---
title: "deepSTRAPP: Continuous trait data"
author: "Maël Doré"
date: "`r Sys.Date()`"
output: rmarkdown::html_vignette
vignette: >
  %\VignetteIndexEntry{deepSTRAPP: Continuous trait data}
  %\VignetteEngine{knitr::rmarkdown}
  %\VignetteEncoding{UTF-8}
---

```{r set_options, include = FALSE}
knitr::opts_chunk$set(
  eval = FALSE, # Chunks of codes will not be evaluated by default
  collapse = TRUE,
  comment = "#>",
  fig.width = 7, fig.height = 5   # Set device size at rendering time (when plots are generated)
)
```

```{r setup, eval = TRUE, include = FALSE}
library(deepSTRAPP)

is_dev_version <- function (pkg = "deepSTRAPP")
{
  # # Check if ran on CRAN
  # not_cran <- identical(Sys.getenv("NOT_CRAN"), "true") # || interactive()

  # Version number check
  version <- tryCatch(as.character(utils::packageVersion(pkg)), error = function(e) "")
  dev_version <- grepl("\\.9000", version)

  # not_cran || dev_version
  
  return(dev_version)
}

```

```{r adjust_dpi_CRAN, include = FALSE, eval = !is_dev_version()}
knitr::opts_chunk$set(
  dpi = 50   # Lower DPI to save space
)
```
```{r adjust_dpi_dev, include = FALSE, eval = is_dev_version()}
knitr::opts_chunk$set(
  dpi = 72   # Default DPI for the dev version
)
```

<br>
This is a simple example that shows how deepSTRAPP can be used to test for __correlations__ between a __continuous trait__ and __diversification rates__ along evolutionary times.
It presents the main functions in a typical __deepSTRAPP workflow__.

<br>
For an example with __binary data__ (2 levels), please see the example in the __Main tutorial__: `vignette("main_tutorial")`.

For an example with __biogeographic data__, see this vignette: `vignette("deepSTRAPP_biogeographic_data")`.

<br>
Please note that the trait data and phylogeny calibration used in this example are __NOT valid biological data__. They were modified in order to provide results illustrating the usefulness of deepSTRAPP.

<br>


```{r load_data_cont}
# ------ Step 0: Load data ------ #

## Load trait df
data("Ponerinae_trait_tip_data", package = "deepSTRAPP")

dim(Ponerinae_trait_tip_data)
View(Ponerinae_trait_tip_data)

# Extract continuous trait data as a named vector
Ponerinae_cont_tip_data <- setNames(object = Ponerinae_trait_tip_data$fake_cont_tip_data,
                                    nm = Ponerinae_trait_tip_data$Taxa)

# This not valid biological data. For the sake of this example, we will assume this is size data.

# Select a color scheme from lowest to highest values (i.e., smallest to largest ants)
color_scale = c("darkgreen", "limegreen", "orange", "red")

## Load phylogeny with old time-calibration
data("Ponerinae_tree_old_calib", package = "deepSTRAPP")

plot(Ponerinae_tree_old_calib)
ape::Ntip(Ponerinae_tree_old_calib) == length(Ponerinae_cont_tip_data)

## Check that trait data and phylogeny are named and ordered similarly
all(names(Ponerinae_cont_tip_data) == Ponerinae_tree_old_calib$tip.label)


## Inputs needed for Step 1 are the tip_data (Ponerinae_cont_tip_data) and the phylogeny
# (Ponerinae_tree_old_calib), and optionally, a color scheme (color_scale).

