---
title: "deepSTRAPP: Categorical trait data"
author: "Maël Doré"
date: "`r Sys.Date()`"
output: rmarkdown::html_vignette
vignette: >
  %\VignetteIndexEntry{deepSTRAPP: Categorical 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)
  fig.align = "center"
)
```

```{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 __differences__ in diversification rates between multiple (three) __categorical states__ 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 __continuous data__, see this vignette: `vignette("deepSTRAPP_continuous_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_cat_3lvl}
# ------ 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 categorical data with 3-levels
Ponerinae_cat_3lvl_tip_data <- setNames(object = Ponerinae_trait_tip_data$fake_cat_3lvl_tip_data,
                                    nm = Ponerinae_trait_tip_data$Taxa)
# Here, data represent three types of habitats
table(Ponerinae_cat_3lvl_tip_data)

## Select color scheme for states (i.e., habitats)
colors_per_states <- c("forestgreen", "sienna", "goldenrod")
names(colors_per_states) <- c("arboreal", "subterranean", "terricolous")

## 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_cat_3lvl_tip_data)

## Check that trait data and phylogeny are named and ordered similarly
all(names(Ponerinae_cat_3lvl_tip_data) == Ponerinae_tree_old_calib$tip.label)

## Reorder trait data as in phylogeny
Ponerinae_cat_3lvl_tip_data <- Ponerinae_cat_3lvl_tip_data[match(x = Ponerinae_tree_old_calib$tip.label,
                                                                 table = names(Ponerinae_cat_3lvl_tip_data))]

## Plot data on tips for visualization
pdf(file = "./Ponerinae_cat_3lvl_data_old_calib_on_phylo.pdf", width = 20, height = 150)

# Set plotting parameters
old_par <- par(no.readonly = TRUE)
par(mar = c(0.1,0.1,0.1,0.1), oma = c(0,0,0,0)) # bltr

# Graph presence/absence using plotTree.datamatrix
range_map <- phytools::plotTree.datamatrix(
  tree = Ponerinae_tree_old_calib,
  X = as.data.frame(Ponerinae_cat_3lvl_tip_data),
  fsize = 0.7, yexp = 1.1,
  header = TRUE, xexp = 1.25,
  colors = colors_per_states)

# Get plot info in "last_plot.phylo"
plot_info <- get("last_plot.phylo", envir=.PlotPhyloEnv)

# Add time line

# Extract root age
root_age <- max(phytools::nodeHeights(Ponerinae_tree_old_calib))

# Define ticks
# ticks_labels <- seq(from = 0, to = 100, by = 20)
ticks_labels <- seq(from = 0, to = 120, by = 20)
axis(side = 1, pos = 0, at = (-1 * ticks_labels) + root_age, labels = ticks_labels, cex.axis = 1.5)
legend(x = root_age/2,
       y = 0 - 5, adj = 0,
       bty = "n", legend = "", title = "Time  [My]", title.cex = 1.5)

# Add a legend
legend(x = plot_info$x.lim[2] - 10,
       y = mean(plot_info$y.lim),
       # adj = c(0,0),
       # x = "topleft",
       legend = c("Absence", "Presence"),
       pch = 22, pt.bg = c("white","gray30"), pt.cex =  1.8,
       cex = 1.5, bty = "n")

dev.off()

# Reset plotting parameters
par(old_par)

## Inputs needed for Step 1 are the tip_data (Ponerinae_cat_3lvl_tip_data) and the phylogeny
## (Ponerinae_tree_old_calib), and optionally, a color scheme (colors_per_states).

```
```{r load_data_cat_3lvl_eval, eval = TRUE, echo = FALSE}
## Select color scheme for states
colors_per_states <- c("forestgreen", "sienna", "goldenrod")
names(colors_per_states) <- c("arboreal", "subterranean", "terricolous")
```

```{r prepare_trait_data_cat_3lvl}
# ------ 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/ Run stochastic mapping simulations to generate evolutionary histories
#      compatible with the best model and inferred ACE.
# 1.5/ Infer ancestral states along branches.
#  - Compute posterior frequencies of each state to produce a `densityMap` for each state.

