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Run deepSTRAPP to test for a relationship between diversification rates and trait data over multiple time steps

Source: R/run_deepSTRAPP_over_time.R
run_deepSTRAPP_over_time.Rd

Wrapper function to run deepSTRAPP workflows over multiple time steps in the past. It starts from traits mapped on a phylogeny (trait data) and BAMM output (diversification data) and carries out the appropriate statistical method to test for a relationship between diversification rates and trait data. The workflow is repeated over multiple points in time (i.e. the time_steps) and results are summarized in a data.frame. The function can also provide summaries of trait values and diversification rates extracted along branches over the different time_steps.

Statistical tests are based on block-permutations: rates data are randomized across tips following blocks defined by the diversification regimes identified on each tip (typically from a BAMM). Such tests are called STructured RAte Permutations on Phylogenies (STRAPP) as described in Rabosky, D. L., & Huang, H. (2016). A robust semi-parametric test for detecting trait-dependent diversification. Systematic biology, 65(2), 181-193. doi:10.1093/sysbio/syv066 .

See the original BAMMtools::traitDependentBAMM() function used to carry out STRAPP test on extant time-calibrated phylogenies.

Tests can be carried out on speciation, extinction and net diversification rates.

Usage

run_deepSTRAPP_over_time(
  contMap = NULL,
  contMaps = NULL,
  densityMaps = NULL,
  simmaps = NULL,
  nb_simulations = NULL,
  ace = NULL,
  tip_data = NULL,
  trait_data_type,
  keep_tip_labels = TRUE,
  BAMM_object,
  rate_type = "net_diversification",
  time_steps = NULL,
  time_range = NULL,
  nb_time_steps = NULL,
  time_step_duration = NULL,
  uncertainty_strategy = "paired",
  trait_maps_vs_BAMM_samples_list = NULL,
  seed = NULL,
  nb_permutations = NULL,
  alpha = 0.05,
  two_tailed = TRUE,
  one_tailed_hypothesis = NULL,
  posthoc_pairwise_tests = FALSE,
  p.adjust_method = "none",
  return_perm_data = FALSE,
  nthreads = 1,
  print_hypothesis = TRUE,
  extract_trait_data_melted_df = FALSE,
  extract_diversification_data_melted_df = FALSE,
  return_STRAPP_results = FALSE,
  return_updated_Maps = FALSE,
  return_updated_BAMM_objects = FALSE,
  verbose = TRUE,
  verbose_extended = FALSE
)

Arguments

contMap

For continuous trait data. Object of class "contMap", typically generated with prepare_trait_data() or phytools::contMap(), that contains a phylogenetic tree and associated continuous trait mapping. The phylogenetic tree must be rooted and fully resolved/dichotomous, but it does not need to be ultrametric (it can include fossils).

contMaps

For continuous trait data. List of objects of class "contMap", typically generated with prepare_trait_data(), that contains multiple continuous stochastic maps that represent possible trait evolutionary histories conditioned on the observed trait values and model fit.

densityMaps

For categorical trait or biogeographic data. List of objects of class "densityMap", typically generated with prepare_trait_data(), that contains a phylogenetic tree and associated posterior probability of being in a given state/range along branches. Each object (i.e., densityMap) corresponds to a state/range. The phylogenetic tree must be rooted and fully resolved/dichotomous, but it does not need to be ultrametric (it can include fossils).

simmaps

For categorical trait or biogeographic data. List of objects of class "simmap", typically generated with prepare_trait_data() or phytools::make.simmap(), that represent discrete character/geographic evolutionary history (i.e., transitions in character states/geographic ranges) mapped along branches. This is needed to be able to track which simulated history provided which trait data in downstream analyses employing the "paired" or "full" strategies to account for uncertainty in ancestral trait estimates.

nb_simulations

For categorical trait or biogeographic data. Integer. The number of stochastic maps used to simulate trait evolution. This is needed for the "paired" and "full" strategies to account for trait estimate uncertainty, if only densityMaps summarizing posterior state/range density are provided, but not the simmaps representing all evolutionary histories.

ace

(Optional) Ancestral Character Estimates (ACE) at the internal nodes. Obtained with prepare_trait_data() as output in the $ace slot.

  • For continuous trait data: Named numeric vector typically generated with phytools::fastAnc(), phytools::anc.ML(), or ape::ace(). Names are nodes_ID of the internal nodes. Values are ACE of the trait.

  • For categorical trait or biogeographic data: Matrix that records the posterior probabilities of ancestral states/ranges. Rows are internal nodes_ID. Columns are states/ranges. Values are posterior probabilities of each state per node. Needed in all cases to provide accurate estimates of trait values.

tip_data

(Optional) Named vector of tip values of the trait.

  • For continuous trait data: Named numeric vector of trait values.

