In daijiang/phyr: Model Based Phylogenetic Analysis

knitr::opts_chunk$set(
  collapse = TRUE,
  comment = "##",
  eval = F
)

In this file, we show the memory and time used for phyr::communityPGLMM(). It seems that for Gaussian models, use bobyqa as optimizer can save time and memory. But for Binomial models, the optimizer does not affect too much. Thus, may use Nelder-Mead for its robustness.

Also, note the hugh differences in memory usage between cpp = TRUE and cpp = FALSE.

First, let's prepare some toy data.

library(profvis)
library(dplyr, quietly = TRUE)
library(phyr)
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)
cov_ranef = list(sp = phylotree)

Then, we start the profvis.

Gaussian models

bobyqa with R

profvis({
  phyr::communityPGLMM(freq ~ 1 + shade + (1|sp__) + (1|site) + (1|sp@site), dat, 
                       cov_ranef = list(sp = phylotree), REML = TRUE, cpp = FALSE, optimizer = "bobyqa")
})

bobyqa with Rcpp

profvis({
  phyr::communityPGLMM(freq ~ 1 + shade + (1|sp__) + (1|site) + (1|sp@site), dat, 
                       cov_ranef = list(sp = phylotree), REML = TRUE, cpp = TRUE, optimizer = "bobyqa")
})

Nelder-Mead with R

profvis({
  phyr::communityPGLMM(freq ~ 1 + shade + (1|sp__) + (1|site) + (1|sp@site), dat, 
                       cov_ranef = list(sp = phylotree), REML = TRUE, cpp = FALSE, optimizer = "Nelder-Mead")
})

Nelder-Mead with Rcpp

profvis({
  phyr::communityPGLMM(freq ~ 1 + shade + (1|sp__) + (1|site) + (1|sp@site), dat, 
                       cov_ranef = list(sp = phylotree), REML = TRUE, cpp = TRUE, optimizer = "Nelder-Mead")
})

Binomial models

bobyqa with R

profvis({
  phyr::communityPGLMM(pa ~ 1 + shade + (1|sp__) + (1|site) + (1|sp@site), dat, 
                       family = "binomial", cov_ranef = list(sp = phylotree), REML = TRUE, cpp = FALSE, 
                       optimizer = "bobyqa")
})

bobyqa with Rcpp

profvis({
  phyr::communityPGLMM(pa ~ 1 + shade + (1|sp__) + (1|site) + (1|sp@site), dat, 
                       family = "binomial", cov_ranef = list(sp = phylotree), REML = TRUE, cpp = TRUE, 
                       optimizer = "bobyqa")
})

Nelder-Mead with R

profvis({
  phyr::communityPGLMM(pa ~ 1 + shade + (1|sp__) + (1|site) + (1|sp@site), dat, 
                       family = "binomial", cov_ranef = list(sp = phylotree), REML = TRUE, cpp = FALSE, 
                       optimizer = "Nelder-Mead")
})

Nelder-Mead with Rcpp

profvis({
  phyr::communityPGLMM(pa ~ 1 + shade + (1|sp__) + (1|site) + (1|sp@site), dat, 
                       family = "binomial", cov_ranef = list(sp = phylotree), REML = TRUE, cpp = TRUE, 
                       optimizer = "Nelder-Mead")
})

PSV

psv with R

profvis({
  psv(comm_sim, tree_sim, cpp = FALSE)
})

psv with Rcpp

profvis({
  x <- psv(comm_sim, tree_sim, cpp = TRUE)
})

microbenchmark::microbenchmark(psv(comm, tree, cpp = FALSE),
                               psv(comm, tree, cpp = TRUE), times = 100)

microbenchmark::microbenchmark(pse(comm, tree, cpp = FALSE),
                               pse(comm, tree, cpp = TRUE), times = 100)

phy = ape::rtree(n = 10000)
profvis({
  ape::vcv(phy, corr = TRUE)
})
profvis({
  vcv2(phy, corr = TRUE)
})

daijiang/phyr documentation built on Feb. 25, 2024, 3:01 a.m.