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 categorical trait, an ARD model is assumed by default.
Ponerinae_trait_object <- prepare_trait_data(
   tip_data = Ponerinae_cat_3lvl_tip_data,
   phylo = Ponerinae_tree_old_calib,
   trait_data_type = "categorical",
   colors_per_levels = colors_per_states,
   nb_simulations = 100, # Reduce number of simulations to save time
   seed = 1234) # Set seed for reproducibility

# Explore output
str(Ponerinae_trait_object, 1)

# Extract the densityMaps representing the posterior probabilities of states on the phylogeny
Ponerinae_densityMaps <- Ponerinae_trait_object$densityMaps

# Plot ancestral states as a single continuously mapped phylogeny overlaying
# all state posterior probabilities
plot_densityMaps_overlay(Ponerinae_densityMaps,
                         colors_per_levels = colors_per_states)

# Plot posterior probabilities of each state on an independent densityMap
# Plot densityMap for state = "arboreal"
plot(Ponerinae_densityMaps[[1]])
# Plot densityMap for state = "subterranean"
plot(Ponerinae_densityMaps[[2]])
# Plot densityMap for state = "terricolous"
plot(Ponerinae_densityMaps[[3]])

# 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 densityMaps, and optionally, the tip_data 
## (Ponerinae_cat_3lvl_tip_data), and the ACE (Ponerinae_ACE)


```


```{r prepare_diversification_data_cat_3lvl}
# ------ 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_old_calib",
   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_cat_3lvl}
# ------ 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

# Because we have three levels as trait data, two types of tests can be performed:
#  - Overall Kruskal-Wallis tests that test for rate differences across all states at once.
#  - post hoc pairwise Dunn's tests that test for rate differences between pairs of states.
# Here, we select 'posthoc_pairwise_tests = TRUE' to conduct post hoc pairwise tests
# in addition to overall Kruskal-Wallis tests.

# 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 five time steps of 5 My. Will generate deepSTRAPP workflows for 0 to 40 Mya.
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_cat_3lvl_old_calib_0_40 <- run_deepSTRAPP_over_time(
    densityMaps = Ponerinae_densityMaps,
    ace = Ponerinae_ACE,
    tip_data = Ponerinae_cat_3lvl_data,
    trait_data_type = "categorical",
    BAMM_object = Ponerinae_BAMM_object_old_calib,
    time_range = time_range,
    time_step_duration = time_step_duration,
    seed = 1234, # Set seed for reproducibility
    alpha = 0.10, # Set significance threshold to use for tests
    posthoc_pairwise_tests = TRUE, # To run pairwise posthoc tests between pairs of states
    # 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 densityMaps (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_cat_3lvl_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_cat_3lvl_old_calib_0_40, max.level = 1)

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

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

# For overall Kruskal-Wallis tests over time-steps
Ponerinae_deepSTRAPP_cat_3lvl_old_calib_0_40$pvalues_summary_df
# For posthoc pairwise Dunn's tests over time-steps
Ponerinae_deepSTRAPP_cat_3lvl_old_calib_0_40$pvalues_summary_df_for_posthoc_pairwise_tests

# 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

# For overall Kruskal-Wallis tests over time-steps
str(Ponerinae_deepSTRAPP_cat_3lvl_old_calib_0_40$STRAPP_results, max.level = 2)
# For posthoc pairwise Dunn's tests over time-steps
# Results are found in the '$posthoc_pairwise_tests' element of each 'STRAPP_result'.
str(lapply(X = Ponerinae_deepSTRAPP_cat_3lvl_old_calib_0_40$STRAPP_results, 
           FUN = function (x) { x$posthoc_pairwise_tests } ), max.level = 3)

# Access trait data in a melted data.frame
head(Ponerinae_deepSTRAPP_cat_3lvl_old_calib_0_40$trait_data_df_over_time)
# Access the diversification data in a melted data.frame
head(Ponerinae_deepSTRAPP_cat_3lvl_old_calib_0_40$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 densityMaps for each focal time
# Can be used to plot densityMaps with branch cut-off at focal time
# with [deepSTRAPP::plot_densityMaps_overlay()]
str(Ponerinae_deepSTRAPP_cat_3lvl_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_cat_3lvl_old_calib_0_40$updated_BAMM_objects_over_time, max.level = 2)

## Input needed for Step 4 is the deepSTRAPP object (Ponerinae_deepSTRAPP_cat_3lvl_old_calib_0_40)

```


```{r plot_pvalues_cat_3lvl}
# ------ 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. states 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_cat_3lvl_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()