  • For categorical trait or biogeographic data: Named character vector of states/ranges. Names must match tip.label in the phylogeny. Needed to provide accurate tip values.

  • For biogeographic data, ranges should follow the coding scheme of BioGeoBEARS with a unique CAPITAL letter per unique area (ex: A, B), combined to form multi-area ranges (Ex: AB). Alternatively, you can provide tip_data as a matrix or data.frame of binary presence/absence in each area (coded as unique CAPITAL letter). In this case, columns are unique areas, rows are taxa, and values are integer (0/1) signaling absence or presence of the taxa in the area.

trait_data_type

Character string. Specify the type of trait data. Must be one of "continuous", "categorical", "biogeographic".

keep_tip_labels

Logical. Specify whether terminal branches with a single descendant tip must retain their initial tip.label on the updated phylogeny. Default is TRUE.

BAMM_object

Object of class "bammdata", typically generated with prepare_diversification_data(), that contains a phylogenetic tree and associated diversification rate mapping across selected posterior samples. The phylogenetic tree must be the same as the one associated with the contMap, ace and tip_data.

rate_type

A character string specifying the type of diversification rates to use. Must be one of 'speciation', 'extinction' or 'net_diversification' (default).

time_steps

Numeric vector. Time steps at which the STRAPP tests should be carried out. If NULL (the default), time_steps will be generated from a combination of two arguments among time_range, nb_time_steps, and/or time_step_duration.

time_range

Numeric vector of length 2. Time boundaries within which the time_steps must be defined if not provided. If NULL (the default), and time_range is needed to generate the time_steps, the depth of the tree is used by default: c(0, root_age). However, no time step will be generated for the 'root_age'.

nb_time_steps, time_step_duration

Numeric. Number of time steps and duration of each time step used to generate time_steps if not provided. You must provide at least one of those two arguments to be able to generate time_steps.

uncertainty_strategy

Character string. To select the strategy used to account for uncertainty in estimates.

  • "rates_only": Only accounts for diversification-rate uncertainty across BAMM posterior samples. Uses ML estimates for continuous traits and the most frequent state/range observed across stochastic maps for categorical and biogeographic data.

  • "paired": Default option. Accounts for both diversification-rate and ancestral trait/range reconstruction uncertainty by pairing BAMM posterior samples with stochastic maps. When the number of BAMM samples and stochastic maps differ, random pairing with replacement from the smaller set is used so that all posterior samples and stochastic maps contribute to the analysis.

  • "full": Exhaustive option that accounts for trait/range- and rate- uncertainty by crossing all BAMM posterior samples with all stochastic maps. Accounts for both diversification-rate and ancestral reconstruction uncertainty by evaluating every combination of BAMM posterior samples and stochastic maps. WARNING: This exhaustive approach can substantially increase computation time and memory requirements and is therefore recommended only for moderate-sized analyses.

trait_maps_vs_BAMM_samples_list

(Optional) List of two elements manually providing the names to associate stochastic maps ($trait_map_ID) with BAMM samples ($BAMM_posterior_sample_ID). This is typically used to ensure the same stochastic maps and BAMM samples are used to test across multiple time-steps. Values are the names of the objects such as "Map_X" and "BAMM_X". Default = NULL. * For uncertainty_strategy == 'rates_only', the $trait_map_ID must be "Map_ML" as only the ML estimates of trait values/states/ranges are used. * For uncertainty_strategy == 'paired', each pair of stochastic map and BAMM sample will be used once. * For uncertainty_strategy == 'full', all stochastic maps will be matched with all BAMM samples. Those may partly differ from the actual maps and BAMM samples used for the tests as recorded in $STRAPP_results_over_time[[X]]$perm_data_df because invalid maps with not enough states/ranges are discarded at each time-step.

seed

Integer. Set the seed to ensure reproducibility. Default is NULL (a random seed is used).

nb_permutations

Integer. To select the number of random permutations to perform during the tests. If NULL (default), all posterior samples will be used once.

alpha

Numeric. Significance level to use to compute the estimate corresponding to the values of the test statistic used to assess significance of the test. This does NOT affect p-values. Default is 0.05.

two_tailed

Logical. To define the type of tests. If TRUE (default), tests for correlations/differences in rates will be carried out with a null hypothesis that rates are not correlated with trait values (continuous data) or equal between trait states (categorical and biogeographic data). If FALSE, one-tailed tests are carried out.

  • For continuous data, it involves defining a one_tailed_hypothesis testing for either a "positive" or "negative" correlation under the alternative hypothesis.

  • For binary data (two states), it involves defining a one_tailed_hypothesis indicating which states have higher rates under the alternative hypothesis.

