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psvlibrary(phyr)
# simulate data
nspp = 500
nsite = 100
tree_sim = ape::rtree(n = nspp)
comm_sim = matrix(rbinom(nspp * nsite, size = 1, prob = 0.6),
nrow = nsite, ncol = nspp)
row.names(comm_sim) = paste0("site_", 1:nsite)
colnames(comm_sim) = paste0("t", 1:nspp)
comm_sim = comm_sim[, tree_sim$tip.label]
# about 40 times faster
rbenchmark::benchmark(
"picante" = {picante::psv(comm_sim, tree_sim)},
"phyr R" = {phyr::psv(comm_sim, tree_sim, cpp = FALSE)},
"phyr c++" = {phyr::psv(comm_sim, tree_sim, cpp = TRUE)},
replications = 10,
columns = c("test", "replications", "elapsed",
"relative", "user.self", "sys.self"))
#> test replications elapsed relative user.self sys.self
#> 3 phyr c++ 10 0.339 1.000 0.298 0.030
#> 2 phyr R 10 3.287 9.696 2.907 0.303
#> 1 picante 10 16.265 47.979 14.824 0.795psecomm_sim = matrix(rpois(nspp * nsite, 3), nrow = nsite, ncol = nspp)
row.names(comm_sim) = paste0("site_", 1:nsite)
colnames(comm_sim) = paste0("t", 1:nspp)
comm_sim = comm_sim[, tree_sim$tip.label]
# about 2-3 times faster
rbenchmark::benchmark(
"picante" = {picante::pse(comm_sim, tree_sim)},
"phyr R" = {phyr::pse(comm_sim, tree_sim, cpp = FALSE)},
"phyr c++" = {phyr::pse(comm_sim, tree_sim, cpp = TRUE)},
replications = 20,
columns = c("test", "replications", "elapsed",
"relative", "user.self", "sys.self"))
#> test replications elapsed relative user.self sys.self
#> 3 phyr c++ 20 1.456 1.000 1.329 0.105
#> 2 phyr R 20 4.233 2.907 3.453 0.555
#> 1 picante 20 3.858 2.650 3.319 0.475pcd# pcd is about 20 times faster
rbenchmark::benchmark(
"phyr" = {phyr::pcd(comm = comm_a, tree = phylotree, reps = 1000, verbose = FALSE)},
"picante" = {picante::pcd(comm = comm_a, tree = phylotree, reps = 1000)},
replications = 10,
columns = c("test", "replications", "elapsed",
"relative", "user.self", "sys.self"))
#> test replications elapsed relative user.self sys.self
#> 1 phyr 10 0.214 1.000 0.192 0.012
#> 2 picante 10 4.516 21.103 4.043 0.074pglmm)To compare the cpp version and R version, and the version from the pez package.
library(dplyr)
comm = comm_a
comm$site = row.names(comm)
dat = tidyr::gather(comm, key = "sp", value = "freq", -site) %>%
left_join(envi, by = "site") %>%
left_join(traits, by = "sp")
dat$pa = as.numeric(dat$freq > 0)
# data prep for pez::communityPGLMM, not necessary for phyr::pglmm
dat = arrange(dat, site, sp)
dat = filter(dat, sp %in% sample(unique(dat$sp), 10),
site %in% sample(unique(dat$site), 5))
nspp = n_distinct(dat$sp)
nsite = n_distinct(dat$site)
dat$site = as.factor(dat$site)
dat$sp = as.factor(dat$sp)
tree = ape::drop.tip(phylotree, setdiff(phylotree$tip.label, unique(dat$sp)))
Vphy <- ape::vcv(tree)
Vphy <- Vphy/max(Vphy)
Vphy <- Vphy/exp(determinant(Vphy)$modulus[1]/nspp)
Vphy = Vphy[levels(dat$sp), levels(dat$sp)]
# prepare random effects
re.site <- list(1, site = dat$site, covar = diag(nsite))
re.sp <- list(1, sp = dat$sp, covar = diag(nspp))
re.sp.phy <- list(1, sp = dat$sp, covar = Vphy)
# sp is nested within site
re.nested.phy <- list(1, sp = dat$sp, covar = Vphy, site = dat$site)
re.nested.rep <- list(1, sp = dat$sp, covar = solve(Vphy), site = dat$site) # equal to sp__@site
# can be named
re = list(re.sp = re.sp, re.sp.phy = re.sp.phy, re.nested.phy = re.nested.phy, re.site = re.site)
# about 4-10 times faster for a small dataset
rbenchmark::benchmark(
"phyr c++ bobyqa" = {phyr::pglmm(freq ~ 1 + shade + (1|sp__) + (1|site) + (1|sp__@site),
dat, cov_ranef = list(sp = phylotree), REML = FALSE,
cpp = TRUE, optimizer = "bobyqa")},
"phyr c++ Nelder-Mead" = {phyr::pglmm(freq ~ 1 + shade + (1|sp__) + (1|site) + (1|sp__@site),
dat, cov_ranef = list(sp = phylotree), REML = FALSE,
cpp = TRUE, optimizer = "Nelder-Mead")},
"phyr R Nelder-Mead" = {phyr::pglmm(freq ~ 1 + shade + (1|sp__) + (1|site) + (1|sp__@site),
dat, cov_ranef = list(sp = phylotree), REML = FALSE,
