LBH song type prevalence and degradation
Source code and data found at https://github.com/maRce10/lbh_songtype_prevalence_and_degradation
# knitr is require for creating html/pdf/word reports formatR is
# used for soft-wrapping code
# install/ load packages
sketchy::load_packages(packages = c("knitr", "formatR", "rprojroot",
github = "maRce10/warbleR", github = "maRce10/baRulho", "readxl",
"Rraven", "ggplot2", "brms", "viridis", "corrplot", "DT", "ape",
"phangorn", "ggtree", "phytools", "ratematrix", "geomorph", "evolvability",
"geiger", "ggdist", "gghalves"))trees <- read.tree("/home/m/Dropbox/Projects/lbh_cultural_evolution_revBayes/output/most_recent_revbayes_models/SUR_prank_old_early_global_219792.trees")
# mcct <- mcc(x = tre)
tree_sty <- unique(unlist(lapply(trees, "[", "tip.label")))
ntips_trees <- sapply(trees, Ntip)
tree <- trees[[which.max(ntips_trees)]]
# #Highlight and label clade ggtree(tree, layout = 'fan', size =
# 2) + geom_nodelab(col= 'red', hjust = 2) + geom_tiplab(size=3,
# offset=0.1)
sur_dat <- read.csv("./data/raw/allsongsSUR.csv")
sur_dat <- sur_dat[grep("marker|measu", sur_dat$song.type, invert = TRUE),
]
sur_dat$song.type.year <- paste(sur_dat$song.type, sur_dat$year, sep = "-")
# unique(sur_dat$song.type.year)
sur_dat <- sur_dat[sur_dat$song.type.year %in% tree_sty, ]
sty <- unique(sur_dat$song.type.year)
trees <- lapply(trees, function(x) drop.tip(x, tip = setdiff(x$tip.label,
sty)))
# unique(unlist(lapply(trees, '[', 'tip.label')))
ntips_trees <- sapply(trees, Ntip)
tree <- trees[[which.max(ntips_trees)]]
ggtree(tree, layout = "fan", size = 2) + geom_nodelab(col = "red",
hjust = 2) + geom_tiplab(size = 3, offset = 0.1)
1 2 3
13 2 18 spec_features <- spectro_analysis(est, wl = 200, ovlp = 90)
spec_features$song.type.year <- est$song.type.year
spec_features$org.sound.files <- est$sound.files
spec_features$SNR <- est$SNR
# read fundamental freq contour data fund_contours <-
# read.csv('./data/raw/seltailor_output.csv') cs <-
# check_sels(fund_contours, fix.selec = T, path = './data/raw')
# fund_contours <- fund_contours[cs$check.res == 'OK',]
# contour_id <- imp_raven(path = './data/raw/', files =
# '44kKz_songtype_segment_analysis_sep_2019.txt', warbler.format
# = TRUE, all.data = TRUE) contour_id$song.type.year <-
# paste(contour_id$lek.song.type, contour_id$year, sep = '-')
# contour_id$song.type.year.sfs <-
# paste(contour_id$lek.song.type, contour_id$year,
# contour_id$sf.selec, sep = '-')
# all(table(contour_id$song.type.year.sfs) == 20) spid <-
# song_param(contour_id[, c(1:6, ncol(contour_id))], song_colm =
# 'song.type.year.sfs') spid$song.type.year <-
# sapply(spid$song.type.year.sfs, function(x)
# contour_id$song.type.year[contour_id$song.type.year.sfs ==
# x][1]) colms <- c('sound.files', 'start', 'end', 'selec',
# 'song.type.year.sfs', 'song.type.year')
# fund_contours$song.type.year <-
# sapply(seq_len(nrow(fund_contours)), function(x){ Y <-
# fund_contours[x, c('sound.files', 'start', 'end', 'selec')] Z
# <- spid[, c('sound.files', 'start', 'end', 'selec',
# 'song.type.year.sfs', 'song.type.year')] Y$song.type.year.sfs
# <- NA Y$song.type.year.sfs <-
# as.character(Y$song.type.year.sfs) Y$song.type.year <- NA
# Y$song.type.year <- as.character(Y$song.type.year) Y$source <-
# 'contours' Z$source <- 'IDs' X <- rbind(Y, Z) ovlp <-
# overlapping_sels(X, indx.row = TRUE, pb = FALSE, verbose =
# FALSE) sty <-
# ovlp$song.type.year[!is.na(ovlp$song.type.year.sfs) &
# !is.na(ovlp$indx.row)] return(sty) }) spec_features$song.type
# <- substr(spec_features$song.type.year, 0, 6)
# fund_contours$song.type <-
# substr(fund_contours$song.type.year, 0, 6)
# spec_features$meanfun <- sapply(spec_features$song.type,
# function(x){ X <- fund_contours[fund_contours$song.type == x,
# ] meanfun <- mean(unlist(X[,grep('ff', names(X))]))
# return(meanfun) }) spec_features$minfun <-
# sapply(spec_features$song.type, function(x){ X <-
# fund_contours[fund_contours$song.type == x, ] minfun <-
# min(unlist(X[,grep('ff', names(X))])) return(minfun) })features <- c("meandom", "mindom", "maxdom", "dfrange", "sp.ent",
"modindx", "duration", "skew", "kurt", "time.ent", "time.IQR",