  • For multinomial data (more than two states), it defines the type of post hoc pairwise tests to carry out between pairs of states. If posthoc_pairwise_tests = TRUE, all two-tailed (if two_tailed = TRUE) or one-tailed (if two_tailed = FALSE) tests are automatically carried out.

one_tailed_hypothesis

A character string specifying the alternative hypothesis in the one-tailed test. For continuous data, it is either "negative" or "positive" correlation. For binary data, it lists the trait states with states ordered in increasing rates under the alternative hypothesis, separated by a greater-than such as c('A > B').

posthoc_pairwise_tests

Logical. Only for multinomial data (with more than two states). If TRUE, all possible post hoc pairwise (Dunn) tests will be computed across all pairs of states. This is a way to detect which pairs of states have significant differences in rates if the overall test (Kruskal-Wallis) is significant. Default is FALSE.

p.adjust_method

A character string. Only for multinomial data (with more than two states). It specifies the type of correction to apply to the p-values in the post hoc pairwise tests to account for multiple comparisons. See stats::p.adjust() for the available methods. Default is none.

return_perm_data

Logical. Whether to return the stats data computed from the posterior samples for observed and permuted data in the output. This is needed to plot the histograms of the null distribution used to assess significance of the tests with plot_histogram_STRAPP_test_for_focal_time(). (for a single focal_time) and plot_histograms_STRAPP_tests_over_time() (for multiple time_steps). Default is FALSE.

nthreads

Integer. Number of threads to use for parallel computing of the STRAPP tests across the permutations. The R package parallel must be loaded for nthreads > 1. Default is 1.

print_hypothesis

Logical. Whether to print information on what test is carried out, detailing the null and alternative hypotheses, what significance level is used to reject or not the null hypothesis, and how uncertainty in trait estimates is handled. Default is TRUE.

extract_trait_data_melted_df

Logical. Specify whether trait data must be extracted from the mapped phylogenies at each time step and returned in a melted data.frame. Default is FALSE.

extract_diversification_data_melted_df

Logical. Specify whether diversification data (regimes ID and tip rates) must be extracted from the updated_BAMM_object at each time step and returned in a melted data.frame. Default is FALSE.

return_STRAPP_results

Logical. Specify whether the STRAPP_results objects summarizing the results of the STRAPP tests carried out at each time step should be returned among the outputs in addition to the $pvalues_summary_df already providing test stat estimates and p-values obtained across all time_steps.

return_updated_Maps

Logical. Specify whether the updated versions of the mapped phylogenies (contMap(s)/densityMaps/simmaps), should be returned among the outputs. The update of the contMap(s)/densityMaps/simmaps consists of cutting off branches and mapping that are younger than the time_steps used to perform the tests. Default is FALSE.

return_updated_BAMM_objects

Logical. Specify whether the updated_BAMM_objects with phylogeny and mapped diversification rates cut-off at the time_steps used to perform the tests should be returned among the outputs.

verbose

Logical. Should progression per time_steps be displayed? Default is TRUE.

verbose_extended

Should progression per time_steps AND within each deepSTRAPP workflow be displayed? In addition to printing progress along time_steps, a message will be printed at each step of the deepSTRAPP workflow, and for every batch of 100 BAMM posterior samples whose rates and regimes are updated. If extract_diversification_data_melted_df = TRUE, a message will also be printed when rates are extracted. Default is FALSE.

Value

The function returns a list with at least five elements.

  • $pvalues_summary_df Data.frame with three columns providing test stat $estimate and $p_value obtained for each time step (i.e., $focal_time), that can be passed down to plot_STRAPP_pvalues_over_time() to generate a plot showing the evolution of the test results across time.

  • $time_steps Numeric vector. Time steps at which the STRAPP tests were carried out in the same order as the objects returned in the output lists.

  • $trait_data_type Character string. Specify the type of trait data. Possible values are: "continuous", "categorical", "biogeographic".

  • $trait_data_type_for_stats Character string. The type of trait data used to select statistical method. One of 'continuous', 'binary', or 'multinomial'. The method is fixed once for the whole run, based on the number of states/ranges found in the complete trait mapping, so that the same test is used at every time step for consistency across p-values.

  • $states_observed_overall (Only for categorical and biogeographic data) Character string vector of all states/ranges found in the complete trait mapping. This is what the statistical method trait_data_type_for_stats is based on, and it can be larger than the set of states/ranges found at any given time step.

  • $states_observed_per_time_steps (Only for categorical and biogeographic data) List of character string vectors, one per time step and named after it. States/ranges found at each time step. There can be steps with fewer states/ranges than others, which may affect the interpretation of the p-values obtained for these steps.

  • $nb_states_observed_per_time_steps (Only for categorical and biogeographic data) Named numeric vector. Number of states/ranges found at each time step. Recorded so that the interpretation of the p-values obtained from steps with fewer states/ranges can be adjusted if needed. For cases with only one state/range, no test could be computed, and they should be matching a NA in $pvalues_summary_df.