cpp = FALSE, optimizer = "Nelder-Mead")},
"pez R Nelder-Mead" = {pez::communityPGLMM(freq ~ 1 + shade, data = dat, sp = dat$sp, site = dat$site,
random.effects = re, REML = FALSE)},
replications = 5,
columns = c("test", "replications", "elapsed",
"relative", "user.self", "sys.self")
)
#> test replications elapsed relative user.self sys.self
#> 4 pez R Nelder-Mead 5 32.214 88.989 30.821 0.374
#> 1 phyr c++ bobyqa 5 0.362 1.000 0.342 0.006
#> 2 phyr c++ Nelder-Mead 5 1.156 3.193 1.115 0.015
#> 3 phyr R Nelder-Mead 5 33.281 91.936 31.198 0.480
# about 6 times faster for a small dataset
rbenchmark::benchmark(
"phyr c++ bobyqa" = {phyr::pglmm(pa ~ 1 + shade + (1|sp__) + (1|site) + (1|sp__@site),
dat, family = "binomial", cov_ranef = list(sp = phylotree), REML = FALSE,
cpp = TRUE, optimizer = "bobyqa")},
"phyr c++ Nelder-Mead" = {phyr::pglmm(pa ~ 1 + shade + (1|sp__) + (1|site) + (1|sp__@site),
dat, family = "binomial", cov_ranef = list(sp = phylotree), REML = FALSE,
cpp = TRUE, optimizer = "Nelder-Mead")},
"phyr R Nelder-Mead" = {phyr::pglmm(pa ~ 1 + shade + (1|sp__) + (1|site) + (1|sp__@site),
dat, family = "binomial", cov_ranef = list(sp = phylotree), REML = FALSE,
cpp = FALSE, optimizer = "Nelder-Mead")},
"pez R Nelder-Mead" = {pez::communityPGLMM(pa ~ 1 + shade, data = dat, sp = dat$sp,
family = "binomial", site = dat$site,
random.effects = re, REML = FALSE)},
replications = 5,
columns = c("test", "replications", "elapsed",
"relative", "user.self", "sys.self")
)
#> test replications elapsed relative user.self sys.self
#> 4 pez R Nelder-Mead 5 49.296 42.314 45.731 0.604
#> 1 phyr c++ bobyqa 5 1.840 1.579 1.750 0.033
#> 2 phyr c++ Nelder-Mead 5 1.165 1.000 1.093 0.021
#> 3 phyr R Nelder-Mead 5 24.355 20.906 23.024 0.317cor_phylo()library(ape)
# Set up parameter values for simulating data
n <- 50
phy <- rcoal(n, tip.label = 1:n)
trt_names <- paste0("par", 1:2)
R <- matrix(c(1, 0.7, 0.7, 1), nrow = 2, ncol = 2)
d <- c(0.3, 0.95)
B2 <- 1
Se <- c(0.2, 1)
M <- matrix(Se, nrow = n, ncol = 2, byrow = TRUE)
colnames(M) <- trt_names
# Set up needed matrices for the simulations
p <- length(d)
star <- stree(n)
star$edge.length <- array(1, dim = c(n, 1))
star$tip.label <- phy$tip.label
Vphy <- vcv(phy)
Vphy <- Vphy/max(Vphy)
Vphy <- Vphy/exp(determinant(Vphy)$modulus[1]/n)
tau <- matrix(1, nrow = n, ncol = 1) %*% diag(Vphy) - Vphy
C <- matrix(0, nrow = p * n, ncol = p * n)
for (i in 1:p) for (j in 1:p) {
Cd <- (d[i]^tau * (d[j]^t(tau)) * (1 - (d[i] * d[j])^Vphy))/(1 - d[i] * d[j])
C[(n * (i - 1) + 1):(i * n), (n * (j - 1) + 1):(j * n)] <- R[i, j] * Cd
}
MM <- matrix(M^2, ncol = 1)
V <- C + diag(as.numeric(MM))
iD <- t(chol(V))
XX <- iD %*% rnorm(2 * n)
X <- matrix(XX, n, p)
colnames(X) <- trt_names
rownames(X) <- phy$tip.label
rownames(M) <- phy$tip.label
U <- list(cbind(rnorm(n, mean = 2, sd = 10)))
names(U) <- trt_names[2]
X[,2] <- X[,2] + B2[1] * U[[1]][,1] - B2[1] * mean(U[[1]][,1])
z <- cor_phylo(variates = X,
covariates = U,
meas_errors = M,
phy = phy,
species = phy$tip.label)
U2 <- list(NULL, matrix(rnorm(n, mean = 2, sd = 10), nrow = n, ncol = 1))
rownames(U2[[2]]) <- phy$tip.label
colnames(U2[[2]]) <- "par2"
X2 = X
X2[,2] <- X2[,2] + B2[1] * U2[[2]][,1] - B2[1] * mean(U2[[2]][,1])
z_r <- corphylo(X = X2, SeM = M, U = U2, phy = phy, method = "Nelder-Mead")
rbenchmark::benchmark(
"cor_phylo" = {cor_phylo(variates = X, covariates = U, meas_errors = M,
phy = phy, species = phy$tip.label)},
"corphylo" = {corphylo(X = X2, SeM = M, U = U2, phy = phy, method = "Nelder-Mead")},
replications = 5,
columns = c("test", "replications", "elapsed",
"relative", "user.self", "sys.self")
)
#> test replications elapsed relative user.self sys.self
#> 1 cor_phylo 5 4.511 1.000 4.329 0.062
#> 2 corphylo 5 16.190 3.589 13.863 1.369These binaries (installable software) and packages are in development.
They may not be fully stable and should be used with caution. We make no claims about them.