"meanpeakf")
spec_features <- spec_features[, c("sound.files", "selec", "song.type.year",
"org.sound.files", "SNR", features)]
mean_spec_features <- aggregate(x = . ~ song.type.year, spec_features[,
c("song.type.year", features)], mean)
# add standard error
se_spec_features <- aggregate(x = . ~ song.type.year, spec_features[,
c("song.type.year", features)], function(x) sd(x)/sqrt(length(x)))
for (i in features) se_spec_features[is.na(se_spec_features[, i]),
i] <- mean(se_spec_features[, i], na.rm = TRUE)
out <- lapply(features, function(x) {
trait <- mean_spec_features[, x]
# trait_se <- se_spec_features[, x]
# names(trait_se) <-
names(trait) <- mean_spec_features$song.type.year
ps <- phylosig(tree = tree, x = trait, method = "lambda", test = TRUE)
out_df <- data.frame(feature = x, as.data.frame(ps[1:4]))
return(out_df)
})
phylosig_df <- do.call(rbind, out)
phylosig_df$lambda <- round(phylosig_df$lambda, 2)
phylosig_df[order(phylosig_df$lambda), ]| feature | lambda | logL | logL0 | P | |
|---|---|---|---|---|---|
| 3 | maxdom | 0.00 | -49.87925 | -49.87867 | 1.0000000 |
| 4 | dfrange | 0.00 | -55.89417 | -55.89400 | 1.0000000 |
| 6 | modindx | 0.00 | -77.84348 | -77.84286 | 1.0000000 |
| 12 | meanpeakf | 0.00 | -56.19731 | -56.19693 | 1.0000000 |
| 7 | duration | 0.16 | 90.97903 | 90.48411 | 0.3197809 |
| 8 | skew | 0.24 | -39.79507 | -40.01873 | 0.5036099 |
| 2 | mindom | 0.25 | -45.85286 | -46.79134 | 0.1706793 |
| 10 | time.ent | 0.26 | 145.65136 | 144.92847 | 0.2292064 |
| 5 | sp.ent | 0.37 | 67.33066 | 65.10486 | 0.0348684 |
| 9 | kurt | 0.42 | -109.40088 | -109.55856 | 0.5744056 |
| 11 | time.IQR | 0.44 | 116.85830 | 116.03394 | 0.1991345 |
| 1 | meandom | 0.66 | -44.64417 | -47.08116 | 0.0272644 |
sub_features <- c(Modulation = "modindx", `Max dominant` = "maxdom",
`Frequency range` = "dfrange", `Mean frequency` = "meandom", `Time IQR` = "time.IQR",
Kurtosis = "kurt")
cols <- viridis(10)[c(3, 7)]
par(mfcol = c(3, 2))
for (i in seq_along(sub_features)) {
feat <- mean_spec_features[, sub_features[i]]
feat <- scale(feat)
names(feat) <- mean_spec_features$song.type.year
phenogram(tree, x = feat, spread.labels = F, offset = 0, ftype = "off",
colors = ifelse(i < 4, cols[1], cols[2]), ylim = c(-2.4, 2.4))
title(names(sub_features)[i], cex.main = 2)
}
library(mvMORPH)
mean_spec_features_matrix <- as.matrix(mean_spec_features[, 2:ncol(mean_spec_features)])
rownames(mean_spec_features_matrix) <- mean_spec_features$song.type.year
dat <- list(Y = mean_spec_features_matrix)
signal <- mvgls(Y ~ 1, data = dat, tree, model = "lambda", penalty = "RidgeArch")
summary(signal)
Call:
mvgls(formula = Y ~ 1, data = dat, tree = tree, model = "lambda",
penalty = "RidgeArch")
Generalized least squares fit by penalized REML
GIC logLik
-625.5474 384.4835
Parameter estimate(s):
lambda: 0.2947
Regularization parameter (gamma): 0
Covariance matrix of size: 12 by 12
for 33 observations
Coefficients (truncated):
meandom mindom maxdom dfrange sp.ent modindx duration skew kurt
(Intercept) 5.488 2.726 8.125 5.399 0.9063 10.84 0.1417 2.702 13.6
time.ent
(Intercept) 0.8912
Use "coef" to display all the coefficientsmodels <- c(brownian_motion = "BM", Ornstein_Uhlenbeck = "OU", early_burst = "EB",
rate_trend = "rate_trend", puntctuational = "kappa", time_dependent = "delta",
white = "white")
out <- warbleR:::pblapply_wrblr_int(features, cl = 10, function(x) {