## 4.1.1/ Plot results of overall Kruskal-Wallis tests over time

deepSTRAPP::plot_STRAPP_pvalues_over_time(
   deepSTRAPP_outputs = Ponerinae_deepSTRAPP_cat_3lvl_old_calib_0_40,
   alpha = 0.10)


# This is the main output of deepSTRAPP. They show the evolution of the significance of
# the STRAPP tests over time.
# Here, overall Kruskal-Wallis tests for rate difference across all states (i.e., habitats) are shown.
# This example highlights the importance of deepSTRAPP as the significance of
# STRAPP tests change over time. 
# Differences in net diversification rates are not significant in the present 
# (assuming a significant threshold of alpha = 0.10).
# Meanwhile, rates are significantly different in the past between 5 My to 15 My (the green area).
# This result supports the idea that differences in biodiversity across habitats 
# (i.e., "arboreal" vs. , "subterranean" vs. "terricolous" ants) can be explained 
# by differences of diversification rates that was detected in the past. Without use of deepSTRAPP, 
# this conclusion would not have been supported by current diversification rates alone.

# Note: This is NOT true ecological data. It is not a valid scientific result,
# but an illustration of the use of deepSTRAPP.

# A next step is to look in details into rate differences across pairs of states (i.e., habitats).
# For this, we can plot the results of the post hoc pairwise tests.

## 4.1.2/ Plot results of posthoc pairwise Dunn's tests over time

deepSTRAPP::plot_STRAPP_pvalues_over_time(
   deepSTRAPP_outputs = Ponerinae_deepSTRAPP_cat_3lvl_old_calib_0_40,
   plot_posthoc_tests = TRUE) # To plot results of post hoc pairwise tests instead

# Here, post hoc pairwise Dunn's tests for rate difference between pairs of states are shown.
# These results show that differences in rates were only detected between "arboreal" 
# and "terricolous" ants between 2 My to 15 My (the green area), providing more detailed insights on
# how type of habitats may affect diversification rates.
# Note: This is NOT true ecological data. It is not a valid scientific result, 
# but an illustration of the use of deepSTRAPP.
# This highlights the critical use of deepSTRAPP in revealing differences in diversification rates
# occurring in the past, that may drive current biodiversity patterns.

```
```{r plot_pvalues_cat_3lvl_eval_dev, eval = is_dev_version(), echo = FALSE}

# Load the deepSTRAPP output summarizing results for 0 to 40 My
data(Ponerinae_deepSTRAPP_cat_3lvl_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_cat_3lvl_old_calib_0_40,
   alpha = 0.10, 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)

# Produce the results of posthoc pairwise Dunn's tests over time
ggplot_STRAPP_pvalues_posthoc <- deepSTRAPP::plot_STRAPP_pvalues_over_time(
   deepSTRAPP_outputs = Ponerinae_deepSTRAPP_cat_3lvl_old_calib_0_40,
   plot_posthoc_tests = TRUE, # To plot results of post hoc pairwise tests instead
   display_plot = FALSE)
# Adjust main title size + legend
ggplot_STRAPP_pvalues_posthoc <- ggplot_STRAPP_pvalues_posthoc +
  ggplot2::theme(
    plot.title = ggplot2::element_text(size = 18),
    legend.title = ggplot2::element_text(size  = 12),
    legend.position.inside = c(0.30, 0.40),
    legend.text = ggplot2::element_text(size = 9))
# Print plot
print(ggplot_STRAPP_pvalues_posthoc)

```
```{r plot_pvalues_cat_3lvl_eval_CRAN, eval = !is_dev_version(), echo = FALSE, out.width = "100%"}

# Plot pre-rendered graph
knitr::include_graphics("figures/1.2_deepSTRAPP_categorical_3lvl_data_4.1_plot_pvalues_1.PNG")
knitr::include_graphics("figures/1.2_deepSTRAPP_categorical_3lvl_data_4.1_plot_pvalues_2.PNG")

```

```{r plot_histogram_STRAPP_tests_overall_cat_3lvl}
### 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_cat_3lvl_old_calib_0_40$time_steps