  • $rate_type Character string. The type of diversification rates used in the tests: 'speciation', 'extinction' or 'net_diversification'.

  • $uncertainty_strategy Character string. The strategy used to account for uncertainty in estimates. One of 'rates_only', 'paired', or 'full'.

  • $trait_maps_vs_BAMM_samples_list List of two elements recording the stochastic maps ($trait_map_ID) and BAMM samples ($BAMM_posterior_sample_ID) chosen for testing across time-steps. Those may partly differ from the actual maps and BAMM samples used for the tests as recorded in $STRAPP_results_over_time[[X]]$perm_data_df because invalid maps with not enough states/ranges are discarded.

Optional summary df for multinomial data, if posthoc_pairwise_tests = TRUE:

  • $pvalues_summary_df_for_posthoc_pairwise_tests Data.frame with four or five columns providing test stat $estimate, $p_value, and $p_value_adjusted (if p.adjust_method used is not "none") for each $pair of states involved in post hoc Dunn's tests obtained for each time step (i.e., $focal_time). This data.frame can be passed down to plot_STRAPP_pvalues_over_time() to generate a plot showing the evolution of the post hoc test results across time.

Optional melted data.frames:

  • $trait_data_df_over_time Data.frame with five columns providing $trait_value associated with each $tip_ID found along each time step (i.e., $focal_time) across all stochastic maps ($Map_ID). Set extract_trait_data_melted_df = TRUE to include it in the output.

  • $diversification_data_df_over_time Data.frame with six columns providing diversification regimes ($regime_ID) and $rates sorted by $rate_type along tips ($tip_ID) found across all posterior samples ($BAMM_sample_ID) over each time step (i.e., $focal_time). Set extract_diversification_data_melted_df = TRUE to include it in the output.

  • Those data.frames can be passed down to plot_rates_through_time() to generate a plot showing the evolution of diversification rates across trait values over time.

Optional objects generated for each time step (i.e., focal_time) and ordered as in $time_steps:

  • $STRAPP_results_over_time List of objects summarizing the results of the STRAPP tests See compute_STRAPP_test_for_focal_time() for a detailed description of the elements in each object. Set return_STRAPP_results = TRUE to include it in the output. Combined with return_perm_data = TRUE, it allows you to plot the histograms of the null distributions used to assess significance of the tests with plot_histogram_STRAPP_test_for_focal_time(). (for a single focal_time) and plot_histograms_STRAPP_tests_over_time() (for multiple time_steps).

  • $updated_Maps_over_time List of objects containing updated contMap(s)/densityMaps/simmaps. The updated mapped phylogenies can be plotted with plot_contMap() or plot_densityMaps_overlay(), to display a phylogeny mapped with trait values with branches cut at each focal_time.

  • $updated_BAMM_objects_over_time List of objects containing rates and regimes ID mapped on phylogeny. Updated BAMM_objects can be plotted with plot_BAMM_rates() to display a phylogeny mapped with diversification rates with branches cut at each focal_time.

Details

The function is a wrapper of run_deepSTRAPP_for_focal_time() that runs the deepSTRAPP workflow over multiple time_steps.

The deepSTRAPP workflow is described step by step in the run_deepSTRAPP_for_focal_time() documentation.

Its main output is the $pvalues_summary_df: a data.frame providing test stat estimates and p-values obtained across all time_steps, that can be passed down to plot_STRAPP_pvalues_over_time() to generate a plot showing the evolution of the test results across time. If using multinomial data (with more than two states) and posthoc_pairwise_tests = TRUE, the output will also contain a data.frame providing test stat estimates and p-values for post hoc pairwise tests in $pvalues_summary_df_for_posthoc_pairwise_tests.

The function offers options to generate summary data.frames of the data extracted across time_steps:

  • If extract_trait_data_melted_df = TRUE, a data.frame of trait values found along branches at each time step is provided in $trait_data_df_over_time.

  • If extract_diversification_data_melted_df = TRUE, a data.frame of diversification data (regimes ID and tip rates) found along branches at each time step is provided in $diversification_data_df_over_time.

  • Those data.frames can be passed down to plot_rates_through_time() to generate a plot showing the evolution of diversification rates across trait values over time.

The function also allows you to keep records of the intermediate objects generated during the STRAPP workflow:

  • If return_STRAPP_results = TRUE, a list of STRAPP test outputs is provided in $STRAPP_results_over_time. Combined with return_perm_data = TRUE, it allows you to plot the histograms of the null distributions used to assess significance of the tests with plot_histogram_STRAPP_test_for_focal_time(). (for a single focal_time) and plot_histograms_STRAPP_tests_over_time() (for multiple time_steps).

