Code
# options to customize chunk outputs
knitr::opts_chunk$set(
tidy.opts = list(width.cutoff = 65),
tidy = TRUE,
message = FALSE
)Parrot vocal repertoire evolution
Statistical analysis
Code
# print link to github repo if any
if (file.exists("./.git/config")){
config <- readLines("./.git/config")
url <- grep("url", config, value = TRUE)
url <- gsub("\\turl = |.git$", "", url)
cat("\nSource code and data found at [", url, "](", url, ")", sep = "")
}1 Custom functions
Code
brms_summary <- function(...) {
extended_summary(..., highlight = TRUE, remove.intercepts = TRUE,
trace.palette = viridis::mako, fill = "#54C9ADFF", gsub.pattern = "b_treatment|b_",
gsub.replacement = "")
}Purpose
- Determine the factors shaping call repertoire size in parrots
Load packages
Code
# install sketchy package if not installed
if (!requireNamespace("sketchy", quietly = TRUE)) {
install.packages("sketchy")
}
# knitr is require for creating html/pdf/word reports formatR is
# used for soft-wrapping code
pkgs <- c("ape", "bayesplot", "brms", "dagitty", "devtools", "dplyr",
"fitdistrplus", "ggcorrplot", "ggdag", "ggplot2", "gridExtra",
"Hmisc", "loo", "pdftools", "phylobase", "phylolm", "phylosignal",
"phytools", "MCMCglmm", "nFactors", "reshape2", "rio", "tidyverse",
github = "maRce10/brmsish", "viridis", github = "stan-dev/cmdstanr")
# install/ load packages
sketchy::load_packages(packages = pkgs)Warning: replacing previous import 'brms::rstudent_t' by 'ggdist::rstudent_t'
when loading 'brmsish'Warning: replacing previous import 'brms::dstudent_t' by 'ggdist::dstudent_t'
when loading 'brmsish'Warning: replacing previous import 'brms::qstudent_t' by 'ggdist::qstudent_t'
when loading 'brmsish'Warning: replacing previous import 'brms::pstudent_t' by 'ggdist::pstudent_t'
when loading 'brmsish'2 Format data
Code
BStree <- read.tree("./data/raw/08112025_BrianSmith.treefile")
trait <- read.csv("./data/raw/ALLTRAITS.MCMCglmm.csv", header = T)
# Align tree and trait data
justthetip <- intersect(BStree$tip.label, trait$gs)
BStree <- drop.tip(BStree, setdiff(BStree$tip.label, justthetip))
trait <- trait[trait$gs %in% justthetip, ]
trait$flock <- trait$Log.max.flocksize
# rename and prepare table for brms
trait <- trait %>%
rename(phylo = "gs", repertoire = "sp") %>%
mutate_at(c("cs", "diet", "hd"), as.factor) %>%
mutate_at(c("brain", "color", "flock", "longevity", "mass"), ~(scale(.) %>%
as.vector)) #added flock for median scaling
trait$species <- trait$phylo
A <- ape::vcv.phylo(BStree)3 Define DAG
Code
PCVEdag <- dagify(longevity ~ mass, repertoire ~ brain + cs + flock +
hd + color + diet + longevity, color ~ repertoire + diet + flock +
hd + mass + cs, cs ~ flock, brain ~ longevity + mass)
ggdag(PCVEdag) + theme_dag_blank()
4 Regression models
4.1 Brain
Code
#------------------------modeling iterations