```

```{r prepare_trait_data_cont}
# ------ Step 1: Prepare trait data ------ #

## Goal: Map trait evolution on the time-calibrated phylogeny

# 1.1/ Fit evolutionary models to trait data using Maximum Likelihood.
# 1.2/ Select the best fitting model comparing AICc.
# 1.3/ Infer ancestral characters estimates (ACE) at nodes.
# 1.4/ Infer ancestral states along branches using interpolation to produce a `contMap`.

library(deepSTRAPP)

# All these actions are performed by a single function: deepSTRAPP::prepare_trait_data()
?deepSTRAPP::prepare_trait_data()

# Run prepare_trait_data with default options
# For continuous trait, a BM model is assumed by default.
Ponerinae_trait_object <- prepare_trait_data(tip_data = Ponerinae_cont_tip_data,
                                             trait_data_type = "continuous",
                                             phylo = Ponerinae_tree_old_calib,
                                             seed = 1234) # Set seed for reproducibility

# Explore output
str(Ponerinae_trait_object, 1)

# Extract the contMap representing continuous trait evolution on the phylogeny
Ponerinae_contMap <- Ponerinae_trait_object$contMap
plot_contMap(Ponerinae_contMap)

# Extract the Ancestral Character Estimates (ACE) = trait values at nodes
Ponerinae_ACE <- Ponerinae_trait_object$ace
head(Ponerinae_ACE)

## Inputs needed for Step 2 are the contMap, and optionally, the tip_data (Ponerinae_cont_tip_data),
# and the ACE (Ponerinae_ACE)

```


```{r prepare_diversification_data_cont}
# ------ Step 2: Prepare diversification data ------ #

## Goal: Map evolution of diversification rates and regime shifts on the time-calibrated phylogeny

# Run a BAMM (Bayesian Analysis of Macroevolutionary Mixtures)

# You need the BAMM C++ program installed in your machine to run this step.
# See the BAMM website: http://bamm-project.org/ and the companion R package [BAMMtools].

# 2.1/ Set BAMM - Record BAMM settings and generate all input files needed for BAMM.
# 2.2/ Run BAMM - Run BAMM and move output files in dedicated directory.
# 2.3/ Evaluate BAMM - Produce evaluation plots and ESS data.
# 2.4/ Import BAMM outputs - Load `BAMM_object` in R and subset posterior samples.
# 2.5/ Clean BAMM files - Remove files generated during the BAMM run.

# All these actions are performed by a single function: deepSTRAPP::prepare_diversification_data()
?deepSTRAPP::prepare_diversification_data()

# Run BAMM workflow with deepSTRAPP
## This step is time-consuming. You can skip it and load directly the result if needed
Ponerinae_BAMM_object_old_calib <- prepare_diversification_data(
   BAMM_install_directory_path = "./software/bamm-2.5.0/", # To adjust to your own path to BAMM
   phylo = Ponerinae_tree_old_calib,
   prefix_for_files = "Ponerinae",
   seed = 1234, # Set seed for reproducibility
   numberOfGenerations = 10^7, # Set high for optimal run, but will take a long time
   BAMM_output_directory_path =  "./BAMM_outputs/")

# Load directly the result
data(Ponerinae_BAMM_object_old_calib)
# This dataset is only available in development versions installed from GitHub.
# It is not available in CRAN versions.
# Use remotes::install_github(repo = "MaelDore/deepSTRAPP") to get the latest development version.

# Explore output
str(Ponerinae_BAMM_object_old_calib, 1)
# Record the regime shift events and macroevolutionary regimes parameters across posterior samples
str(Ponerinae_BAMM_object_old_calib$eventData, 1) 
# Mean speciation rates at tips aggregated across all posterior samples
head(Ponerinae_BAMM_object_old_calib$meanTipLambda) 
# Mean extinction rates at tips aggregated across all posterior samples
head(Ponerinae_BAMM_object_old_calib$meanTipMu) 

# Plot mean net diversification rates and regime shifts on the phylogeny
plot_BAMM_rates(Ponerinae_BAMM_object_old_calib,
                labels = FALSE, legend = TRUE)

## Input needed for Step 3 is the BAMM_object (Ponerinae_BAMM_object)