  • If return_updated_Maps = TRUE, a list of objects containing the updated contMap(s)/densityMaps/simmaps provided as inputs is provided in $updated_Maps_over_time. The updated mapped phylogenies can be visualized with plot_contMap() or plot_densityMaps_overlay(), to display a phylogeny mapped with trait values/states/ranges with branches cut at each focal_time.

  • If return_updated_BAMM_objects = TRUE, a list of updated BAMM_objects of class "bammdata" that contains rates and regimes ID found at each focal_time is provided in $updated_BAMM_objects_over_time. Updated BAMM_objects can be plotted with plot_BAMM_rates() to display a phylogeny mapped with diversification rates with branches cut at each focal_time.

See also

To run the deepSTRAPP workflow for a single focal_time: run_deepSTRAPP_for_focal_time() extract_most_likely_trait_values_for_focal_time() update_rates_and_regimes_for_focal_time() extract_diversification_data_melted_df_for_focal_time() compute_STRAPP_test_for_focal_time()

For a guided tutorial on complete deepSTRAPP workflow, see the associated vignettes:

Author

Maël Doré

Examples

if (deepSTRAPP::is_dev_version())
{
# ----- Example 1: Continuous trait ----- #
 ## Load data

 # Load trait df
data(Ponerinae_trait_tip_data, package = "deepSTRAPP")
 # Load phylogeny with old calibration
 data(Ponerinae_tree_old_calib, package = "deepSTRAPP")

 # Load the BAMM_object summarizing 1000 posterior samples of BAMM
 data(Ponerinae_BAMM_object_old_calib, 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.

 ## Prepare trait 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)

 # Select a color scheme from lowest to highest values
 color_scale = c("darkgreen", "limegreen", "orange", "red")

  # (May take several minutes to run)
 # Map trait evolution across multiple simulations (i.e., continuous stochastic maps)
 Ponerinae_cont_data_old_calib <- prepare_trait_data(
     tip_data = Ponerinae_cont_tip_data,
     trait_data_type = "continuous",
     phylo = Ponerinae_tree_old_calib,
     seed = 1234,
     evolutionary_models = "BM",
     plot_map = FALSE,
     verbose = TRUE)

 # Plot contMap = ML estimates of continuous trait evolution
 plot_contMap(contMap = Ponerinae_cont_data_old_calib$contMap,
              color_scale = color_scale)

 ## 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
 Ponerinae_deepSTRAPP_cont_old_calib_0_40 <- run_deepSTRAPP_over_time(
    contMap = Ponerinae_cont_data_old_calib$contMap, # Provide ML estimates as a contMap
    ace = Ponerinae_cont_data_old_calib$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,
    uncertainty_strategy = "rates_only",
    rate_type = "net_diversification",
    return_perm_data = TRUE,
    extract_trait_data_melted_df = TRUE,
    extract_diversification_data_melted_df = TRUE,
    return_STRAPP_results = TRUE,
    return_updated_Maps = TRUE,
    return_updated_BAMM_objects = TRUE,
    verbose = TRUE,
    verbose_extended = TRUE) 

 ## Load directly deepSTRAPP output
 Ponerinae_deepSTRAPP_cont_old_calib_0_40 <- readRDS(system.file("extdata",
     "Ponerinae_deepSTRAPP_cont_old_calib_0_40.rds", 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)
 deepSTRAPP_outputs <- Ponerinae_deepSTRAPP_cont_old_calib_0_40

 # 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.
 deepSTRAPP_outputs$pvalues_summary_df

 # Access trait data in a melted data.frame
 head(deepSTRAPP_outputs$trait_data_df_over_time)
 # Trait data includes only the ML estimates
 table(deepSTRAPP_outputs$trait_data_df_over_time$Map_ID)

 # Access the diversification data in a melted data.frame
 head(deepSTRAPP_outputs$diversification_data_df_over_time)
 # Diversification data includes 1000 BAMM posteriors
 table(deepSTRAPP_outputs$diversification_data_df$BAMM_sample_ID)

 # Access deepSTRAPP results ordered by time-steps
 str(deepSTRAPP_outputs$STRAPP_results, max.level = 2)

 ## Visualize updated phylogenies

  # (May take time to plot)
 # Plot updated contMap for time step n°2
 contMap_step2 <- deepSTRAPP_outputs$updated_Maps_over_time[[2]]$contMap
 plot_contMap(contMap_step2, ftype = "off", color_scale = color_scale)

 # Plot diversification rates on updated phylogeny for time step n°2
 BAMM_object_step2 <- deepSTRAPP_outputs$updated_BAMM_objects_over_time[[2]]
 plot_BAMM_rates(BAMM_object = BAMM_object_step2,
   legend = TRUE, labels = FALSE,
   colorbreaks = BAMM_object_step2$initial_colorbreaks$net_diversification) 