trait <- mean_spec_features[, x]
# trait_se <- se_spec_features[, x]
names(trait) <- mean_spec_features$song.type.year
mods <- lapply(models, function(y) {
fc <- fitContinuous(phy = tree, dat = trait, model = y)
out <- data.frame(feature = x, model = y, AICc = fc$opt$aicc,
alpha = ifelse(length(fc$opt$alpha) > 0, fc$opt$alpha,
NA))
return(out)
})
out_df <- do.call(rbind, mods)
return(out_df)
})
evo_mods <- do.call(rbind, out)
write.csv(evo_mods, "./data/processed/evolutionary_models_by_feature.csv",
row.names = FALSE)evo_mods <- read.csv("./data/processed/evolutionary_models_by_feature.csv")
ou_evo <- evo_mods[evo_mods$model == "OU", ]
out <- warbleR:::pblapply_wrblr_int(1:nrow(ou_evo), cl = 10, function(x) {
trait <- mean_spec_features[, ou_evo$feature[x]]
# trait_se <- se_spec_features[, x]
names(trait) <- mean_spec_features$song.type.year
cou <- compar.ou(trait, tree, alpha = ou_evo$alpha[x])
out_df <- data.frame(feature = ou_evo$feature[x], ou_opt = cou$para[2,
1])
return(out_df)
})
ou_opts <- do.call(rbind, out)
spec_features$year <- as.numeric(substr(spec_features$song.type.year,
8, 11))
agg_feat_by_year <- aggregate(. ~ year, spec_features[, c("year",
features)], mean)
stck_feat_by_year <- stack(agg_feat_by_year)
stck_feat_by_year <- stck_feat_by_year[stck_feat_by_year$ind != "year",
]
stck_feat_by_year$year <- agg_feat_by_year$year
ggplot(stck_feat_by_year, aes(x = year, y = values)) + geom_line() +
theme_classic(base_size = 20) + facet_wrap(~ind, scales = "free_y")
evo_mods <- read.csv("./data/processed/evolutionary_models_by_feature.csv")
evo_mods <- evo_mods[order(evo_mods$feature, evo_mods$AICc), ]
out <- lapply(unique(evo_mods$feature), function(x) {
X <- evo_mods[evo_mods$feature == x, ]
X <- X[X$AICc <= min(X$AICc) + 4, ]
return(X)
})
do.call(rbind, out)| feature | model | AICc | alpha | |
|---|---|---|---|---|
| 28 | dfrange | white | 118.11993 | NA |
| 23 | dfrange | OU | 118.55581 | 0.1415737 |
| 49 | duration | white | -172.58969 | NA |
| 44 | duration | OU | -170.16211 | 2.7182818 |
| 63 | kurt | white | 222.15864 | NA |
| 58 | kurt | OU | 224.21211 | 0.1376262 |
| 21 | maxdom | white | 100.54426 | NA |
| 16 | maxdom | OU | 101.75903 | 0.1782065 |
| 2 | meandom | OU | 86.73243 | 0.0581166 |
| 84 | meanpeakf | white | 111.74744 | NA |
| 79 | meanpeakf | OU | 113.31265 | 0.1728527 |
| 14 | mindom | white | 99.05904 | NA |
| 9 | mindom | OU | 99.31139 | 0.1265486 |
| 42 | modindx | white | 161.54394 | NA |
| 37 | modindx | OU | 163.78033 | 0.2568796 |
| 56 | skew | white | 83.75584 | NA |
| 51 | skew | OU | 84.47327 | 0.0863016 |
| 30 | sp.ent | OU | -125.80764 | 0.0799415 |
| 35 | sp.ent | white | -122.97104 | NA |
| 70 | time.ent | white | -282.66898 | NA |
| 65 | time.ent | OU | -280.24139 | 2.7182818 |
| 77 | time.IQR | white | -225.28922 | NA |
| 72 | time.IQR | OU | -222.86163 | 2.7182818 |
out <- lapply(unique(evo_mods$feature), function(x) {
X <- evo_mods[evo_mods$feature == x, ]
X$delta_aicc <- X$AICc - min(X$AICc)
X$delta_aicc <- X$delta_aicc
return(X)
})
evo_delta_aic <- do.call(rbind, out)
restricted <- c("meandom", "mindom", "maxdom", "dfrange", "sp.ent",
"meanpeakf")
evo_delta_aic$restriction <- ifelse(evo_delta_aic$feature %in% restricted,
"restricted", "unrestricted")
agg_delta_aicc <- aggregate(delta_aicc ~ model + restriction, evo_delta_aic,
mean)
# agg_delta_aicc$delta_aicc <- agg_delta_aicc$delta_aicc * -1
agg_delta_aicc <- agg_delta_aicc[agg_delta_aicc$model != "white",
]
ggplot(agg_delta_aicc, aes(x = model, y = delta_aicc, fill = restriction)) +
geom_bar(stat = "identity", position = "dodge") + scale_fill_viridis_d(begin = 0.2,
end = 0.8, alpha = 0.6) + theme_classic(base_size = 20) + labs(x = "Evolutionary model",
y = "Delta AICc", fill = "Morphological\nrestriction")