```


```{r run_deepSTRAPP_cont}
# ------ Step 3: Run a deepSTRAPP workflow ------ #

## Goal: Extract traits, diversification rates and regimes at a given time in the past
# to test for differences with a STRAPP test

# 3.1/ Extract trait data at a given time in the past ('focal_time')
# 3.2/ Extract diversification rates and regimes at a given time in the past ('focal_time')
# 3.3/ Compute STRAPP test
# 3.4/ Repeat previous actions for many timesteps along evolutionary time

# All these actions are performed by a single function:
#  For a single 'focal_time': deepSTRAPP::run_deepSTRAPP_for_focal_time()
#  For multiple 'time_steps': deepSTRAPP::run_deepSTRAPP_over_time()
?deepSTRAPP::run_deepSTRAPP_for_focal_time()
?deepSTRAPP::run_deepSTRAPP_over_time()

## Set for time steps of 5 My. Will generate deepSTRAPP workflows for 0 to 40 Mya.
# nb_time_steps <- 5
time_step_duration <- 5
time_range <- c(0, 40)

# Run deepSTRAPP on net diversification rates
## This step is time-consuming. You can skip it and load directly the result if needed
Ponerinae_deepSTRAPP_cont_old_calib_0_40 <- run_deepSTRAPP_over_time(
    contMap = Ponerinae_contMap,
    ace = Ponerinae_ACE,
    tip_data = Ponerinae_cont_tip_data,
    trait_data_type = "continuous",
    BAMM_object = Ponerinae_BAMM_object_old_calib,
    # nb_time_steps = nb_time_steps,
    time_range = time_range,
    time_step_duration = time_step_duration,
    seed = 1234, # Set seed for reproducibility
    # Needed to obtain STRAPP stats and plot evaluation histograms (See 4.2)
    return_perm_data = TRUE, 
    # Needed to get trait data and plot rates through time (See 4.3)
    extract_trait_data_melted_df = TRUE, 
    # Needed to get diversification data and plot rates through time (See 4.3)
    extract_diversification_data_melted_df = TRUE, 
    # Needed to obtain STRAPP stats and plot evaluation histograms (See 4.2)
    return_STRAPP_results = TRUE, 
    # Needed to plot updated contMaps (See 4.4)
    return_updated_trait_data_with_Map = TRUE, 
    # Needed to map diversification rates on updated phylogenies (See 4.5)
    return_updated_BAMM_object = TRUE, 
    verbose = TRUE,
    verbose_extended = TRUE)

# Load the deepSTRAPP output summarizing results for 0 to 40 My
data(Ponerinae_deepSTRAPP_cont_old_calib_0_40, package = "deepSTRAPP")
# This dataset is only available in development versions installed from GitHub.
# It is not available in CRAN versions.
# Use remotes::install_github(repo = "MaelDore/deepSTRAPP") to get the latest development version.

## Explore output
str(Ponerinae_deepSTRAPP_cont_old_calib_0_40, max.level = 1)

# See next step for how to generate plots from those outputs

# Display test summary
# Can be passed down to [deepSTRAPP::plot_STRAPP_pvalues_over_time()] to generate a plot
# showing the evolution of the test results across time
Ponerinae_deepSTRAPP_cont_old_calib_0_40$pvalues_summary_df

# Access STRAPP test results
# Can be passed down to [deepSTRAPP::plot_histograms_STRAPP_tests_over_time()] to generate plot
# showing the null distribution of the test statistics
str(Ponerinae_deepSTRAPP_cont_old_calib_0_40$STRAPP_results, max.level = 2)

# Access trait data in a melted data.frame
head(Ponerinae_deepSTRAPP_cont_old_calib_0_40$trait_data_df_over_time)
# Access the diversification data in a melted data.frame
head(Ponerinae_deepSTRAPP_cont_old_calib_0_40$diversification_data_df_over_time)
# Both can be passed down to [deepSTRAPP::plot_rates_through_time()] to generate a plot
# showing the evolution of diversification rates though time in relation to trait values

# Access updated contMaps for each focal time
# Can be used to plot contMap with branch cut-off at focal time with [deepSTRAPP::plot_contMap()]
str(Ponerinae_deepSTRAPP_cont_old_calib_0_40$updated_trait_data_with_Map_over_time, max.level = 2)

# Access updated BAMM_object for each focal time
# Can be used to map rates and regime shifts on phylogeny with branch cut-off 
# at focal time with [deepSTRAPP::plot_BAMM_rates()]
str(Ponerinae_deepSTRAPP_cont_old_calib_0_40$updated_BAMM_objects_over_time, max.level = 2)

## Input needed for Step 4 is the deepSTRAPP object (Ponerinae_deepSTRAPP_cont_old_calib_0_40)