 ## Visualize test results

  # (May take time to plot)
 # Plot p-values of Spearman tests across all time-steps
 plot_STRAPP_pvalues_over_time(
    deepSTRAPP_outputs = Ponerinae_deepSTRAPP_cont_old_calib_0_40,
    alpha = 0.05)

 # Plot evolution of mean rates through time
 plot_rates_through_time(deepSTRAPP_outputs = Ponerinae_deepSTRAPP_cont_old_calib_0_40,
    plot_CI = TRUE)

 # Plot rates vs. trait values across branches for time step = 10 My
 plot_rates_vs_trait_data_for_focal_time(
    deepSTRAPP_outputs = Ponerinae_deepSTRAPP_cont_old_calib_0_40,
    focal_time = 10)

# Plot histogram of Spearman test stats for time step = 10 My
 plot_histogram_STRAPP_test_for_focal_time(
    deepSTRAPP_outputs = Ponerinae_deepSTRAPP_cont_old_calib_0_40,
    focal_time = 10)

 # Plot histograms of Spearman test results (One plot per time-step)
 plot_histograms_STRAPP_tests_over_time(
    deepSTRAPP_outputs = Ponerinae_deepSTRAPP_cont_old_calib_0_40,
    display_plots = TRUE)  

 # ----- Example 2: Categorical trait ----- #

## Load data

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

 # Load the BAMM_object summarizing 1000 posterior samples of BAMM
 data(Ponerinae_BAMM_object_old_calib, 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.

 ## Prepare trait 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)
 table(Ponerinae_cat_3lvl_tip_data)

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

  # (May take several minutes to run)
 ## Produce densityMaps using stochastic character mapping based on an ARD Mk model
 Ponerinae_cat_3lvl_data_old_calib <- 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,
    evolutionary_models = "ER", # Use default ER model
    nb_simulations = 100, # Reduce number of simulations to save time
    seed = 1234, # Set seed for reproducibility
    return_best_model_fit = TRUE,
    return_model_selection_df = TRUE,
    plot_map = FALSE) 

  # Load directly trait data output
 data(Ponerinae_cat_3lvl_data_old_calib, package = "deepSTRAPP")

## 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)

  # (May take several minutes to run)
 ## Run deepSTRAPP on net diversification rates across time-steps.
 Ponerinae_deepSTRAPP_cat_3lvl_old_calib_0_40 <- run_deepSTRAPP_over_time(
    densityMaps = Ponerinae_cat_3lvl_data_old_calib$densityMaps,
    # Inform the nb of simulations to reconstruct state distribution across stochastic maps
    nb_simulations = 100,
    ace = Ponerinae_cat_3lvl_data_old_calib$ace,
    tip_data = Ponerinae_cat_3lvl_tip_data,
    trait_data_type = "categorical",
    BAMM_object = Ponerinae_BAMM_object_old_calib,
    # nb_time_steps = nb_time_steps,
    time_range = time_range,
    time_step_duration = time_step_duration,
    uncertainty_strategy = "paired",
    rate_type = "net_diversification",
    seed = 1234,
    alpha = 0.10, # Select a generous level of significance for the sake of the example
    posthoc_pairwise_tests = TRUE,
    return_perm_data = TRUE,
    extract_trait_data_melted_df = TRUE,
    extract_diversification_data_melted_df = TRUE,
    return_STRAPP_results = TRUE,
    return_updated_Maps = TRUE,
    return_updated_BAMM_objects = TRUE,
    verbose = TRUE,
    verbose_extended = TRUE) 

 ## Load directly deepSTRAPP output
 Ponerinae_deepSTRAPP_cat_3lvl_old_calib_0_40 <- readRDS(system.file("extdata",
     "Ponerinae_deepSTRAPP_cat_3lvl_old_calib_0_40.rds", package = "deepSTRAPP"))
 deepSTRAPP_outputs <- Ponerinae_deepSTRAPP_cat_3lvl_old_calib_0_40
 ## 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(deepSTRAPP_outputs, max.level = 1)

 # 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.
 deepSTRAPP_outputs$pvalues_summary_df
 # Results for posthoc pairwise Dunn's tests over time-steps
 deepSTRAPP_outputs$pvalues_summary_df_for_posthoc_pairwise_tests

 # Access trait data in a melted data.frame
 head(deepSTRAPP_outputs$trait_data_df_over_time)
 # Trait data were extracted from densityMaps recording only frequency of states.
 # Thus, trait states have been distributed accordingly across 100 'Dummy_maps'
 # to reproduce the recorded frequencies.
 table(deepSTRAPP_outputs$trait_data_df_over_time$Map_ID)

 # Access the diversification data in a melted data.frame
 head(deepSTRAPP_outputs$diversification_data_df_over_time)
 # Diversification data includes 1000 BAMM posteriors
 table(deepSTRAPP_outputs$diversification_data_df_over_time$BAMM_sample_ID)