## Run multiple MCMC chains.
handle_1 <- ratematrixMCMC(data = mean_spec_features_matrix, phy = tree,
gen = 30000, dir = tempdir())
handle_2 <- ratematrixMCMC(data = mean_spec_features_matrix, phy = tree,
gen = 30000, dir = tempdir())
## Read both chains:
posterior_1 <- readMCMC(handle_1, burn = 0.2, thin = 1)
posterior_2 <- readMCMC(handle_2, burn = 0.2, thin = 1)
## Check for convergence checkConvergence(posterior_1,
## posterior_2) Merge all posteriors in the list.
merged.posterior <- mergePosterior(posterior_1, posterior_2)
# length(merged.posterior$matrix)
# str(merged.posterior$matrix[[1]])
mean_cov <- merged.posterior$matrix[[1]]
for (i in 2:length(merged.posterior$matrix)) mean_cov <- mean_cov +
merged.posterior$matrix[[i]]
mean_cov <- mean_cov/length(merged.posterior$matrix)
# range(mean_cov)
mean_corr <- cov2cor(mean_cov)
colnames(mean_corr) <- paste(names(mean_spec_features)[-1], c(1, 1,
1, 1, 1, 0, 0, 0, 0, 0, 0, 1))
cols_corr <- colorRampPalette(c(viridis(3, direction = 1, begin = 0.2,
end = 0.5), "#BEBEBE1A", "white", "#BEBEBE1A", viridis(3, direction = 1,
begin = 0.7, end = 0.9)))(30)
cp <- corrplot.mixed(mean_corr, tl.cex = 0.7, upper.col = cols_corr,
lower.col = cols_corr, order = "hclust", lower = "number", upper = "ellipse",
tl.col = "black")
## Plot results: plotRatematrix(merged.posterior)
Call:
Observed Rate Ratio: 6.72984
P-value: 1e-04
Effect Size: 2.83204
Based on 10001 random permutations
The rate for group 0 is 0.675139810269116
The rate for group 1 is 0.100320309806049 # get confidence intervals
out <- warbleR:::pblapply_wrblr_int(1:50, cl = 20, function(x) {
tips <- sample(tree$tip.label, 4)
sub_tree <- drop.tip(tree, tip = tips)
sub_mat <- mean_spec_features_matrix[!row.names(mean_spec_features_matrix) %in%
tips, ]
jck_EMR <- compare.multi.evol.rates(A = sub_mat, gp = feat_grp,
Subset = TRUE, phy = sub_tree, iter = 10000, print.progress = FALSE)
return(jck_EMR$sigma.d.gp)
})
jck_evo_rates <- as.data.frame(do.call(rbind, out))
names(jck_evo_rates) <- c("Unrestricted", "Restricted")
jck_evo_rates <- stack(jck_evo_rates)
write.csv(jck_evo_rates, "./data/processed/evolutionary_rate_jacknife.csv",
row.names = FALSE)jck_evo_rates <- read.csv("./data/processed/evolutionary_rate_jacknife.csv")
cols <- viridis(10)
# raincoud plot:
fill_color <- adjustcolor(cols[[6]], 0.4)
ggplot(jck_evo_rates, aes(y = values, x = ind)) +
# add half-violin from {ggdist} package
stat_halfeye(
fill = fill_color,
alpha = 0.5,
# custom bandwidth
adjust = .5,
# adjust height
width = .6,
.width = 0,
# move geom to the cright
justification = -.2,
point_colour = NA
) +
geom_boxplot(fill = fill_color,
width = .15,
# remove outliers
outlier.shape = NA # `outlier.shape = NA` works as well
) +
# add justified jitter from the {gghalves} package
geom_half_point(
color = fill_color,
# draw jitter on the left
side = "l",
# control range of jitter
range_scale = .4,
# add some transparency
alpha = .5,
) +
# ylim(c(-30, 310)) +
labs(x = "Acoustic features", y = "Evolutionary rate") + theme(axis.text.x = element_text(angle = 15, hjust = 1)) +
theme_classic(base_size = 20)
Call:
phylo.integration(A = mean_spec_features_matrix, phy = tree,
partition.gp = feat_grp, iter = 10000, print.progress = FALSE)
r-PLS: 0.29
Effect Size (Z): -0.11624
P-value: 0.5490451
Based on 10001 random permutations# Taking only numeric data for the pca
degrad_df <- read.csv("./data/processed/degradation_measures_SUR_ALL.csv")
degrad_params <- c("signal.to.noise.ratio", "tail.to.signal.ratio",
"excess.attenuation", "envelope.correlation", "blur.ratio", "cross.correlation",