# no phylo BRAIN brainnophylo <- brm( formula = repertoire ~
# brain + longevity + mass, data = trait, chains = 4, cores = 4)
# plot(brainnophylo) summary(brainnophylo) rm(brainnophylo) no
# phylo BRAIN student brainnophylo2 <- brm( formula = repertoire
# ~ brain + longevity + mass, data = trait, family = student(),
# chains = 4, cores = 4) plot(brainnophylo2)
# summary(brainnophylo2) pp_check(brainnophylo2, ndraw = 100)
# plot(loo(brainnophylo2)) y = as.numeric(trait$repertoire) yrep
# = posterior_predict(brainnophylo2, draws = 500)
# loo_brainnophylo = loo(brainnophylo2, save_psis = T)
# ppc_loo_pit_overlay(yrep = yrep, y = y, lw =
# weights(loo_brainnophylo$psis_object)) + yaxis_text()
# rm(brainnophylo2)
#---------------------brain
adjustmentSets(x = PCVEdag, outcome = "repertoire", exposure = "brain",
effect = "direct") #(color, longevity) | (longevity, mass) -> (longevity) this is updated after correlation plot{ longevity, mass }
Code
# brain
brain_reg <- brm(formula = repertoire ~ brain + longevity + mass +
(1 | gr(phylo, cov = A)) + (1 | species), data = trait, data2 = list(A = A),
iter = 6000, family = poisson(link = "log"), prior = c(prior(normal(0,
10), "b"), prior(normal(0, 50), "Intercept"), prior(student_t(3,
0, 20), "sd")), control = list(adapt_delta = 0.99, max_treedepth = 15),
file = "./data/processed/brain_reg.rds")
# print results
brms_summary(brain_reg)4.2 brain_reg
| priors | formula | iterations | chains | thinning | warmup | diverg_transitions | rhats > 1.05 | min_bulk_ESS | min_tail_ESS | seed | |
|---|---|---|---|---|---|---|---|---|---|---|---|
| 1 | b-normal(0, 10) Intercept-normal(0, 50) sd-student_t(3, 0, 20) | repertoire ~ brain + longevity + mass + (1 | gr(phylo, cov = A)) + (1 | species) | 6000 | 4 | 1 | 3000 | 0 (0%) | 0 | 7498.014 | 8152.458 | 507281327 |
| Estimate | l-95% CI | u-95% CI | Rhat | Bulk_ESS | Tail_ESS | |
|---|---|---|---|---|---|---|
| brain | 0.394 | -0.338 | 1.135 | 1 | 7545.506 | 8677.625 |
| longevity | 0.165 | -0.087 | 0.410 | 1.001 | 8741.012 | 8380.815 |
| mass | -0.299 | -0.994 | 0.409 | 1 | 7498.014 | 8152.458 |
4.3 Color
Code
adjustmentSets(x = PCVEdag, outcome = "repertoire", exposure = "color",
effect = "direct") #(brain, longevity) | (mass) -> () after update no relation
color_reg <- brm(formula = repertoire ~ color + (1 | gr(phylo, cov = A)) +
(1 | species), data = trait, data2 = list(A = A), iter = 6000,
family = poisson(link = "log"), prior = c(prior(normal(0, 10),
"b"), prior(normal(0, 50), "Intercept"), prior(student_t(3,
0, 20), "sd")), control = list(adapt_delta = 0.99, max_treedepth = 15),
file = "./data/processed/color_reg.rds")
# print results
brms_summary(color_reg)4.4 color_reg
| priors | formula | iterations | chains | thinning | warmup | diverg_transitions | rhats > 1.05 | min_bulk_ESS | min_tail_ESS | seed | |
|---|---|---|---|---|---|---|---|---|---|---|---|