```


```{r plot_pvalues_cont}
# ------ Step 4: Plot results ------ #

## Goal: Summarize the outputs in meaningful plots

# 4.1/ Plot evolution of STRAPP tests p-values through time
# 4.2/ Plot histogram of STRAPP test stats
# 4.3/ Plot evolution of rates through time in relation to trait values
# 4.4/ Plot rates vs. trait values across branches for a given 'focal_time'
# 4.5/ Plot updated densityMaps mapping trait evolution for a given 'focal_time'
# 4.6/ Plot updated diversification rates and regimes for a given 'focal_time'
# 4.7/ Combine 4.5 and 4.6 to plot both mapped phylogenies with trait evolution (4.5)
#      and diversification rates and regimes (4.6).

# Each plot is achieved through a dedicated function

# Load the deepSTRAPP output summarizing results for 0 to 40 My
data(Ponerinae_deepSTRAPP_cont_old_calib_0_40, package = "deepSTRAPP")
# This dataset is only available in development versions installed from GitHub.
# It is not available in CRAN versions.
# Use remotes::install_github(repo = "MaelDore/deepSTRAPP") to get the latest development version.

### 4.1/ Plot evolution of STRAPP tests p-values through time ####

# ?deepSTRAPP::plot_STRAPP_pvalues_over_time()

## Plot results of Spearman's tests over time
deepSTRAPP::plot_STRAPP_pvalues_over_time(
   deepSTRAPP_outputs = Ponerinae_deepSTRAPP_cont_old_calib_0_40)

# This is the main output of deepSTRAPP. It shows the evolution of the significance
# of the STRAPP tests over time.
# This example highlights the importance of deepSTRAPP as the significance of STRAPP tests
# change over time. 
# Correlation between trait values and net diversification rates are not significant in the present
# (assuming a significant threshold of alpha = 0.05).
# Meanwhile, correlations were significant in the past between 5 My to 25 My (the green area).
# This result supports the idea that differences in biodiversity in relation to trait values
# (e.g., ant size) can be explained by correlations between rates and net diversification rates
# that occurred in the past. Without use of deepSTRAPP, this conclusion would not have been supported
# by current diversification rates alone.


```
```{r plot_pvalues_cont_eval_dev, eval = is_dev_version(), echo = FALSE}

# Load the deepSTRAPP output summarizing results for 0 to 40 My
data(Ponerinae_deepSTRAPP_cont_old_calib_0_40, package = "deepSTRAPP")

# Produce the results of overall Kruskal-Wallis tests over time
ggplot_STRAPP_pvalues <- deepSTRAPP::plot_STRAPP_pvalues_over_time(
   deepSTRAPP_outputs = Ponerinae_deepSTRAPP_cont_old_calib_0_40,
   display_plot = FALSE)
# Adjust main title size
ggplot_STRAPP_pvalues <- ggplot_STRAPP_pvalues +
  ggplot2::theme(plot.title = ggplot2::element_text(size = 18))
# Print plot
print(ggplot_STRAPP_pvalues)

```
```{r plot_pvalues_cont_eval_CRAN, eval = !is_dev_version(), echo = FALSE, out.width = "100%"}

# Plot pre-rendered graph
knitr::include_graphics("figures/1.1_deepSTRAPP_continuous_data_4.1_plot_pvalues.PNG")

```

```{r plot_histogram_STRAPP_tests_cont}
### 4.2/ Plot histogram of STRAPP test stats ####

# Plot an histogram of the distribution of the test statistics used to assess
# the significance of STRAPP tests
  #  For a single 'focal_time': deepSTRAPP::plot_histogram_STRAPP_test_for_focal_time()
  #  For multiple 'time_steps': deepSTRAPP::plot_histograms_STRAPP_tests_over_time()

# ?deepSTRAPP::plot_histogram_STRAPP_test_for_focal_time
# ?deepSTRAPP::plot_histograms_STRAPP_tests_over_time

## These functions are used to provide visual illustration of the results of each STRAPP test. 
# They can be used to complement the provision of the statistical results summarized in Step 3.

# Display the time-steps
Ponerinae_deepSTRAPP_cont_old_calib_0_40$time_steps

# Plot the histogram of test stats for time-step n°5 = 20 My
plot_histogram_STRAPP_test_for_focal_time(
   deepSTRAPP_outputs = Ponerinae_deepSTRAPP_cont_old_calib_0_40,
   focal_time = 20)

# The black line represents the expected value under the null hypothesis H0 => Δ abs(Spearman rho stat) = 0.
# The histogram shows the distribution of the test statistics as observed
# across the 1000 posterior samples from BAMM.
# The red line represents the significance threshold for which 95% of the observed data
# exhibited a higher value than expected.
# Since this red line is above the null expectation (quantile 5% = 0.036), 
# the test is significant for a value of alpha = 0.05.
# Therefore, ant size was significantly correlated with net diversification rates 20 Mya.
# Since we performed a two-tailed test (default), we do not know the direction of this correlation (yet).

# Plot the histograms of test stats for all time-steps
plot_histograms_STRAPP_tests_over_time(
   deepSTRAPP_outputs = Ponerinae_deepSTRAPP_cont_old_calib_0_40)