 # Access details of deepSTRAPP results across time-steps
 str(deepSTRAPP_outputs$STRAPP_results, max.level = 2)

 ## Visualize updated phylogenies

  # (May take time to plot)
 # Plot updated densityMaps for time step n°2 = 10My
 densityMaps_10My <- deepSTRAPP_outputs$updated_Maps_over_time[[2]]
 plot_densityMaps_overlay(densityMaps_10My$densityMaps, colors_per_levels = colors_per_states)

 # Plot diversification rates on updated phylogeny for time step n°2
 BAMM_object_10My <- deepSTRAPP_outputs$updated_BAMM_objects_over_time[[2]]
 plot_BAMM_rates(BAMM_object = BAMM_object_10My,
    legend = TRUE, labels = FALSE,
   colorbreaks = BAMM_object_10My$initial_colorbreaks$net_diversification) 

 ## Visualize test results

  # (May take time to plot)
 # Plot p-values of overall Kruskal-Wallis test across all time-steps
 plot_STRAPP_pvalues_over_time(
    deepSTRAPP_outputs = deepSTRAPP_outputs,
   alpha = 0.10)

 # Plot p-values of post hoc pairwise Dunn's tests between pairs of tests across all time-steps
 plot_STRAPP_pvalues_over_time(
    deepSTRAPP_outputs = deepSTRAPP_outputs,
    alpha = 0.10,
    plot_posthoc_tests = TRUE)

 # Plot evolution of mean rates through time
 plot_rates_through_time(deepSTRAPP_outputs = deepSTRAPP_outputs,
    colors_per_levels = colors_per_states, plot_CI = TRUE)

 # Plot rates vs. trait values across branches for time step n°2 = 10 My
 plot_rates_vs_trait_data_for_focal_time(
    deepSTRAPP_outputs = deepSTRAPP_outputs,
    focal_time = 10,
    colors_per_levels = colors_per_states)

 # Plot histogram of overall Kruskal-Wallis test for time step n°2 = 10 My
 plot_histogram_STRAPP_test_for_focal_time(
    deepSTRAPP_outputs = deepSTRAPP_outputs,
    focal_time = 10)

 # Plot histograms of overall Kruskal-Wallis test results across all time-steps
 # (One plot per time-step)
 plot_histograms_STRAPP_tests_over_time(
    deepSTRAPP_outputs = deepSTRAPP_outputs,
    plot_posthoc_tests = FALSE)

 # Plot histograms of post hoc pairwise Dunn's test results across all time-steps
 # (One multifaceted plot per time-step)
 plot_histograms_STRAPP_tests_over_time(
    deepSTRAPP_outputs = deepSTRAPP_outputs,
    plot_posthoc_tests = TRUE) 

# ----- Example 3: Biogeographic ranges ----- #

## Load data

 # Load phylogeny
 data(Ponerinae_tree_old_calib, package = "deepSTRAPP")
 # Load trait df
 data(Ponerinae_binary_range_table, package = "deepSTRAPP")

 # Load the BAMM_object summarizing 1000 posterior samples of BAMM
 data(Ponerinae_BAMM_object_old_calib, 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.

 ## Prepare range data for Old World vs. New World

 # No overlap in ranges
 table(Ponerinae_binary_range_table$Old_World, Ponerinae_binary_range_table$New_World)

 Ponerinae_NO_tip_data <- stats::setNames(object = Ponerinae_binary_range_table$Old_World,
                                      nm = Ponerinae_binary_range_table$Taxa)
 Ponerinae_NO_tip_data <- as.character(Ponerinae_NO_tip_data)
 Ponerinae_NO_tip_data[Ponerinae_NO_tip_data == "TRUE"] <- "O" # O = Old World
 Ponerinae_NO_tip_data[Ponerinae_NO_tip_data == "FALSE"] <- "N" # N = New World
 names(Ponerinae_NO_tip_data) <- Ponerinae_binary_range_table$Taxa
 table(Ponerinae_NO_tip_data)

 colors_per_ranges <- c("mediumpurple2", "peachpuff2")
 names(colors_per_ranges) <- c("N", "O")

  # (May take several minutes to run)

 # Load ape and BioGeoBEARS to use models internally
 library(ape)
 library(BioGeoBEARS)

 ## Run evolutionary models
 Ponerinae_biogeo_data <- prepare_trait_data(
   tip_data = Ponerinae_NO_tip_data,
    trait_data_type = "biogeographic",
    phylo = Ponerinae_tree_old_calib,
    evolutionary_models = "DEC+J", # Default = "DEC" for biogeographic
    BioGeoBEARS_directory_path = tempdir(), # Ex: "./BioGeoBEARS_directory/"
    keep_BioGeoBEARS_files = FALSE,
    prefix_for_files = "Ponerinae_old_calib",
    max_range_size = 2,
    split_multi_area_ranges = TRUE, # Set to TRUE to use only unique areas "A" and "B"
    nb_simulations = 100, # Reduce to save time (Default = '1000')
    colors_per_levels = colors_per_ranges,
    return_model_selection_df = TRUE,
    verbose = TRUE) 