## 4.2.1/ Plot results from overall Kruskal-Wallis tests across all states ####

# Plot the histogram of overall Kruskal-Wallis stats for time-step n°3 = 10 My
plot_histogram_STRAPP_test_for_focal_time(
   deepSTRAPP_outputs = Ponerinae_deepSTRAPP_cat_3lvl_old_calib_0_40,
   focal_time = 10)

# The black line represents the expected value under the null hypothesis H0 
#   => Δ Kruskal-Wallis H-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 90% of the observed data
# exhibited a higher value than expected.
# Since this red line is below the null expectation (quantile 10% = 6.942), 
# the test is significant for a value of alpha = 0.10.
# However, this significance must be discussed in regards to the relatively generous
# significance threshold chosen here (alpha = 0.10).

# Plot the histograms of overall Kruskal-Wallis stats for all time-steps
plot_histograms_STRAPP_tests_over_time(
   deepSTRAPP_outputs = Ponerinae_deepSTRAPP_cat_3lvl_old_calib_0_40)

## 4.2.2/ Plot results from posthoc pairwise Dunn's tests between pairs of states ####

# Plot the histogram of posthoc pairwise Dunn's stats for time-step n°3 = 10 My
plot_histogram_STRAPP_test_for_focal_time(
   deepSTRAPP_outputs = Ponerinae_deepSTRAPP_cat_3lvl_old_calib_0_40,
   plot_posthoc_tests = TRUE, # To plot results of post hoc pairwise tests instead
   focal_time = 10)

# Each facet represent a pairwise post hoc test conducted across a given pair of states.
# In each facet, the black line represents the expected value under the null hypothesis H0
#  => Δ Dunn's Z-stat = 0.
# The red line represents the significance threshold for which 90% of the observed data 
# exhibited a higher value than expected.
# This red line is below the null expectation for the "arboreal != subterranean" and 
# "subterranean != terricolous" pairs. This means the test is not significant for these pairs of habitats.
# The red line is above the null expectation for the "arboreal != terricolous" pair
# (Q10% = 1.695, p = 0.025). This means the test is significant for this pair of habitat.
# This is the pair that is driving the significance detected in the previous plot
# when looking at differences across all habitats.
# This significance must still be discussed in regards to the relatively generous
# significance threshold chosen here (alpha = 0.10).

# Plot the histograms of posthoc pairwise Dunn's stats for all time-steps
plot_histograms_STRAPP_tests_over_time(
   deepSTRAPP_outputs = Ponerinae_deepSTRAPP_cat_3lvl_old_calib_0_40,
   plot_posthoc_tests = TRUE) # To plot results of post hoc pairwise tests instead

```
```{r plot_histogram_STRAPP_tests_cat_3lvl_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°3 = 10 My
plot_histogram_STRAPP_test_for_focal_time(
   deepSTRAPP_outputs = Ponerinae_deepSTRAPP_cat_3lvl_old_calib_0_40,
   focal_time = 10)

# Plot the histogram of test stats for time-step n°3 = 10 My
ggplot_STRAPP_pvalues_posthoc <- plot_histogram_STRAPP_test_for_focal_time(
   deepSTRAPP_outputs = Ponerinae_deepSTRAPP_cat_3lvl_old_calib_0_40,
   plot_posthoc_tests = TRUE, # To plot results of post hoc pairwise tests instead
   focal_time = 10)
```
```{r plot_histogram_STRAPP_tests_cat_3lvl_eval_CRAN, eval = !is_dev_version(), echo = FALSE, out.width = "100%"}

# Plot pre-rendered graph
knitr::include_graphics("figures/1.2_deepSTRAPP_categorical_3lvl_data_4.2_plot_histograms_1.PNG")
knitr::include_graphics("figures/1.2_deepSTRAPP_categorical_3lvl_data_4.2_plot_histograms_2.PNG")

```

```{r plot_rates_through_time_cat_3lvl}
### 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_cat_3lvl_old_calib_0_40,
                        colors_per_levels = colors_per_states,
                        plot_CI = TRUE)

# This plot helps to visualize how differences in rates evolved over time.

# You can see that both type of ants "arboreal" and "terricolous" had fairly different rates over time,
# with differences detected as significant between 2 to 15 My.
# Meanwhile, "subterranean" ants exhibited intermediate diversification levels.
# This plot, alongside results of deepSTRAPP, supports the Diversification Rate Hypothesis in showing
# how "terricolous" ant lineages may have accumulated faster, especially between 2 to 15 My.
# It hints that "terricolous" ant lineages are fairly recent as no lineage in this state/habitat
# is inferred to have existed before 25 Mya.
# The larger uncertainty across estimates of diversification rates for "terricolous" ant lineages
# also hints at their relatively lower number due to their recent emergence.