```
```{r plot_histogram_STRAPP_tests_cont_eval_dev, fig.width = 8.5, fig.height = 6, out.width = "100%", eval = is_dev_version(), echo = FALSE}

# Plot the histogram of test stats for time-step n°5 = 20 My
plot_histogram_STRAPP_test_for_focal_time(
   deepSTRAPP_outputs = Ponerinae_deepSTRAPP_cont_old_calib_0_40,
   focal_time = 20)

```
```{r plot_histogram_STRAPP_tests_cont_eval_CRAN, eval = !is_dev_version(), echo = FALSE, out.width = "100%"}

# Plot pre-rendered graph
knitr::include_graphics("figures/1.1_deepSTRAPP_continuous_data_4.2_plot_STRAPP_tests.PNG")

```

```{r plot_rates_through_time_cont}
### 4.3/ Plot evolution of rates through time ~ trait data ####

# ?deepSTRAPP::plot_rates_through_time()

# Generate ggplot
plot_rates_through_time(deepSTRAPP_outputs = Ponerinae_deepSTRAPP_cont_old_calib_0_40,
                        plot_CI = TRUE)

# This plot helps to visualize how correlations between trait values and rates evolved over time.
# Here, we observe a "negative" correlation as ants in the lowest quartile of trait values (in blue)
# display the highest net diversification rates over time, while ants in the highest quartile
# of trait values (in red) display the lowest net diversification rates over time.
# However, in the present, we recorded an increase in diversification rates that blurred 
# these differences and led to a non-significant STRAPP test when comparing current rates.
# This plot, alongside results of deepSTRAPP, supports the Diversification Rate Hypothesis
# in showing how ant lineages with low trait values (e.g., small size) may have accumulated faster
# than ant lineages with high trait value (e.g., large size), especially between 5 to 25 My.

# N.B.: The increase of diversification rates recorded in the present may largely be artifactual,
# due to the fact some lineages in the present will go extinct in the future,
# but have not yet been recorded as such. 
# This bias is named the "pull of the present", and can impair evaluation of 
# the Diversification Rate Hypothesis based only on current rates.
# deepSTRAPP offers a solution to this issue by investigating rate differences at any time in the past.


```
```{r plot_rates_through_time_cont_eval_dev, fig.width = 8.5, out.width = "100%", eval = is_dev_version(), echo = FALSE}

# Produce RTT plot
ggplot_RTT_list <- plot_rates_through_time(deepSTRAPP_outputs = Ponerinae_deepSTRAPP_cont_old_calib_0_40,
                        plot_CI = TRUE, display_plot = FALSE)
# Adjust title size
ggplot_RTT <- ggplot_RTT_list$rates_TT_ggplot +
  ggplot2::theme(plot.title = ggplot2::element_text(size = 18),
                 axis.title = ggplot2::element_text(size = 16))
# Print plot
print(ggplot_RTT)

```
```{r plot_rates_through_time_cont_eval_CRAN, eval = !is_dev_version(), echo = FALSE, out.width = "100%"}

# Plot pre-rendered graph
knitr::include_graphics("figures/1.1_deepSTRAPP_continuous_data_4.3_plot_rates_through_time.PNG")

