---
title: "Elapsed time (log₁₀ generations)"
---
::: {.workflow}
::: {}
**Input**
Saved prediction and summary files.
:::
::: {}
**Action**
Read the model record and build its figure.
:::
::: {}
**Output**
Model table, checks, and plot.
:::
::: {}
**Next**
Continue to the next model chapter.
:::
:::
```{r setup, include=FALSE}
source(here::here("Scripts", "14_epred_cache.R"))
cache_dir <- here::here("Rdata", "epred_draws")
cache <- read_epred_cache("m04", cache_dir)
```
## Phylogenetic covariance and model species
This model contains **253 species** (7,186 contrasts). The phylogenetic correlation matrix was constructed and aligned to those species as follows; `brms` uses it in `(1 | gr(sp_ncbi, cov = A))`.
```{r phylogenetic-covariance, eval=FALSE}
A_full <- prepR4pcm::pr_phylo_cor(tree_final)
# Fallback if needed: ape::vcv.phylo(tree_final, corr = TRUE)
dat_model <- dat_es |> dplyr::filter(!is.na(log10_generations))
phylo <- prepare_phylo_and_data(dat_model, A_full, fill_unmatched = TRUE)
dat_model <- phylo$dat_model
A <- phylo$A_mod
formula <- brms::bf(
yi_lnM_safe ~ log10_generations + (1 | ref_id) +
(1 | gr(sp_ncbi, cov = A)) + (1 | gr(es_id_model, cov = V)),
sigma ~ log10_generations
)
V <- phylo$V
fit <- brms::brm(formula, data = dat_model,
data2 = list(A = A, V = V))
```
Continuous moderator: divergence as a function of elapsed time in generations (log₁₀).
## Model summary
`summary()` is read from `Rdata/epred_draws/m04.rds`; the full `brms` fit is not loaded at render.
```{r summary}
print_cache_summary(cache)
```
## Main predictions in equivalent-d units
Predictions at 1, 10, and 100 generations are reported on the $\ln M$ scale
and as $d_{\mathrm{eq}} \approx \sqrt{2\exp(2\ln M)}$. These are
prediction-scale summaries from the cached posterior draws; the regression
slope itself is not transformed into an equivalent $d$.
```{r elapsed-generations-d-eq}
source(here::here("Scripts", "18_contrasts_cache.R"))
kable_contrasts(
format_continuous_d_eq(cache, unit = "Generations"),
caption = "Predicted lnM and equivalent d at selected elapsed generations, with 95% credible intervals."
)
```
## Orchard-like model figure
The orchard-style figure is read from `Rdata/figures/publication/orchard/`. The complete editable code used to build this plot is shown below.
```{r figure, fig.width=8, fig.height=9}
knitr::include_graphics(
here::here("Rdata", "figures", "publication", "orchard", "m04_orchard_combined.png")
)
```
### Editable plotting code
```{r build-orchard-m04, eval=FALSE}
library(here)
library(tidyverse)
library(patchwork)
model_id <- "m04"
moderator <- "log10_generations"
moderator_label <- "Elapsed time (log10 generations)"
col_location <- "#0072B2"
col_location_light <- "#88CCEE"
col_scale <- "#D55E00"
col_scale_light <- "#E69F00"
col_scale_ribbon <- "#F2B27E"
out_dir <- here::here("Rdata", "figures", "publication", "orchard")
out_dir_pdf <- here::here("Figures", "publication", "orchard")
dir.create(out_dir, recursive = TRUE, showWarnings = FALSE)
dir.create(out_dir_pdf, recursive = TRUE, showWarnings = FALSE)
dat_es <- readRDS(here::here("Rdata", "effect_sizes", "proceed_lnm_safe.rds"))
cache <- readRDS(here::here("Rdata", "epred_draws", paste0(model_id, ".rds")))