# Note: This is NOT true ecological data. It is not a valid scientific result,
# but an illustration of the use of deepSTRAPP.

```
```{r plot_rates_through_time_cat_3lvl_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_cat_3lvl_old_calib_0_40,
                        colors_per_levels = colors_per_states,
                        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_cat_3lvl_eval_CRAN, eval = !is_dev_version(), echo = FALSE, out.width = "100%"}

# Plot pre-rendered graph
knitr::include_graphics("figures/1.2_deepSTRAPP_categorical_3lvl_data_4.3_plot_rates_through_time.PNG")

```

```{r plot_rates_vs_traits_cat_3lvl}
### 4.4/ Plot rates vs. states 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. states across all branches
# found at specific time-steps (i.e., 'focal_time').

# Generate ggplot for time = 10 My
plot_rates_vs_trait_data_for_focal_time(
   deepSTRAPP_outputs = Ponerinae_deepSTRAPP_cat_3lvl_old_calib_0_40,
   focal_time = 10,
   colors_per_levels = colors_per_states)

# Here we focus on T = 10 My to highlight the differences detected in the previous steps.
# You can see that "terricolous" ants tend to have higher rates than "subterranean" ants,
# who tends to have higher rates than "arboreal" ants, at this time-step.
# This plot, alongside other results of deepSTRAPP, supports the Diversification Rate Hypothesis in showing 
# how "terricolous" ant lineages may have accumulated faster, especially between 5 to 15 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 states (i.e., habitats) shown on the plot.
#     Here, H-stat obs = 374.82. Please note that 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, Q10% = 6.942, so the test is significant (according to this significance threshold).
#   * The p-value of the associated STRAPP test. Here, p = 0.071.

# Plot rates vs. trait data for all time-steps
plot_rates_vs_trait_data_over_time(
   deepSTRAPP_outputs = Ponerinae_deepSTRAPP_cat_3lvl_old_calib_0_40,
   colors_per_levels = colors_per_states)

```
```{r plot_rates_vs_traits_cat_3lvl_eval_dev, fig.height = 7, fig.width = 8.5, out.width = "100%", eval = is_dev_version(), echo = FALSE}
# Generate ggplot for time = 10 My
plot_rates_vs_trait_data_for_focal_time(
   deepSTRAPP_outputs = Ponerinae_deepSTRAPP_cat_3lvl_old_calib_0_40,
   focal_time = 10,
   colors_per_levels = colors_per_states)
```
```{r plot_rates_vs_traits_cat_3lvl_eval_CRAN, eval = !is_dev_version(), echo = FALSE, out.width = "100%"}

# Plot pre-rendered graph
knitr::include_graphics("figures/1.2_deepSTRAPP_categorical_3lvl_data_4.4_plot_rates_vs_traits.PNG")

```

```{r plot_updated_densityMaps_cat_3lvl}
### 4.5/ Plot updated densityMaps mapping trait evolution for a given 'focal_time' ####

# ?deepSTRAPP::plot_densityMaps_overlay()

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

# Display the time-steps
Ponerinae_deepSTRAPP_cat_3lvl_old_calib_0_40$time_steps

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

# Plot initial densityMaps (t = 0)
densityMaps_0My <- Ponerinae_deepSTRAPP_cat_3lvl_old_calib_0_40$updated_trait_data_with_Map_over_time[[1]]
plot_densityMaps_overlay(densityMaps_0My$densityMaps,
                         colors_per_levels = colors_per_states,
                         fsize = 0.1) # Reduce tip label size
title(main = "Trait evolution for 100-0 My")

# It highlights the relatively recent emergence of "terricolous" ants (in this fake illustrative dataset),
# where no lineages exhibit this state in deep times.