 # Load directly output
 data(Ponerinae_biogeo_data_old_calib, package = "deepSTRAPP")

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

  # (May take several minutes to run)
 ## Run deepSTRAPP on net diversification rates for time-steps = 0 to 40 Mya.
 Ponerinae_deepSTRAPP_biogeo_old_calib_0_40 <- run_deepSTRAPP_over_time(
   densityMaps = Ponerinae_biogeo_data_old_calib$densityMaps,
   # Inform the nb of simulations to reconstruct state distribution across stochastic maps
    nb_simulations = 100,
    ace = Ponerinae_biogeo_data_old_calib$ace,
    tip_data = Ponerinae_NO_tip_data,
    trait_data_type = "biogeographic",
    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, # Select a generous level of significance for the sake of the example
    uncertainty_strategy = "paired",
    rate_type = "net_diversification",
    return_perm_data = TRUE,
    extract_trait_data_melted_df = TRUE,
    extract_diversification_data_melted_df = TRUE,
    return_STRAPP_results = TRUE,
    return_updated_Maps = TRUE,
    return_updated_BAMM_objects = TRUE,
    verbose = TRUE,
    verbose_extended = TRUE) 

 ## Load directly output
 Ponerinae_deepSTRAPP_biogeo_old_calib_0_40 <- readRDS(system.file("extdata",
    "Ponerinae_deepSTRAPP_biogeo_old_calib_0_40.rds", package = "deepSTRAPP"))
 deepSTRAPP_outputs <- Ponerinae_deepSTRAPP_biogeo_old_calib_0_40
 ## 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(deepSTRAPP_outputs, max.level = 1)

 # 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.
 deepSTRAPP_outputs$pvalues_summary_df

 # Access bioregeographic range data in a melted data.frame
 head(deepSTRAPP_outputs$trait_data_df_over_time)
 # Ranges were extracted from densityMaps recording only frequency of states.
 # Thus, ranges have been distributed accordingly across 'Dummy_maps'
 # to reproduce the recorded frequencies.
 table(deepSTRAPP_outputs$trait_data_df_over_time$Map_ID)

 # Access the diversification data in a melted data.frame
 head(deepSTRAPP_outputs$diversification_data_df_over_time)
 # Diversification data includes 1000 BAMM posteriors
 table(deepSTRAPP_outputs$diversification_data_df_over_time$BAMM_sample_ID)

 # Access details of deepSTRAPP results
 str(deepSTRAPP_outputs$STRAPP_results, max.level = 2)

 ## Visualize updated phylogenies

  # (May take time to plot)
 # Plot updated densityMaps for time step n°2 = 10My
 densityMaps_10My <- deepSTRAPP_outputs$updated_Maps_over_time[[2]]
 plot_densityMaps_overlay(densityMaps_10My$densityMaps)

 # Plot diversification rates on updated phylogeny for time step n°2
 BAMM_object_10My <- deepSTRAPP_outputs$updated_BAMM_objects_over_time[[2]]
 plot_BAMM_rates(BAMM_object = BAMM_object_10My,
   legend = TRUE, labels = FALSE,
   colorbreaks = BAMM_object_10My$initial_colorbreaks$net_diversification) 

 ## Visualize test results

  # (May take time to plot)
 # Plot p-values of Mann-Whitney-Wilcoxon tests across all time-steps
 plot_STRAPP_pvalues_over_time(
    deepSTRAPP_outputs = deepSTRAPP_outputs,
    alpha = 0.10) # Set a generous significance threshold for the example

# Plot evolution of mean rates through time
 plot_rates_through_time(deepSTRAPP_outputs = deepSTRAPP_outputs,
   colors_per_levels = colors_per_ranges, plot_CI = TRUE)

 # Plot rates vs. trait values across branches for time step n°2 = 10 My
 plot_rates_vs_trait_data_for_focal_time(
    deepSTRAPP_outputs = deepSTRAPP_outputs,
    focal_time = 10,
    colors_per_levels = colors_per_ranges)

 # Plot histogram of Mann-Whitney-Wilcoxon test stats for time step n°2 = 10My
 plot_histogram_STRAPP_test_for_focal_time(
    deepSTRAPP_outputs = deepSTRAPP_outputs,
   focal_time = 10)

# Plot histograms of Mann-Whitney-Wilcoxon test stats for all time-steps (One plot per time-step)
 plot_histograms_STRAPP_tests_over_time(
    deepSTRAPP_outputs = deepSTRAPP_outputs,
    display_plots = TRUE,
    plot_posthoc_tests = FALSE) 
}