```

```{r plot_rates_vs_traits_cont}
### 4.4/ Plot rates vs. ranges across branches for a given focal time ####

# ?deepSTRAPP::plot_rates_vs_trait_data_for_focal_time()
# ?deepSTRAPP::plot_rates_vs_trait_data_over_time()

# This plot help to visualize differences in rates vs. ranges across all branches
# found at specific time-steps (i.e., 'focal_time').

# Generate ggplot for time = 20 My
plot_rates_vs_trait_data_for_focal_time(
   deepSTRAPP_outputs = Ponerinae_deepSTRAPP_cont_old_calib_0_40,
   focal_time = 20,
   color_scale = color_scale)

# Here we focus on T = 20 My to highlight the correlation detected in the previous steps.
# You can see that ants in the highest trait values (in red) exhibits the lowest rates, at this time-step.
# This plot, alongside other results of deepSTRAPP, supports the Diversification Rate Hypothesis in showing 
# how ant lineages with low trait values (e.g., small size) may have accumulated faster
# than ant lineages with high trait value (e.g., large size), especially between 5 to 25 My.
# Additionally, the plot displays summary statistics for the STRAPP test associated with the data shown:
#   * An observed statistic computed across the mean rates and trait values shown on the plot.
#     Here, rho obs = -0.743, indicating a negative correlation between size and diversification in ponerine ants.
#     This is not the statistic of the STRAPP test itself, which is conducted across all BAMM posterior samples.
#   * The quantile of null statistic distribution at the significant threshold used to define test significance. 
#     The test will be considered significant (i.e., the null hypothesis is rejected)
#     if this value is higher than zero, as shown on the histogram in Section 4.2.
#     Here, Q5% = 0.036, so the test is significant (according to this significance threshold).
#   * The p-value of the associated STRAPP test. Here, p = 0.022.

# Plot rates vs. ranges for all time-steps
plot_rates_vs_trait_data_over_time(
   deepSTRAPP_outputs = Ponerinae_deepSTRAPP_cont_old_calib_0_40,
   color_scale = color_scale)

```
```{r plot_rates_vs_traits_cont_eval_dev, fig.height = 7, fig.width = 8.5, out.width = "100%", eval = is_dev_version(), echo = FALSE}
# Select a color scheme from lowest to highest values
color_scale = c("darkgreen", "limegreen", "orange", "red")

# Generate ggplot for time = 20 My
plot_rates_vs_trait_data_for_focal_time(
   deepSTRAPP_outputs = Ponerinae_deepSTRAPP_cont_old_calib_0_40,
   focal_time = 20,
   color_scale = color_scale)
```
```{r plot_rates_vs_traits_cont_eval_CRAN, eval = !is_dev_version(), echo = FALSE, out.width = "100%"}

# Plot pre-rendered graph
knitr::include_graphics("figures/1.1_deepSTRAPP_continuous_data_4.4_plot_rates_vs_traits.PNG")