## The next plot shows the evolution of states from root to 10 Mya (100-10 My).

# Plot updated densityMaps for time-step n°3 = 10 My
densityMaps_10My <- Ponerinae_deepSTRAPP_cat_3lvl_old_calib_0_40$updated_trait_data_with_Map_over_time[[3]]
plot_densityMaps_overlay(densityMaps_10My$densityMaps,
                         colors_per_levels = colors_per_states,
                         fsize = 0.1) # Reduce tip label size
title(main = "Trait evolution for 100-10 My")

## The next plot shows the evolution of states from root to 40 Mya (100-40 My).

# Plot updated densityMaps for time-step n°9 = 40 My
densityMaps_40My <- Ponerinae_deepSTRAPP_cat_3lvl_old_calib_0_40$updated_trait_data_with_Map_over_time[[9]]
plot_densityMaps_overlay(densityMaps = densityMaps_40My$densityMaps,
                         colors_per_levels = colors_per_states,
                         fsize = 0.2) # Reduce tip label size
title(main = "Trait evolution for 100-40 My")

# In this simulated illustrative dataset, no ant lineages are inferred in "terricolous" habitats 40 Mya.

```
```{r plot_updated_densityMaps_cat_3lvl_eval_dev, fig.height = 7, eval = is_dev_version(), echo = FALSE}
# Plot initial densityMaps (t = 0)
densityMaps_0My <- Ponerinae_deepSTRAPP_cat_3lvl_old_calib_0_40$updated_trait_data_with_Map_over_time[[1]]
plot_densityMaps_overlay(densityMaps_0My$densityMaps,
                         colors_per_levels = colors_per_states,
                         cex_pies = 0.3,   
                         fsize = 0.1) # Reduce tip label size
title(main = "Trait evolution for 100-0 My")

# Plot updated densityMaps for time-step n°9 = 40 My
densityMaps_40My <- Ponerinae_deepSTRAPP_cat_3lvl_old_calib_0_40$updated_trait_data_with_Map_over_time[[9]]
plot_densityMaps_overlay(densityMaps_40My$densityMaps,
                         colors_per_levels = colors_per_states,
                         cex_pies = 0.4,
                         fsize = 0.2) # Reduce tip label size
title(main = "Trait evolution for 100-40 My")
```
```{r plot_updated_densityMaps_cat_3lvl_eval_CRAN, eval = !is_dev_version(), echo = FALSE, out.width = "100%"}

# Plot pre-rendered graph
knitr::include_graphics("figures/1.2_deepSTRAPP_categorical_3lvl_data_4.5_plot_updated_densityMaps_1.PNG")
knitr::include_graphics("figures/1.2_deepSTRAPP_categorical_3lvl_data_4.5_plot_updated_densityMaps_2.PNG")

```

```{r plot_BAMM_rates_cat_3lvl}
### 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_cat_3lvl_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 diversification rates across the whole phylogeny (100-0 My).

# Plot diversification rates on initial phylogeny (t = 0)
BAMM_map_0My <- Ponerinae_deepSTRAPP_cat_3lvl_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 - 10, col = "red", lty = 2) # Show where the phylogeny will be cut at 10 Mya
abline(v = root_age - 40, col = "red", lty = 2) # Show where the phylogeny will be cut at 40 Mya
title(main = "BAMM rates for 100-0 My")

## The next plot shows the evolution of diversification rates from root to 10 Mya (100-10 My).

# Plot diversification rates on updated phylogeny for time-step n°3 = 10 My
BAMM_map_10My <- Ponerinae_deepSTRAPP_cat_3lvl_old_calib_0_40$updated_BAMM_objects_over_time[[3]]
plot_BAMM_rates(BAMM_map_10My, labels = FALSE,
                colorbreaks = BAMM_map_10My$initial_colorbreaks$net_diversification)
title(main = "BAMM rates for 100-10 My")

## The next plot shows the evolution of diversification rates from root to 40 Mya (100-40 My).

# Plot diversification rates on updated phylogeny for time-step n°9 = 40 My
BAMM_map_40My <- Ponerinae_deepSTRAPP_cat_3lvl_old_calib_0_40$updated_BAMM_objects_over_time[[9]]
plot_BAMM_rates(BAMM_map_40My, labels = FALSE,
                colorbreaks = BAMM_map_40My$initial_colorbreaks$net_diversification)
title(main = "BAMM rates for 100-40 My")

```
```{r plot_BAMM_rates_cat_3lvl_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_cat_3lvl_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]) - 10, col = "red", lty = 2) # Show where the phylogeny will be cut at 10 Mya
title(main = "BAMM rates for 100-0 My")

