()` |
The other packages are thus: `adiv` [@Pavoine_adiv_2020], `BAT` [@Cardoso_BAT_2015], `betapart` [@Baselga_betapart_2012], `FD` [@Laliberte_FD_2014], `hillR` [@Li_hillR_2018], and `mFD` [@Magneville_mFD_2022]. For fairness of comparison, even if `FD::dbFD()` contains most indices we're not considering it as it computes all indices together for each call, and would necessarily be slower.
## Benchmark between packages
We will now benchmark the functions included in `fundiversity` with the corresponding function in other packages using the `microbenchmark::microbenchmark()` function. We will use the fairly small (~220 species, 8 sites, 4 traits) provided dataset in `fundiversity`.
```r
tictoc::tic() # Time execution of vignette
library(fundiversity)
data("traits_birds", package = "fundiversity")
data("site_sp_birds", package = "fundiversity")
dist_traits_birds <- dist(traits_birds)
```
### Functional Dispersion (FDis)
```r
fdis_bench <- microbenchmark::microbenchmark(
fundiversity = {
fundiversity::fd_fdis(traits_birds, site_sp_birds)
},
FD = {
FD::fdisp(dist_traits_birds, site_sp_birds)
},
mFD = {
mFD::alpha.fd.multidim(
traits_birds, site_sp_birds, ind_vect = "fdis",
scaling = FALSE, verbose = FALSE, details_returned = FALSE
)
},
times = 30
)
ggplot2::autoplot(fdis_bench) +
labs(title = "Functional Dispersion (FDis)")
```
### Functional Divergence (FDiv)
```r
fdiv_bench <- microbenchmark::microbenchmark(
fundiversity = fd_fdiv(traits_birds, site_sp_birds),
mFD = mFD::alpha.fd.multidim(
traits_birds, site_sp_birds, ind_vect = "fdiv",
scaling = FALSE, verbose = FALSE
),
times = 30
)
ggplot2::autoplot(fdiv_bench) +
labs(title = "Functional Divergence (FDiv)")
```
### Functional Evenness (FEve)
```r
feve_bench <- microbenchmark::microbenchmark(
fundiversity = fd_feve(traits_birds, site_sp_birds),
mFD = mFD::alpha.fd.multidim(
traits_birds, site_sp_birds, ind_vect = "feve",
scaling = FALSE, verbose = FALSE
),
times = 30
)
ggplot2::autoplot(feve_bench) +
labs(title = "Functional Evenness (FEve)")
```
### Functional Richness (FRic)
```r
fric_bench <- microbenchmark::microbenchmark(
fundiversity = fd_fric(traits_birds, site_sp_birds),
BAT_tree = BAT::alpha(
site_sp_birds, traits_birds
),
BAT_alpha_hull = BAT::hull.alpha(
BAT::hull.build(site_sp_birds, traits_birds)
),
mFD = mFD::alpha.fd.multidim(
traits_birds, site_sp_birds, ind_vect = "fric",
scaling = FALSE, verbose = FALSE
),
times = 30
)
ggplot2::autoplot(fric_bench) +
labs(title = "Functional Richness (FRic)")
```
### Functional Richness Intersection (FRic_intersect)
```r
fric_bench <- microbenchmark::microbenchmark(
fundiversity = fd_fric_intersect(traits_birds, site_sp_birds) ,
betapart = betapart::functional.beta.pair(
site_sp_birds, traits_birds
),
hillR = hillR::hill_func_parti_pairwise(
site_sp_birds, traits_birds, .progress = FALSE
),
times = 30
)
ggplot2::autoplot(fric_bench) +
labs(title = "Functional Richness Intersection (FRic)")
```
### Rao's Quadratic Entropy (Q)
```r
raoq_bench <- fric_bench <- microbenchmark::microbenchmark(
fundiversity = fd_raoq(traits_birds, site_sp_birds),
adiv= adiv::QE(
site_sp_birds, dist_traits_birds
),
BAT_rao = BAT::rao(
site_sp_birds, distance = traits_birds
),
hillR_hill_func = hillR::hill_func(
site_sp_birds, traits_birds, fdis = FALSE
),
mFD_alpha_fd_hill = mFD::alpha.fd.hill(
site_sp_birds, dist_traits_birds, q = 2,
tau = "max"
),
times = 30
)
ggplot2::autoplot(raoq_bench) +
labs(title = "Rao's Quadatric Entropy (Q)")
```
# Benchmark within `fundiversity`
We now proceed to the performance evaluation of functions within `fundiversity` with datasets of increasing sizes.