```


```{r plot_updated_contMap_cont, eval = FALSE, echo = TRUE}
### 4.5/ Plot updated contMap mapping trait evolution for a given 'focal_time' ####

# ?deepSTRAPP::plot_contMap()

## These plots help to visualize the evolution of trait values across the phylogeny, 
## and to focus on tip values at specific time-steps.

# Display the time-steps
Ponerinae_deepSTRAPP_cont_old_calib_0_40$time_steps

# Extract root age
root_age <- max(phytools::nodeHeights(Ponerinae_tree_old_calib)[,2])

## The next plot shows the evolution of trait values across the whole phylogeny (100-0 My).

# Plot initial contMap (t = 0)
contMap_0My <- Ponerinae_deepSTRAPP_cont_old_calib_0_40$updated_trait_data_with_Map_over_time[[1]]
plot_contMap(contMap_0My$contMap,
             color_scale = c("darkgreen", "limegreen", "orange", "red"),
             lwd = 0.7, # Adjust width of branches
             fsize = c(0.1, 1)) # Reduce tip label size
abline(v = root_age - 20, col = "red", lty = 2) # Show where the phylogeny will be cut

## The next plot shows the evolution of trait values from root to 20Mya (100-20 My).

# Plot updated contMap for time-step n°5 = 20 My
contMap_20My <- Ponerinae_deepSTRAPP_cont_old_calib_0_40$updated_trait_data_with_Map_over_time[[5]]
plot_contMap(contMap_20My$contMap,
             color_scale = c("darkgreen", "limegreen", "orange", "red"),
             lwd = 0.9, # Adjust width of branches
             fsize = c(0.2, 1)) # Reduce tip label size
```
```{r plot_updated_contMap_cont_eval_dev, eval = is_dev_version(), echo = TRUE}

# Extract root age
root_age <- max(phytools::nodeHeights(Ponerinae_tree_old_calib)[,2])

## The next plot shows the evolution of trait values across the whole phylogeny (100-0 My).

# Plot initial contMap (t = 0)
contMap_0My <- Ponerinae_deepSTRAPP_cont_old_calib_0_40$updated_trait_data_with_Map_over_time[[1]]
plot_contMap(contMap_0My$contMap,
             color_scale = c("darkgreen", "limegreen", "orange", "red"),
             lwd = 0.7, # Adjust width of branches
             fsize = c(0.1, 1)) # Reduce tip label size
abline(v = root_age - 20, col = "red", lty = 2) # Show where the phylogeny will be cut

## The next plot shows the evolution of trait values from root to 20Mya (100-20 My).

# Plot updated contMap for time-step n°5 = 20 My
contMap_20My <- Ponerinae_deepSTRAPP_cont_old_calib_0_40$updated_trait_data_with_Map_over_time[[5]]
plot_contMap(contMap_20My$contMap,
             color_scale = c("darkgreen", "limegreen", "orange", "red"),
             lwd = 0.9, # Adjust width of branches
             fsize = c(0.2, 1)) # Reduce tip label size
```
```{r plot_updated_contMap_cont_eval_CRAN, eval = !is_dev_version(), echo = FALSE, out.width = "100%"}

# Plot pre-rendered graph
knitr::include_graphics("figures/1.1_deepSTRAPP_continuous_data_4.5_plot_updated_contMap_1.PNG")
knitr::include_graphics("figures/1.1_deepSTRAPP_continuous_data_4.5_plot_updated_contMap_2.PNG")

```

```{r plot_BAMM_rates_cont}
### 4.6/ Plot updated diversification rates and regimes for a given 'focal_time' ####

# ?deepSTRAPP::plot_BAMM_rates()

## These plots help to visualize the evolution of diversification rates across the phylogeny,
## and to focus on tip values at specific time-steps.

# Display the time-steps
Ponerinae_deepSTRAPP_cont_old_calib_0_40$time_steps

# Extract root age
root_age <- max(phytools::nodeHeights(Ponerinae_tree_old_calib)[,2])

## The next plot shows the evolution of net diversification rates across the whole phylogeny (100-0 My).

# Plot diversification rates on initial phylogeny (t = 0)
BAMM_map_0My <- Ponerinae_deepSTRAPP_cont_old_calib_0_40$updated_BAMM_objects_over_time[[1]]
plot_BAMM_rates(BAMM_map_0My, labels = FALSE, par.reset = FALSE)
abline(v = root_age - 20, col = "red", lty = 2) # Show where the phylogeny will be cut
title(main = "BAMM rates for 100-0 My")

## The next plot shows the evolution of net diversification rates from root to 20 Mya (100-20 My).

# Plot diversification rates on updated phylogeny for time-step n°5 = 20 My
BAMM_map_20My <- Ponerinae_deepSTRAPP_cont_old_calib_0_40$updated_BAMM_objects_over_time[[5]]
plot_BAMM_rates(BAMM_map_20My, labels = FALSE,
                colorbreaks = BAMM_map_20My$initial_colorbreaks$net_diversification)
title(main = "BAMM rates for 100-20 My")