# Plot diversification rates on updated phylogeny for time-step n°3 = 10 My
BAMM_map_10My <- Ponerinae_deepSTRAPP_cat_3lvl_old_calib_0_40$updated_BAMM_objects_over_time[[3]]
plot_BAMM_rates(BAMM_map_10My, labels = FALSE, legend = TRUE,
                colorbreaks = BAMM_map_10My$initial_colorbreaks$net_diversification)
title(main = "BAMM rates for 100-10 My")

par(old_par)
```
```{r plot_BAMM_rates_cat_3lvl_eval_CRAN, eval = !is_dev_version(), echo = FALSE, out.width = "100%"}

# Plot pre-rendered graph
knitr::include_graphics("figures/1.2_deepSTRAPP_categorical_3lvl_data_4.6_plot_BAMM_rates.PNG")

```

```{r plot_traits_vs_rates_on_phylogeny_cat_3lvl}
### 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_cat_3lvl_old_calib_0_40$time_steps

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

# Plot both mapped phylogenies in the present (t = 0)
plot_traits_vs_rates_on_phylogeny_for_focal_time(
  deepSTRAPP_outputs = Ponerinae_deepSTRAPP_cat_3lvl_old_calib_0_40,
  focal_time = 0,
  ftype = "off", lwd = 0.7,
  colors_per_levels = colors_per_states,
  labels = FALSE, legend = FALSE,
  par.reset = FALSE)

## The next plot shows the evolution of states and rates from root to 10 Mya (100-10 My).

# Plot both mapped phylogenies for time-step n°3 = 10 My
plot_traits_vs_rates_on_phylogeny_for_focal_time(
  deepSTRAPP_outputs = Ponerinae_deepSTRAPP_cat_3lvl_old_calib_0_40,
  focal_time = 10, 
  ftype = "off", lwd = 1.2,
  colors_per_levels = colors_per_states,
  labels = FALSE, legend = FALSE,
  par.reset = FALSE)

## The next plot shows the evolution of states and rates from root to 40 Mya (100-40 My).

# Plot both mapped phylogenies for time-step n°9 = 40 My
plot_traits_vs_rates_on_phylogeny_for_focal_time(
  deepSTRAPP_outputs = Ponerinae_deepSTRAPP_cat_3lvl_old_calib_0_40,
  focal_time = 40, 
  ftype = "off", lwd = 1.2,
  colors_per_levels = colors_per_states,
  labels = FALSE, legend = FALSE,
  par.reset = FALSE)

```
```{r plot_traits_vs_rates_on_phylogeny_cat_3lvl_eval_dev, fig.height = 7, eval = is_dev_version(), echo = FALSE}

# Plot both mapped phylogenies in the present (t = 0)
plot_traits_vs_rates_on_phylogeny_for_focal_time(
  deepSTRAPP_outputs = Ponerinae_deepSTRAPP_cat_3lvl_old_calib_0_40,
  focal_time = 0,
  ftype = "off", lwd = 0.7,
  colors_per_levels = colors_per_states,
  labels = FALSE, legend = FALSE,
  par.reset = FALSE)

# Plot both mapped phylogenies for time-step n°9 = 40 My
plot_traits_vs_rates_on_phylogeny_for_focal_time(
  deepSTRAPP_outputs = Ponerinae_deepSTRAPP_cat_3lvl_old_calib_0_40,
  focal_time = 40, 
  ftype = "off", lwd = 1.2,
  colors_per_levels = colors_per_states,
  labels = FALSE, legend = FALSE,
  par.reset = FALSE)

```
```{r plot_traits_vs_rates_on_phylogeny_cat_3lvl_eval_CRAN, eval = !is_dev_version(), echo = FALSE, out.width = "100%"}

# Plot pre-rendered graph
knitr::include_graphics("figures/1.2_deepSTRAPP_categorical_3lvl_data_4.7_plot_traits_vs_rate_maps_1.PNG")
knitr::include_graphics("figures/1.2_deepSTRAPP_categorical_3lvl_data_4.7_plot_traits_vs_rate_maps_2.PNG")

```