"spectrum.blur.ratio", "spectrum.correlation")
degrad_df <- degrad_df[complete.cases(degrad_df[, degrad_params]),
]
sapply(degrad_df[, degrad_params], function(x) any(is.infinite(x)))signal.to.noise.ratio tail.to.signal.ratio excess.attenuation
FALSE FALSE TRUE
envelope.correlation blur.ratio cross.correlation
FALSE FALSE FALSE
spectrum.blur.ratio spectrum.correlation
FALSE FALSE degrad_df <- degrad_df[!is.infinite(degrad_df$excess.attenuation),
]
pca_degrad <- prcomp(degrad_df[, degrad_params], scale = T)
pca_var <- round(summary(pca_degrad)$importance[2, ] * 100)
degrad_df$pc1 <- pca_degrad$x[, 1]
degrad_params <- c(degrad_params, "pc1")
# plot rotation values by PC
pca_rot <- as.data.frame(pca_degrad$rotation[, 1:4])
pca_rot_stck <- stack(pca_rot)
pca_rot_stck$variable <- rownames(pca_rot)
pca_rot_stck$values[pca_rot_stck$ind == "PC1"] <- pca_rot_stck$values[pca_rot_stck$ind ==
"PC1"]
pca_rot_stck$Sign <- ifelse(pca_rot_stck$values > 0, "Positive", "Negative")
pca_rot_stck$rotation <- abs(pca_rot_stck$values)
pca_rot_stck$ind_var <- paste0(pca_rot_stck$ind, " (", sapply(pca_rot_stck$ind,
function(x) pca_var[names(pca_var) == x]), "%)")
ggplot(pca_rot_stck, aes(x = variable, y = rotation, fill = Sign)) +
geom_col() + coord_flip() + scale_fill_viridis_d(alpha = 0.7,
begin = 0.2, end = 0.8) + facet_wrap(~ind_var) + theme_classic()
mean_spec_features$pc1 <- sapply(mean_spec_features$song.type.year,
function(x) mean(degrad_df$pc1[degrad_df$song.type.year == x]))
# Standardize the tree to unit length
ultra_tree_standardized <- multi2di(ultra_tree)
ultra_tree_standardized <- compute.brlen(ultra_tree_standardized,
method = "cladewise")
mods <- warbleR:::pblapply_wrblr_int(2:ncol(mean_spec_features), cl = 10,
function(x) {
# brownian motion
bm_mod <- rate_gls(x = mean_spec_features$pc1, y = mean_spec_features[,
x], species = mean_spec_features$song.type.year, ultra_tree_standardized,
model = "predictor_BM", silent = TRUE, maxiter = 1000)
bootout_bm <- rate_gls_boot(bm_mod, n = 100, silent = TRUE,
maxiter = 1000)
bootout_bm <- cbind(model = "BM", feature = names(mean_spec_features)[x],
parameter = rownames(bootout_bm$summary), as.data.frame(bootout_bm$summary))
# geometric brownian motion
gbm_mod <- rate_gls(x = mean_spec_features$pc1 + 1, y = mean_spec_features[,
x], species = mean_spec_features$song.type.year, ultra_tree_standardized,
model = "predictor_gBM", silent = TRUE, maxiter = 1000)
bootout_gbm <- rate_gls_boot(gbm_mod, n = 100, silent = TRUE,
maxiter = 1000)
bootout_gbm <- cbind(model = "gBM", feature = names(mean_spec_features)[x],
parameter = rownames(bootout_gbm$summary), as.data.frame(bootout_gbm$summary))
re_mod <- rate_gls(x = mean_spec_features$pc1, y = mean_spec_features[,
x], species = mean_spec_features$song.type.year, ultra_tree_standardized,
model = "recent_evol", silent = TRUE, maxiter = 1000)
bootout_re <- rate_gls_boot(re_mod, n = 100, silent = TRUE,
maxiter = 1000)
bootout_re <- cbind(model = "recent_evol", feature = names(mean_spec_features)[x],
parameter = rownames(bootout_re$summary), as.data.frame(bootout_re$summary))
bootout <- rbind(bootout_bm, bootout_gbm, bootout_re)
return(bootout)
})
gls_res <- do.call(rbind, mods)
write.csv(gls_res, "./data/processed/evolutionary_rate_by_degradation.csv",
row.names = FALSE)gls_res <- read.csv("./data/processed/evolutionary_rate_by_degradation.csv")
gls_res <- gls_res[gls_res$parameter == "b" | gls_res$parameter ==
"Rsquared", c("model", "feature", "parameter", "Estimate", "SE",
"X2.5.", "X97.5.")]