| 1 | b-normal(0, 10) Intercept-normal(0, 50) sd-student_t(3, 0, 20) | repertoire ~ color + (1 | gr(phylo, cov = A)) + (1 | species) | 6000 | 4 | 1 | 3000 | 0 (0%) | 0 | 3497.595 | 5748.972 | 281316726 |
| Estimate | l-95% CI | u-95% CI | Rhat | Bulk_ESS | Tail_ESS | |
|---|---|---|---|---|---|---|
| color | -0.027 | -0.305 | 0.291 | 1.001 | 3497.595 | 5748.972 |
4.5 CS
Code
adjustmentSets(x = PCVEdag, outcome = "repertoire", exposure = "cs",
effect = "direct") #no adjustments
cs_reg <- brm(formula = repertoire ~ cs + (1 | gr(phylo, cov = A)) +
(1 | species), data = trait, data2 = list(A = A), iter = 6000,
family = poisson(link = "log"), prior = c(prior(normal(0, 10),
"b"), prior(normal(0, 50), "Intercept"), prior(student_t(3,
0, 20), "sd")), control = list(adapt_delta = 0.99, max_treedepth = 15),
file = "./data/processed/cs_reg.rds")
# print results
brms_summary(cs_reg)4.6 cs_reg
| priors | formula | iterations | chains | thinning | warmup | diverg_transitions | rhats > 1.05 | min_bulk_ESS | min_tail_ESS | seed | |
|---|---|---|---|---|---|---|---|---|---|---|---|
| 1 | b-normal(0, 10) Intercept-normal(0, 50) sd-student_t(3, 0, 20) | repertoire ~ cs + (1 | gr(phylo, cov = A)) + (1 | species) | 6000 | 4 | 1 | 3000 | 0 (0%) | 0 | 7578.427 | 8436.599 | 1208203268 |
| Estimate | l-95% CI | u-95% CI | Rhat | Bulk_ESS | Tail_ESS | |
|---|---|---|---|---|---|---|
| cs1 | -0.151 | -0.687 | 0.378 | 1 | 7578.427 | 8436.599 |
4.7 Diet
Code
adjustmentSets(x = PCVEdag, outcome = "repertoire", exposure = "diet",
effect = "direct") #(flock, hd) -> () after PCVE update
diet_reg <- brm(formula = repertoire ~ diet + flock + hd + (1 | gr(phylo,
cov = A)) + (1 | species), data = trait, data2 = list(A = A),
iter = 6000, family = poisson(link = "log"), prior = c(prior(normal(0,
10), "b"), prior(normal(0, 50), "Intercept"), prior(student_t(3,
0, 20), "sd")), control = list(adapt_delta = 0.99, max_treedepth = 15),
file = "./data/processed/diet_reg.rds")
# print results
brms_summary(diet_reg)4.8 diet_reg
| priors | formula | iterations | chains | thinning | warmup | diverg_transitions | rhats > 1.05 | min_bulk_ESS | min_tail_ESS | seed | |
|---|---|---|---|---|---|---|---|---|---|---|---|
| 1 | b-normal(0, 10) Intercept-normal(0, 50) sd-student_t(3, 0, 20) | repertoire ~ diet + flock + hd + (1 | gr(phylo, cov = A)) + (1 | species) | 6000 | 4 | 1 | 3000 | 0 (0%) | 0 | 6669.096 | 7320.289 | 941500483 |
| Estimate | l-95% CI | u-95% CI | Rhat | Bulk_ESS | Tail_ESS | |
|---|---|---|---|---|---|---|
| diet1 | 0.124 | -0.346 | 0.591 | 1 | 8144.172 | 7956.631 |
| flock | 0.091 | -0.105 | 0.289 | 1 | 7687.610 | 8376.275 |
| hd2 | -0.257 | -0.670 | 0.152 | 1 | 6669.096 | 7320.289 |
| hd3 | -0.148 | -0.839 | 0.531 | 1.001 | 7719.408 | 8238.211 |
4.9 Flock
Code
adjustmentSets(x = PCVEdag, outcome = "repertoire", exposure = "flock",
effect = "direct") #(diet) -> () after PCVE Adjustments
flock_reg <- brm(formula = repertoire ~ flock + (1 | gr(phylo, cov = A)) +