## Increasing the number of species
```r
make_more_sp <- function(n) {
traits <- do.call(rbind, replicate(n, traits_birds, simplify = FALSE))
row.names(traits) <- paste0("sp", seq_len(nrow(traits)))
site_sp <- do.call(cbind, replicate(n, site_sp_birds, simplify = FALSE))
colnames(site_sp) <- paste0("sp", seq_len(ncol(site_sp)))
list(tr = traits, si = site_sp)
}
null_sp_1000 <- make_more_sp(5)
null_sp_10000 <- make_more_sp(50)
null_sp_100000 <- make_more_sp(500)
```
### Functional Richness
```r
bench_sp_fric <- microbenchmark::microbenchmark(
species_200 = fd_fric( traits_birds, site_sp_birds),
species_1000 = fd_fric( null_sp_1000$tr, null_sp_1000$si),
species_10000 = fd_fric( null_sp_10000$tr, null_sp_10000$si),
species_100000 = fd_fric(null_sp_100000$tr, null_sp_100000$si),
times = 30
)
ggplot2::autoplot(bench_sp_fric)
```
```r
bench_sp_fric
#> Unit: milliseconds
#> expr min lq mean median uq max neval cld
#> species_200 12.40461 12.86473 13.63794 13.65822 13.96949 15.64313 30 a
#> species_1000 23.31024 23.98235 24.77462 24.52898 24.99526 32.20389 30 b
#> species_10000 152.16972 153.69475 155.65197 154.41463 155.87235 167.39586 30 c
#> species_100000 1538.24210 1552.02784 1573.05390 1571.92017 1587.70159 1629.76043 30 d
```
### Functional Divergence
```r
bench_sp_fdiv <- microbenchmark::microbenchmark(
species_200 = fd_fdiv( traits_birds, site_sp_birds),
species_1000 = fd_fdiv( null_sp_1000$tr, null_sp_1000$si),
species_10000 = fd_fdiv( null_sp_10000$tr, null_sp_10000$si),
species_100000 = fd_fdiv(null_sp_100000$tr, null_sp_100000$si),
times = 30
)
ggplot2::autoplot(bench_sp_fdiv)
```
```r
bench_sp_fdiv
#> Unit: milliseconds
#> expr min lq mean median uq max neval cld
#> species_200 38.18281 39.33241 41.03782 39.80735 40.19474 63.38092 30 a
#> species_1000 52.21140 53.58856 54.88775 54.00482 54.58979 61.12244 30 a
#> species_10000 215.96505 218.21261 227.32449 221.46767 231.33971 262.64920 30 b
#> species_100000 1996.22764 2035.16809 2090.07993 2068.75306 2097.02749 2805.37816 30 c
```
### Rao's Quadratic Entropy
```r
bench_sp_raoq <- microbenchmark::microbenchmark(
species_200 = fd_raoq( traits_birds, site_sp_birds),
species_1000 = fd_raoq( null_sp_1000$tr, null_sp_1000$si),
species_10000 = fd_raoq( null_sp_10000$tr, null_sp_10000$si),
times = 30
)
ggplot2::autoplot(bench_sp_raoq)
```
```r
bench_sp_raoq
#> Unit: milliseconds
#> expr min lq mean median uq max neval cld
#> species_200 1.537703 1.751935 1.954434 1.950017 2.041037 3.230497 30 a
#> species_1000 31.957110 34.274255 39.128452 35.156704 37.556228 66.194976 30 a
#> species_10000 9890.756358 10163.144236 11121.366410 11055.979331 11964.694993 13632.668874 30 b
```
### Functional Evenness
```r
bench_sp_feve <- microbenchmark::microbenchmark(
species_200 = fd_feve( traits_birds, site_sp_birds),