gls_res_kbl <- kableExtra::kbl(gls_res, row.names = FALSE, escape = FALSE,
format = "html", digits = 3)
gls_res_kbl <- kableExtra::row_spec(kable_input = gls_res_kbl, row = which(gls_res$`97.5%` *
gls_res$`2.5%` > 0 & gls_res$parameter == "b"), background = grDevices::adjustcolor("#6DCD59FF",
alpha.f = 0.3))
gls_res_kbl| model | feature | parameter | Estimate | SE | X2.5. | X97.5. |
|---|---|---|---|---|---|---|
| BM | meandom | b | -1.727 | 6.478 | -7.960 | 8.042 |
| BM | meandom | Rsquared | 0.002 | NA | 0.000 | 0.091 |
| gBM | meandom | b | -1.198 | 5.854 | -7.603 | 4.771 |
| gBM | meandom | Rsquared | 0.001 | NA | 0.000 | 0.090 |
| recent_evol | meandom | b | -0.422 | 0.263 | -1.030 | 0.402 |
| recent_evol | meandom | Rsquared | 0.076 | NA | 0.000 | 0.376 |
| BM | mindom | b | 3.289 | 18.729 | -6.658 | 13.752 |
| BM | mindom | Rsquared | 0.001 | NA | 0.000 | 0.217 |
| gBM | mindom | b | 4.498 | 14.874 | -2.105 | 61.114 |
| gBM | mindom | Rsquared | 0.003 | NA | 0.005 | 0.640 |
| recent_evol | mindom | b | 1.023 | 0.344 | 0.111 | 1.583 |
| recent_evol | mindom | Rsquared | 0.222 | NA | 0.022 | 0.432 |
| BM | maxdom | b | -8.240 | 17.585 | -24.814 | 13.160 |
| BM | maxdom | Rsquared | 0.007 | NA | 0.000 | 0.095 |
| gBM | maxdom | b | -6.169 | 14.594 | -23.030 | 6.257 |
| gBM | maxdom | Rsquared | 0.006 | NA | 0.000 | 0.085 |
| recent_evol | maxdom | b | -1.249 | 0.297 | -1.638 | 0.107 |
| recent_evol | maxdom | Rsquared | 0.363 | NA | 0.003 | 0.492 |
| BM | dfrange | b | -4.729 | 21.663 | -18.153 | 15.975 |
| BM | dfrange | Rsquared | 0.002 | NA | 0.000 | 0.123 |
| gBM | dfrange | b | -14.815 | 28.522 | -31.707 | 29.440 |
| gBM | dfrange | Rsquared | 0.009 | NA | 0.000 | 0.109 |
| recent_evol | dfrange | b | 0.522 | 0.970 | -2.142 | 3.357 |
| recent_evol | dfrange | Rsquared | 0.009 | NA | 0.000 | 0.356 |
| BM | sp.ent | b | 0.009 | 0.014 | -0.010 | 0.024 |
| BM | sp.ent | Rsquared | 0.011 | NA | 0.000 | 0.218 |
| gBM | sp.ent | b | 0.010 | 0.012 | -0.004 | 0.034 |
| gBM | sp.ent | Rsquared | 0.019 | NA | 0.002 | 0.770 |
| recent_evol | sp.ent | b | 0.001 | 0.000 | 0.000 | 0.001 |
| recent_evol | sp.ent | Rsquared | 0.356 | NA | 0.073 | 0.380 |
| BM | modindx | b | 58.418 | 78.962 | -48.165 | 146.320 |
| BM | modindx | Rsquared | 0.017 | NA | 0.000 | 0.140 |
| gBM | modindx | b | 61.666 | 69.051 | -7.437 | 488.068 |
| gBM | modindx | Rsquared | 0.025 | NA | 0.001 | 0.635 |
| recent_evol | modindx | b | 5.940 | 3.199 | -2.769 | 11.465 |
| recent_evol | modindx | Rsquared | 0.100 | NA | 0.001 | 0.378 |
| BM | duration | b | 0.001 | 0.004 | -0.002 | 0.004 |
| BM | duration | Rsquared | 0.001 | NA | 0.000 | 0.110 |
| gBM | duration | b | 0.001 | 0.003 | 0.000 | 0.004 |
| gBM | duration | Rsquared | 0.002 | NA | 0.000 | 0.570 |
| recent_evol | duration | b | 0.000 | 0.000 | 0.000 | 0.001 |
| recent_evol | duration | Rsquared | 0.126 | NA | 0.002 | 0.367 |
| BM | skew | b | -5.277 | 8.756 | -18.343 | 7.859 |
| BM | skew | Rsquared | 0.012 | NA | 0.000 | 0.168 |
| gBM | skew | b | -3.019 | 7.473 | -12.053 | 9.595 |