```
```{r plot_BAMM_rates_cont_eval_dev, eval = is_dev_version(), echo = FALSE}
old_par <- par(no.readonly = TRUE)
par(mfrow = c(1, 2))

# Plot diversification rates on initial phylogeny (t = 0)
BAMM_map_0My <- Ponerinae_deepSTRAPP_cont_old_calib_0_40$updated_BAMM_objects_over_time[[1]]
plot_BAMM_rates(BAMM_map_0My, labels = FALSE, legend = TRUE, par.reset = FALSE)
abline(v = max(phytools::nodeHeights(Ponerinae_tree_old_calib)[,2]) - 20, col = "red", lty = 2) # Show where the phylogeny will be cut
title(main = "BAMM rates for 100-0 My")

# Plot diversification rates on updated phylogeny for time-step n°5 = 20 My
BAMM_map_20My <- Ponerinae_deepSTRAPP_cont_old_calib_0_40$updated_BAMM_objects_over_time[[5]]
plot_BAMM_rates(BAMM_map_20My, labels = FALSE, legend = TRUE,
                colorbreaks = BAMM_map_20My$initial_colorbreaks$net_diversification)
title(main = "BAMM rates for 100-20 My")

par(old_par)
```
```{r plot_BAMM_rates_cont_eval_CRAN, eval = !is_dev_version(), echo = FALSE, out.width = "100%"}

# Plot pre-rendered graph
knitr::include_graphics("figures/1.1_deepSTRAPP_continuous_data_4.6_plot_BAMM_rates.PNG")

```


```{r plot_traits_vs_rate_maps_cont}
### 4.7/ Plot both trait evolution and diversification rates and regimes updated for a given 'focal_time' ####

# ?deepSTRAPP::plot_traits_vs_rates_on_phylogeny_for_focal_time()

## These plots help to visualize simultaneously the evolution of trait and diversification rates
## across the phylogeny, and to focus on tip values at specific time-steps.

# Display the time-steps
Ponerinae_deepSTRAPP_cont_old_calib_0_40$time_steps

## The next plot shows the evolution of trait values and rates across the whole phylogeny (100-0 My).

# Plot diversification rates on initial phylogeny (t = 0)
plot_traits_vs_rates_on_phylogeny_for_focal_time(
  deepSTRAPP_outputs = Ponerinae_deepSTRAPP_cont_old_calib_0_40,
  focal_time = 0,
  ftype = "off", lwd = 0.7,
  color_scale = c("darkgreen", "limegreen", "orange", "red"),
  labels = FALSE, legend = FALSE,
  par.reset = FALSE)

## The next plot shows the evolution of trait values and rates from root to 20 Mya (100-20 My).

# Plot diversification rates on updated phylogeny for time-step n°5 = 20 My
plot_traits_vs_rates_on_phylogeny_for_focal_time(
  deepSTRAPP_outputs = Ponerinae_deepSTRAPP_cont_old_calib_0_40,
  focal_time = 20, 
  ftype = "off", lwd = 1.2,
  color_scale = c("darkgreen", "limegreen", "orange", "red"),
  labels = FALSE, legend = FALSE,
  par.reset = FALSE)

```
```{r plot_traits_vs_rate_maps_cont_eval_dev, fig.height = 7, eval = is_dev_version(), echo = FALSE}
# Plot diversification rates on initial phylogeny (t = 0)
plot_traits_vs_rates_on_phylogeny_for_focal_time(
  deepSTRAPP_outputs = Ponerinae_deepSTRAPP_cont_old_calib_0_40,
  focal_time = 0,
  ftype = "off", lwd = 0.7,
  color_scale = c("darkgreen", "limegreen", "orange", "red"),
  labels = FALSE, legend = FALSE,
  par.reset = FALSE)

# Plot diversification rates on updated phylogeny for time-step n°5 = 20 My
plot_traits_vs_rates_on_phylogeny_for_focal_time(
  deepSTRAPP_outputs = Ponerinae_deepSTRAPP_cont_old_calib_0_40,
  focal_time = 20, 
  ftype = "off", lwd = 1.2,
  color_scale = c("darkgreen", "limegreen", "orange", "red"),
  labels = FALSE, legend = FALSE,
  par.reset = FALSE)
```
```{r plot_traits_vs_rate_maps_cont_eval_CRAN, eval = !is_dev_version(), echo = FALSE, out.width = "100%"}

# Plot pre-rendered graph
knitr::include_graphics("figures/1.1_deepSTRAPP_continuous_data_4.7_plot_traits_vs_rate_maps_1.PNG")
knitr::include_graphics("figures/1.1_deepSTRAPP_continuous_data_4.7_plot_traits_vs_rate_maps_2.PNG")

```