species_1000 = fd_feve( null_sp_1000$tr, null_sp_1000$si),
species_10000 = fd_feve( null_sp_10000$tr, null_sp_10000$si),
times = 15
)
ggplot2::autoplot(bench_sp_feve)
```
```r
bench_sp_feve
#> Unit: milliseconds
#> expr min lq mean median uq max neval cld
#> species_200 22.25434 23.58857 26.28398 24.66652 25.41282 49.21613 15 a
#> species_1000 133.12192 148.39296 167.29508 163.98844 186.30949 212.94652 15 a
#> species_10000 26209.90991 27553.75296 28085.39492 28186.57578 28599.03918 29311.05335 15 b
```
### Comparing between indices
```r
all_bench_sp <- list(fric = bench_sp_fric,
fdiv = bench_sp_fdiv,
raoq = bench_sp_raoq,
feve = bench_sp_feve) %>%
bind_rows(.id = "fd_index") %>%
mutate(n_sp = gsub("species_", "", expr) %>%
as.numeric())
all_bench_sp %>%
ggplot(aes(n_sp, time * 1e-9, color = fd_index)) +
geom_point(alpha = 1/3) +
geom_smooth() +
scale_x_log10() +
scale_y_log10() +
scale_color_brewer(type = "qual",
labels = c(fric = "FRic", fdiv = "FDiv", raoq = "Rao's Q",
feve = "FEve")) +
labs(title = "Performance comparison between indices",
x = "# of species", y = "Time (in seconds)",
color = "FD index") +
theme_bw() +
theme(aspect.ratio = 1)
```
## Increasing the number of sites
```r
make_more_sites <- function(n) {
site_sp <- do.call(rbind, replicate(n, site_sp_birds, simplify = FALSE))
rownames(site_sp) <- paste0("s", seq_len(nrow(site_sp)))
site_sp
}
site_sp_100 <- make_more_sites(12)
site_sp_1000 <- make_more_sites(120)
site_sp_10000 <- make_more_sites(1200)
```
### Functional Richness
```r
bench_sites_fric <- microbenchmark::microbenchmark(
sites_10 = fd_fric(traits_birds, site_sp_birds),
sites_100 = fd_fric(traits_birds, site_sp_100),
sites_1000 = fd_fric(traits_birds, site_sp_1000),
sites_10000 = fd_fric(traits_birds, site_sp_10000),
times = 15
)
ggplot2::autoplot(bench_sites_fric)
```
```r
bench_sites_fric
#> Unit: milliseconds
#> expr min lq mean median uq max neval cld
#> sites_10 12.60195 13.45944 14.88054 13.96007 14.29602 21.66510 15 a
#> sites_100 45.57530 47.25923 49.87524 48.75620 52.29567 56.19952 15 b
#> sites_1000 372.72833 386.35628 400.43169 395.60903 408.99573 449.87164 15 c
#> sites_10000 3833.05136 3885.11306 3921.60544 3919.29055 3942.32492 4016.96576 15 d
```
### Functional Divergence
```r
bench_sites_fdiv <- microbenchmark::microbenchmark(
sites_10 = fd_fdiv(traits_birds, site_sp_birds),
sites_100 = fd_fdiv(traits_birds, site_sp_100),
sites_1000 = fd_fdiv(traits_birds, site_sp_1000),
sites_10000 = fd_fdiv(traits_birds, site_sp_10000),
times = 15
)
ggplot2::autoplot(bench_sites_fdiv)
```
```r
bench_sites_fdiv
#> Unit: milliseconds
#> expr min lq mean median uq max neval cld
#> sites_10 38.17040 39.17880 40.79484 39.59029 42.08936 45.43952 15 a
#> sites_100 85.09805 85.99399 94.77048 86.88365 96.33675 149.45931 15 b