theme_orchard <- function() {
ggplot2::theme_classic(base_size = 13) +
ggplot2::theme(
axis.text.y = ggplot2::element_text(size = 11),
axis.title.x = ggplot2::element_text(size = 12),
axis.ticks.y = ggplot2::element_blank(),
panel.grid.major.x = ggplot2::element_line(colour = "grey92"),
legend.position = "bottom",
legend.title = ggplot2::element_text(size = 10),
plot.title = ggplot2::element_text(size = 13, face = "bold"),
plot.caption = ggplot2::element_text(size = 9, colour = "grey50")
)
}
save_plot <- function(p, stem, width = 8, height = 6) {
ggplot2::ggsave(file.path(out_dir_pdf, paste0(stem, ".pdf")), p,
width = width, height = height, device = cairo_pdf)
ggplot2::ggsave(file.path(out_dir, paste0(stem, ".png")), p,
width = width, height = height, dpi = 300, type = "cairo")
}
raw <- dat_es[!is.na(dat_es[[moderator]]), ] |>
dplyr::mutate(precision = 1 / sqrt(vi_lnM_safe))
pred <- cache$loc |>
dplyr::group_by(.data[[moderator]]) |>
dplyr::summarise(
estimate = mean(.epred),
lowerCL = quantile(.epred, 0.025),
upperCL = quantile(.epred, 0.975),
.groups = "drop"
)
pred_sig <- cache$scl |>
dplyr::group_by(.data[[moderator]]) |>
dplyr::summarise(
estimate = mean(sigma),
lowerCL = quantile(sigma, 0.025),
upperCL = quantile(sigma, 0.975),
.groups = "drop"
)
raw_pred_loc <- approx(
pred[[moderator]], pred$estimate,
xout = raw[[moderator]], rule = 2
)$y
raw_cont_sig <- raw |>
dplyr::mutate(abs_resid = abs(yi_lnM_safe - raw_pred_loc))
p_loc <- ggplot2::ggplot() +
ggplot2::geom_hline(yintercept = 0, linetype = "dashed", colour = "grey50") +
ggplot2::geom_point(
data = raw,
ggplot2::aes(x = .data[[moderator]], y = yi_lnM_safe, size = precision),
alpha = 0.22, shape = 21, fill = col_location_light, colour = col_location
) +
ggplot2::geom_ribbon(
data = pred,
ggplot2::aes(x = .data[[moderator]], ymin = lowerCL, ymax = upperCL),
alpha = 0.35, fill = col_location
) +
ggplot2::geom_line(
data = pred,
ggplot2::aes(x = .data[[moderator]], y = estimate),
linewidth = 1.1, colour = col_location
) +
ggplot2::scale_size_continuous(name = "Precision (1/SE)", range = c(0.3, 4)) +
ggplot2::labs(
x = moderator_label,
y = "lnM",
title = NULL
) +
theme_orchard()
p_scl <- ggplot2::ggplot() +
ggplot2::geom_point(
data = raw_cont_sig,
ggplot2::aes(x = .data[[moderator]], y = abs_resid, size = precision),
alpha = 0.20, shape = 21, fill = col_scale_light, colour = col_scale
) +
ggplot2::geom_ribbon(
data = pred_sig,
ggplot2::aes(x = .data[[moderator]], ymin = lowerCL, ymax = upperCL),
alpha = 0.35, fill = col_scale_ribbon
) +
ggplot2::geom_line(
data = pred_sig,
ggplot2::aes(x = .data[[moderator]], y = estimate),
linewidth = 1.1, colour = col_scale
) +
ggplot2::scale_size_continuous(name = "Precision (1/SE)", range = c(0.3, 4)) +
ggplot2::labs(
x = moderator_label,
y = "residual lnM (SD)",
title = NULL,
caption = NULL
) +
theme_orchard()
p_comb <- patchwork::wrap_plots(
p_loc,
p_scl + ggplot2::guides(size = "none"),
ncol = 1,
guides = "collect"
) & ggplot2::theme(legend.position = "bottom")
save_plot(p_loc, paste0(model_id, "_orchard_location"), width = 8, height = 5)
save_plot(p_scl, paste0(model_id, "_orchard_scale"), width = 8, height = 4)
save_plot(p_comb, paste0(model_id, "_orchard_combined"), width = 8, height = 9)
p_comb
```