(1 | species), data = trait, data2 = list(A = A), iter = 6000,
family = poisson(link = "log"), prior = c(prior(normal(0, 10),
"b"), prior(normal(0, 50), "Intercept"), prior(student_t(3,
0, 20), "sd")), control = list(adapt_delta = 0.99, max_treedepth = 15),
file = "./data/processed/flock_reg.rds")
# print results
brms_summary(flock_reg)4.10 flock_reg
| priors | formula | iterations | chains | thinning | warmup | diverg_transitions | rhats > 1.05 | min_bulk_ESS | min_tail_ESS | seed | |
|---|---|---|---|---|---|---|---|---|---|---|---|
| 1 | b-normal(0, 10) Intercept-normal(0, 50) sd-student_t(3, 0, 20) | repertoire ~ flock + (1 | gr(phylo, cov = A)) + (1 | species) | 6000 | 4 | 1 | 3000 | 0 (0%) | 0 | 4507.975 | 7125.182 | 1939336726 |
| Estimate | l-95% CI | u-95% CI | Rhat | Bulk_ESS | Tail_ESS | |
|---|---|---|---|---|---|---|
| flock | 0.059 | -0.133 | 0.25 | 1.001 | 4507.975 | 7125.182 |
5 HD
Code
adjustmentSets(x= PCVEdag, outcome = "repertoire", exposure = "hd", effect = "direct") #(diet) -> () after PCVE adjustment
hd_reg <- brm(
formula = repertoire ~ hd + diet + (1|gr(phylo, cov = A)) + (1|species),
data = trait,
data2 = list(A = A),
iter = 6000,
family = poisson(link = "log"),
prior = c(
prior(normal(0, 10), "b"),
prior(normal(0, 50), "Intercept"),
prior(student_t(3, 0, 20), "sd")),
control = list(adapt_delta = 0.99, max_treedepth = 15),
file = "./data/processed/hd_reg.rds"
)
# print results
brms_summary(hd_reg)5.1 hd_reg
| priors | formula | iterations | chains | thinning | warmup | diverg_transitions | rhats > 1.05 | min_bulk_ESS | min_tail_ESS | seed | |
|---|---|---|---|---|---|---|---|---|---|---|---|
| 1 | b-normal(0, 10) Intercept-normal(0, 50) sd-student_t(3, 0, 20) | repertoire ~ hd + diet + (1 | gr(phylo, cov = A)) + (1 | species) | 6000 | 4 | 1 | 3000 | 3 (0.00025%) | 0 | 6891.073 | 7777.737 | 1337805895 |
| Estimate | l-95% CI | u-95% CI | Rhat | Bulk_ESS | Tail_ESS | |
|---|---|---|---|---|---|---|
| hd2 | -0.212 | -0.606 | 0.188 | 1 | 6891.073 | 7923.589 |
| hd3 | -0.072 | -0.731 | 0.570 | 1 | 8165.947 | 7891.109 |
| diet1 | 0.119 | -0.347 | 0.598 | 1 | 7078.525 | 7777.737 |
5.2 Longevity
Code
adjustmentSets(x = PCVEdag, outcome = "repertoire", exposure = "longevity",
effect = "direct") #(brain, color) | (brain, mass) -> (brain) after DAG update{ brain, mass }
Code
long_reg <- brm(formula = repertoire ~ longevity + brain + (1 | gr(phylo,
cov = A)) + (1 | species), data = trait, data2 = list(A = A),
iter = 6000, family = poisson(link = "log"), prior = c(prior(normal(0,
10), "b"), prior(normal(0, 50), "Intercept"), prior(student_t(3,
0, 20), "sd")), control = list(adapt_delta = 0.99, max_treedepth = 15),
file = "./data/processed/long_reg.rds")
# print results
brms_summary(long_reg)5.3 long_reg
| priors | formula | iterations | chains | thinning | warmup | diverg_transitions | rhats > 1.05 | min_bulk_ESS | min_tail_ESS | seed | |