#> sites_1000 538.76135 545.03628 582.84610 564.85683 605.87486 677.86382 15 c
#> sites_10000 5278.55172 5336.69296 5405.90861 5378.53730 5458.24597 5552.26594 15 d
```
### Rao's Quadratic Entropy
```r
bench_sites_raoq = microbenchmark::microbenchmark(
sites_10 = fd_raoq(traits = NULL, site_sp_birds, dist_traits_birds),
sites_100 = fd_raoq(traits = NULL, site_sp_100, dist_traits_birds),
sites_1000 = fd_raoq(traits = NULL, site_sp_1000, dist_traits_birds),
sites_10000 = fd_raoq(traits = NULL, site_sp_10000, dist_traits_birds),
times = 15
)
ggplot2::autoplot(bench_sites_raoq)
```
```r
bench_sites_raoq
#> Unit: milliseconds
#> expr min lq mean median uq max neval cld
#> sites_10 1.443757 1.717630 1.887153 1.861230 2.037991 2.518933 15 a
#> sites_100 5.689849 6.004428 6.718675 6.178415 6.460348 13.786219 15 a
#> sites_1000 158.130064 158.814848 160.907719 159.296631 163.977418 166.066294 15 a
#> sites_10000 16296.391504 17530.369693 18080.130808 18193.538354 18411.571843 21033.249202 15 b
```
### Functional Evenness
```r
bench_sites_feve <- microbenchmark::microbenchmark(
sites_10 = fd_feve(traits = NULL, site_sp_birds, dist_traits_birds),
sites_100 = fd_feve(traits = NULL, site_sp_100, dist_traits_birds),
sites_1000 = fd_feve(traits = NULL, site_sp_1000, dist_traits_birds),
sites_10000 = fd_feve(traits = NULL, site_sp_10000, dist_traits_birds),
times = 15
)
ggplot2::autoplot(bench_sites_feve)
```
```r
bench_sites_feve
#> Unit: milliseconds
#> expr min lq mean median uq max neval cld
#> sites_10 20.26885 20.79379 25.80265 21.06491 21.39182 90.92815 15 a
#> sites_100 113.64419 115.81009 123.60943 118.04145 123.54457 185.57582 15 a
#> sites_1000 1034.37573 1107.36408 1146.78358 1119.68000 1134.07348 1668.68480 15 b
#> sites_10000 10779.65515 10969.94333 11249.27062 11090.00824 11317.37080 12341.22800 15 c
```
### Comparing between indices
```r
all_bench_sites <- list(fric = bench_sites_fric,
fdiv = bench_sites_fdiv,
raoq = bench_sites_raoq,
feve = bench_sites_feve) %>%
bind_rows(.id = "fd_index") %>%
mutate(n_sites = gsub("sites", "", expr) %>%
as.numeric())
all_bench_sites %>%
ggplot(aes(n_sites, time * 1e-9, color = fd_index)) +
geom_point(alpha = 1/3) +
geom_smooth() +
scale_x_log10() +
scale_y_log10() +
scale_color_brewer(type = "qual",
labels = c(fric = "FRic", fdiv = "FDiv", raoq = "Rao's Q",
feve = "FEve")) +
labs(title = "Performance comparison between indices",
x = "# of sites", y = "Time (in seconds)",
color = "FD index") +
theme_bw() +
theme(aspect.ratio = 1)
```
Session info of the machine on which the benchmark was ran and time it took to run
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#>
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#> [5] /usr/lib/R/library
#>
#> ────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────
```
# References