| gBM | skew | Rsquared | 0.005 | NA | 0.000 | 0.082 |
| recent_evol | skew | b | -0.058 | 0.412 | -0.776 | 0.792 |
| recent_evol | skew | Rsquared | 0.001 | NA | 0.000 | 0.278 |
| BM | kurt | b | -366.025 | 569.299 | -839.237 | 353.048 |
| BM | kurt | Rsquared | 0.013 | NA | 0.000 | 0.145 |
| gBM | kurt | b | -205.048 | 489.805 | -664.046 | 509.046 |
| gBM | kurt | Rsquared | 0.006 | NA | 0.000 | 0.093 |
| recent_evol | kurt | b | -3.547 | 30.350 | -50.642 | 45.737 |
| recent_evol | kurt | Rsquared | 0.000 | NA | 0.000 | 0.342 |
| BM | time.ent | b | 0.000 | 0.000 | 0.000 | 0.000 |
| BM | time.ent | Rsquared | 0.000 | NA | 0.000 | 0.121 |
| gBM | time.ent | b | 0.000 | 0.000 | 0.000 | 0.000 |
| gBM | time.ent | Rsquared | 0.000 | NA | 0.000 | 0.115 |
| recent_evol | time.ent | b | 0.000 | 0.000 | 0.000 | 0.000 |
| recent_evol | time.ent | Rsquared | 0.042 | NA | 0.000 | 0.251 |
| BM | time.IQR | b | 0.000 | 0.001 | -0.001 | 0.001 |
| BM | time.IQR | Rsquared | 0.004 | NA | 0.000 | 0.124 |
| gBM | time.IQR | b | 0.000 | 0.001 | 0.000 | 0.002 |
| gBM | time.IQR | Rsquared | 0.006 | NA | 0.000 | 0.533 |
| recent_evol | time.IQR | b | 0.000 | 0.000 | 0.000 | 0.000 |
| recent_evol | time.IQR | Rsquared | 0.026 | NA | 0.000 | 0.175 |
| BM | meanpeakf | b | 5.881 | 19.215 | -9.870 | 25.909 |
| BM | meanpeakf | Rsquared | 0.003 | NA | 0.000 | 0.119 |
| gBM | meanpeakf | b | 3.524 | 15.902 | -6.365 | 22.135 |
| gBM | meanpeakf | Rsquared | 0.002 | NA | 0.000 | 0.254 |
| recent_evol | meanpeakf | b | -0.632 | 0.827 | -2.298 | 1.074 |
| recent_evol | meanpeakf | Rsquared | 0.018 | NA | 0.000 | 0.378 |
| BM | pc1 | b | 1.216 | 1.431 | -1.250 | 3.247 |
| BM | pc1 | Rsquared | 0.023 | NA | 0.000 | 0.096 |
| gBM | pc1 | b | 1.291 | 1.279 | -0.055 | 9.022 |
| gBM | pc1 | Rsquared | 0.032 | NA | 0.001 | 0.536 |
| recent_evol | pc1 | b | 0.000 | 0.068 | -0.150 | 0.177 |
| recent_evol | pc1 | Rsquared | 0.000 | NA | 0.000 | 0.335 |
Session information
R version 4.3.1 (2023-06-16)
Platform: x86_64-pc-linux-gnu (64-bit)
Running under: Ubuntu 20.04.2 LTS
Matrix products: default
BLAS: /usr/lib/x86_64-linux-gnu/atlas/libblas.so.3.10.3
LAPACK: /usr/lib/x86_64-linux-gnu/atlas/liblapack.so.3.10.3; LAPACK version 3.9.0
locale:
[1] LC_CTYPE=pt_BR.UTF-8 LC_NUMERIC=C
[3] LC_TIME=es_CR.UTF-8 LC_COLLATE=pt_BR.UTF-8
[5] LC_MONETARY=es_CR.UTF-8 LC_MESSAGES=pt_BR.UTF-8
[7] LC_PAPER=es_CR.UTF-8 LC_NAME=C
[9] LC_ADDRESS=C LC_TELEPHONE=C
[11] LC_MEASUREMENT=es_CR.UTF-8 LC_IDENTIFICATION=C
time zone: America/Costa_Rica
tzcode source: system (glibc)
attached base packages:
[1] stats graphics grDevices utils datasets methods base
other attached packages:
[1] mvMORPH_1.1.7 subplex_1.8 corpcor_1.6.10 gghalves_0.1.4
[5] ggdist_3.3.0 geiger_2.0.11 evolvability_2.0.0 geomorph_4.0.6
[9] Matrix_1.6-2 rgl_1.2.1 RRPP_1.4.0 ratematrix_1.2.4
[13] phytools_1.9-16 maps_3.4.1.1 ggtree_3.8.2 phangorn_2.11.1
[17] ape_5.7-1 DT_0.30 corrplot_0.92 viridis_0.6.4
[21] viridisLite_0.4.2 brms_2.20.4 Rcpp_1.0.11 ggplot2_3.4.4