|---|---|---|---|---|---|---|---|---|---|---|---|
| 1 | b-normal(0, 10) Intercept-normal(0, 50) sd-student_t(3, 0, 20) | repertoire ~ longevity + brain + (1 | gr(phylo, cov = A)) + (1 | species) | 6000 | 4 | 1 | 3000 | 0 (0%) | 0 | 5908.964 | 7766.633 | 761246686 |
| Estimate | l-95% CI | u-95% CI | Rhat | Bulk_ESS | Tail_ESS | |
|---|---|---|---|---|---|---|
| longevity | 0.141 | -0.102 | 0.384 | 1 | 7653.167 | 7766.633 |
| brain | 0.103 | -0.157 | 0.365 | 1.001 | 5908.964 | 7894.479 |
5.4 Mass
Code
adjustmentSets(x = PCVEdag, outcome = "repertoire", exposure = "mass",
effect = "direct") #(brain, mass, longevity) -> (brain, longevity) after DAG update
mass_reg <- brm(formula = repertoire ~ mass + brain + longevity +
(1 | gr(phylo, cov = A)) + (1 | species), data = trait, data2 = list(A = A),
iter = 6000, family = poisson(link = "log"), prior = c(prior(normal(0,
10), "b"), prior(normal(0, 50), "Intercept"), prior(student_t(3,
0, 20), "sd")), control = list(adapt_delta = 0.99, max_treedepth = 15),
file = "./data/processed/mass_reg.rds")
# print results
brms_summary(mass_reg)5.5 mass_reg
| priors | formula | iterations | chains | thinning | warmup | diverg_transitions | rhats > 1.05 | min_bulk_ESS | min_tail_ESS | seed | |
|---|---|---|---|---|---|---|---|---|---|---|---|
| 1 | b-normal(0, 10) Intercept-normal(0, 50) sd-student_t(3, 0, 20) | repertoire ~ mass + brain + longevity + (1 | gr(phylo, cov = A)) + (1 | species) | 6000 | 4 | 1 | 3000 | 0 (0%) | 0 | 6322.832 | 7561.451 | 124221717 |
| Estimate | l-95% CI | u-95% CI | Rhat | Bulk_ESS | Tail_ESS | |
|---|---|---|---|---|---|---|
| mass | -0.298 | -1.008 | 0.400 | 1 | 6924.930 | 7934.820 |
| brain | 0.390 | -0.343 | 1.140 | 1.001 | 6322.832 | 7561.451 |
| longevity | 0.167 | -0.078 | 0.419 | 1 | 8715.648 | 8550.563 |
6 Phylogenetic path analysis
Based on this example code
Code
# set individual models
long_mod <- bf(longevity ~ mass + (1 | gr(phylo, cov = A)) + (1 |
species))
rep_mod <- bf(repertoire ~ brain + cs + flock + hd + color + diet +
longevity + (1 | gr(phylo, cov = A)) + (1 | species), family = poisson(link = "log"))
col_mod <- bf(color ~ repertoire + diet + flock + hd + mass + cs +
(1 | gr(phylo, cov = A)) + (1 | species))
cs_mod <- bf(cs ~ flock + (1 | gr(phylo, cov = A)) + (1 | species),
family = bernoulli())
br_mod <- bf(brain ~ longevity + mass + (1 | gr(phylo, cov = A)) +
(1 | species))
# fit path analysis
sem_brms <- brm(long_mod + rep_mod + col_mod + cs_mod + br_mod + set_rescor(FALSE),
data = trait, data2 = list(A = A), iter = 20000, cores = 4, chains = 4,
backend = "cmdstanr", prior = c(prior(normal(0, 10), "b"), prior(normal(0,
50), "Intercept")), control = list(adapt_delta = 0.99, max_treedepth = 15),
file = "./data/processed/path_analysis.rds", file_refit = "on_change")
# print results
brms_summary(sem_brms)6.1 sem_brms