[25] Rraven_1.0.14 readxl_1.4.3 baRulho_2.1.0 ohun_1.0.0
[29] warbleR_1.1.29 NatureSounds_1.0.4 seewave_2.2.3 tuneR_1.4.5
[33] rprojroot_2.0.4 formatR_1.14 knitr_1.45
loaded via a namespace (and not attached):
[1] fs_1.6.3 matrixStats_1.1.0 bitops_1.0-7
[4] sf_1.0-14 webshot_0.5.5 httr_1.4.7
[7] doParallel_1.0.17 numDeriv_2016.8-1.1 tools_4.3.1
[10] backports_1.4.1 utf8_1.2.4 R6_2.5.1
[13] lazyeval_0.2.2 withr_2.5.2 Brobdingnag_1.2-9
[16] prettyunits_1.2.0 gridExtra_2.3 soundgen_2.6.1
[19] cli_3.6.1 shinyjs_2.1.0 labeling_0.4.3
[22] mvtnorm_1.2-3 readr_2.1.4 proxy_0.4-27
[25] dtw_1.23-1 pbapply_1.7-2 QuickJSR_1.0.7
[28] systemfonts_1.0.5 yulab.utils_0.1.0 StanHeaders_2.26.28
[31] svglite_2.1.2 parallelly_1.36.0 plotrix_3.8-3
[34] rstudioapi_0.15.0 phylolm_2.6.2 optimParallel_1.0-2
[37] generics_0.1.3 gridGraphics_0.5-1 combinat_0.0-8
[40] gtools_3.9.4 crosstalk_1.2.0 dplyr_1.1.3
[43] distributional_0.3.2 inline_0.3.19 loo_2.6.0
[46] fansi_1.0.5 abind_1.4-5 lifecycle_1.0.4
[49] scatterplot3d_0.3-44 yaml_2.3.7 clusterGeneration_1.3.8
[52] grid_4.3.1 promises_1.2.1 crayon_1.5.2
[55] miniUI_0.1.1.1 lattice_0.22-5 pillar_1.9.0
[58] boot_1.3-28.1 rjson_0.2.21 estimability_1.4.1
[61] fftw_1.0-7 shinystan_2.6.0 future.apply_1.11.0
[64] codetools_0.2-19 fastmatch_1.1-4 glue_1.6.2
[67] packrat_0.9.2 ggfun_0.1.3 remotes_2.4.2.1
[70] vctrs_0.6.4 png_0.1-8 treeio_1.24.3
[73] spam_2.10-0 testthat_3.2.0 cellranger_1.1.0
[76] gtable_0.3.4 cachem_1.0.8 xfun_0.41
[79] mime_0.12 coda_0.19-4 signal_0.7-7
[82] iterators_1.0.14 sketchy_1.0.2 pbmcapply_1.5.1
[85] shinythemes_1.2.0 units_0.8-4 ellipsis_0.3.2
[88] nlme_3.1-163 xts_0.13.1 threejs_0.3.3
[91] rstan_2.32.3 tensorA_0.36.2 Deriv_4.1.3
[94] KernSmooth_2.23-22 colorspace_2.1-0 DBI_1.1.3
[97] mnormt_2.1.1 tidyselect_1.2.0 processx_3.8.2
[100] emmeans_1.8.9 compiler_4.3.1 rvest_1.0.3
[103] expm_0.999-7 xml2_1.3.5 colourpicker_1.3.0
[106] posterior_1.5.0 checkmate_2.3.0 scales_1.2.1
[109] dygraphs_1.1.1.6 classInt_0.4-10 quadprog_1.5-8
[112] callr_3.7.3 stringr_1.5.0 digest_0.6.33
[115] shinyBS_0.61.1 minqa_1.2.6 Sim.DiffProc_4.8
[118] rmarkdown_2.25 jpeg_0.1-10 htmltools_0.5.7
[121] pkgconfig_2.0.3 base64enc_0.1-3 lme4_1.1-35.1
[124] fastmap_1.1.1 rlang_1.1.2 htmlwidgets_1.6.2
[127] shiny_1.7.5.1 farver_2.1.1 zoo_1.8-12
[130] jsonlite_1.8.7 RCurl_1.98-1.13 magrittr_2.0.3
[133] kableExtra_1.3.4 ggplotify_0.1.2 dotCall64_1.1-0
[136] bayesplot_1.10.0 patchwork_1.1.3 munsell_0.5.0
[139] stringi_1.7.12 brio_1.1.3 MASS_7.3-60
[142] plyr_1.8.9 pkgbuild_1.4.2 parallel_4.3.1
[145] listenv_0.9.0 splines_4.3.1 hms_1.1.3
[148] ps_1.7.5 igraph_1.5.1 markdown_1.11
[151] reshape2_1.4.4 stats4_4.3.1 rstantools_2.3.1.1
[154] evaluate_0.23 RcppParallel_5.1.7 deSolve_1.38
[157] nloptr_2.0.3 tzdb_0.4.0 foreach_1.5.2
[160] httpuv_1.6.12 tidyr_1.3.0 purrr_1.0.2
[163] future_1.33.0 xtable_1.8-4 glassoFast_1.0.1
[166] e1071_1.7-13 tidytree_0.4.5 later_1.3.1
[169] class_7.3-22 tibble_3.2.1 aplot_0.2.2
[172] memoise_2.0.1 globals_0.16.2 bridgesampling_1.1-2
[175] xaringanExtra_0.7.0