| priors | formula | iterations | chains | thinning | warmup | diverg_transitions | rhats > 1.05 | min_bulk_ESS | min_tail_ESS | seed | |
|---|---|---|---|---|---|---|---|---|---|---|---|
| 1 | b-normal(0, 10) Intercept-normal(0, 50) Intercept-student_t(3, 0.1, 2.5) Intercept-student_t(3, 0.5, 2.5) Intercept-student_t(3, 0, 2.5) Intercept-student_t(3, -0.1, 2.5) Intercept-student_t(3, 2.5, 2.5) sd-student_t(3, 0, 2.5) sd-student_t(3, 0, 2.5) sd-student_t(3, 0, 2.5) sd-student_t(3, 0, 2.5) sd-student_t(3, 0, 2.5) sigma-student_t(3, 0, 2.5) sigma-student_t(3, 0, 2.5) sigma-student_t(3, 0, 2.5) | list(longevity = list(formula = longevity ~ mass + (1 | gr(phylo, cov = A)) + (1 | species), pforms = list(), pfix = list(), resp = “longevity”, family = list(family = “gaussian”, link = “identity”, linkfun = function (mu) link(mu, link = slink), linkinv = function (eta) inv_link(eta, link = slink), dpars = c(“mu”, “sigma”), type = “real”, ybounds = c(-Inf, Inf), closed = c(NA, NA), ad = c(“weights”, “subset”, “se”, “cens”, “trunc”, “mi”, “index”), normalized = c(“_time_hom”, “_time_het”, “_lagsar”, “_errorsar”, “_fcor”), specials = c(“residuals”, “rescor”)), mecor = TRUE), repertoire = list(formula = repertoire ~ brain + cs + flock + hd + color + diet + longevity + (1 | gr(phylo, cov = A)) + (1 | species), pforms = list(), pfix = list(), family = list(family = “poisson”, link = “log”, linkfun = function (mu) link(mu, link = slink), linkinv = function (eta) inv_link(eta, link = slink), dpars = “mu”, type = “int”, ybounds = c(0, Inf), closed = c(TRUE, NA), ad = c(“weights”, “subset”, “cens”, “trunc”, “rate”, “index”), specials = “sbi_log”), resp = “repertoire”, mecor = TRUE), color = list(formula = color ~ repertoire + diet + flock + hd + mass + cs + (1 | gr(phylo, cov = A)) + (1 | species), pforms = list(), pfix = list(), resp = “color”, family = list(family = “gaussian”, link = “identity”, linkfun = function (mu) link(mu, link = slink), linkinv = function (eta) inv_link(eta, link = slink), dpars = c(“mu”, “sigma”), type = “real”, ybounds = c(-Inf, Inf), closed = c(NA, NA), ad = c(“weights”, “subset”, “se”, “cens”, “trunc”, “mi”, “index”), normalized = c(“_time_hom”, “_time_het”, “_lagsar”, “_errorsar”, “_fcor”), specials = c(“residuals”, “rescor”)), mecor = TRUE), cs = list(formula = cs ~ flock + (1 | gr(phylo, cov = A)) + (1 | species), pforms = list(), pfix = list(), family = list(family = “bernoulli”, link = “logit”, linkfun = function (mu) link(mu, link = slink), linkinv = function (eta) inv_link(eta, link = slink), dpars = “mu”, type = “int”, ybounds = c(0, 1), closed = c(TRUE, TRUE), ad = c(“weights”, “subset”, “index”), specials = c(“binary”, “sbi_logit”)), resp = “cs”, mecor = TRUE), brain = list(formula = brain ~ longevity + mass + (1 | gr(phylo, cov = A)) + (1 | species), pforms = list(), pfix = list(), resp = “brain”, family = list(family = “gaussian”, link = “identity”, linkfun = function (mu) link(mu, link = slink), linkinv = function (eta) inv_link(eta, link = slink), dpars = c(“mu”, “sigma”), type = “real”, ybounds = c(-Inf, Inf), closed = c(NA, NA), ad = c(“weights”, “subset”, “se”, “cens”, “trunc”, “mi”, “index”), normalized = c(“_time_hom”, “_time_het”, “_lagsar”, “_errorsar”, “_fcor”), specials = c(“residuals”, “rescor”)), mecor = TRUE)) | 20000 | 4 | 1 | 10000 | 0 (0%) | 24 | 4.293 | 10.731 | 1171876634 |
| Estimate | l-95% CI | u-95% CI | Rhat | Bulk_ESS | Tail_ESS | |
|---|---|---|---|---|---|---|
| longevity_Intercept | -0.259 | -0.429 | -0.038 | 3.179 | 4.456 | 11.074 |
| repertoire_Intercept | 2.270 | 1.836 | 2.505 | 3.539 | 4.397 | 11.269 |
| color_Intercept | 0.129 | -0.035 | 0.482 | 3.671 | 4.333 | 11.203 |
| cs_Intercept | -3.027 | -8.027 | -0.787 | 3.809 | 4.312 | 11.312 |
| brain_Intercept | -0.114 | -0.287 | -0.030 | 3.51 | 4.375 | 11.043 |
| longevity_mass | 0.692 | 0.594 | 0.845 | 3.004 | 4.528 | 15.249 |
| repertoire_brain | 0.155 | -0.003 | 0.267 | 4.023 | 4.293 | 10.731 |
| repertoire_cs1 | -0.075 | -0.236 | 0.135 | 3.132 | 4.543 | 12.864 |
| repertoire_flock | 0.061 | -0.006 | 0.088 | 3.751 | 4.323 | 12.454 |
| repertoire_hd2 | -0.039 | -0.158 | 0.100 | 3.031 | 4.511 | 15.052 |
| repertoire_hd3 | 0.097 | 0.031 | 0.197 | 3.473 | 4.393 | 11.313 |
| repertoire_color | 0.020 | -0.040 | 0.087 | 2.623 | 4.717 | 11.255 |
| repertoire_diet1 | 0.044 | -0.097 | 0.167 | 3.433 | 4.394 | 11.357 |
| repertoire_longevity | 0.141 | -0.002 | 0.288 | 3.609 | 4.364 | 12.704 |
| color_repertoire | 0.000 | 0.000 | 0.000 | 2.127 | 18.560 | 15.229 |
| color_diet1 | 0.033 | -0.213 | 0.134 | 2.97 | 4.529 | 19.013 |
| color_flock | -0.151 | -0.227 | -0.097 | 3.478 | 4.421 | 11.403 |
| color_hd2 | 0.080 | -0.077 | 0.229 | 3.195 | 4.738 | 16.614 |
| color_hd3 | 0.150 | -0.387 | 0.559 | 3.309 | 4.431 | 13.184 |
| color_mass | -0.073 | -0.139 | 0.026 | 3.247 | 4.462 | 12.639 |
| color_cs1 | -0.342 | -0.497 | -0.182 | 3.445 | 4.389 | 15.105 |
| cs_flock | -2.297 | -4.554 | -1.336 | 3.036 | 4.521 | 27.377 |
| brain_longevity | 0.008 | -0.058 | 0.102 | 3.776 | 4.319 | 12.605 |
| brain_mass | 0.888 | 0.854 | 0.927 | 2.886 | 4.568 | 24.635 |
Takeaways
Session information
Click to see
─ Session info ───────────────────────────────────────────────────────────────
setting value
version R version 4.5.0 (2025-04-11)
os Ubuntu 22.04.5 LTS
system x86_64, linux-gnu
ui X11
language (EN)
collate en_US.UTF-8
ctype en_US.UTF-8
tz America/Costa_Rica
date 2025-08-26
pandoc 3.2 @ /usr/lib/rstudio/resources/app/bin/quarto/bin/tools/x86_64/ (via rmarkdown)
quarto 1.7.31 @ /usr/local/bin/quarto
─ Packages ───────────────────────────────────────────────────────────────────
package * version date (UTC) lib source
abind 1.4-8 2024-09-12 [1] CRAN (R 4.5.0)
ade4 1.7-23 2025-02-14 [1] CRAN (R 4.5.0)
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