Plot mean rates vs. trait data over time-steps

Plot mean rates vs. trait data of branches as extracted for all time-steps. Data are extracted from the output of a deepSTRAPP run carried out with run_deepSTRAPP_over_time()) over multiple time-steps.

For each time-step, returns a plot showing rates vs. trait data. If the trait data are 'continuous', the plot is a scatter plot. If the trait data are 'categorical' or 'biogeographic', the plot is a boxplot.

If a PDF file path is provided in PDF_file_path, all plots will be saved directly in a unique PDF file, with one page per plot/time-step.

Usage

plot_rates_vs_trait_data_over_time(
  deepSTRAPP_outputs,
  rate_type = "net_diversification",
  select_trait_levels = "all",
  color_scale = NULL,
  colors_per_levels = NULL,
  display_plot = TRUE,
  PDF_file_path = NULL,
  return_mean_data_per_branches_df = FALSE
)

Arguments

deepSTRAPP_outputs: List of elements generated with run_deepSTRAPP_over_time() that runs the whole deepSTRAPP workflow over multiple time-steps. The list needs to include two data.frames: $trait_data_df_over_time and $diversification_data_df_over_time by setting extract_trait_data_melted_df = TRUE and extract_diversification_data_melted_df = TRUE.
rate_type: A character string specifying the type of diversification rates to plot. Must be one of 'speciation', 'extinction' or 'net_diversification' (default). Even if the deepSTRAPP_outputs object was generated with run_deepSTRAPP_over_time() for testing another type of rates, the object will contain data for all types of rates.
select_trait_levels: (Vector of) character string. Only for categorical and biogeographic trait data. To provide a list of a subset of states/ranges to plot. Names must match the ones found in the deepSTRAPP_outputs. Default is all which means all states/ranges will be plotted.
color_scale: Character vector. List of colors to use to build the color scale with grDevices::colorRampPalette() to display the points. Color scale from lowest values to highest rate values. Only for continuous data. Default = NULL will use the 'Spectral' color palette in RColorBrewer::brewer.pal().
colors_per_levels: Named character string. To set the colors to use to plot data points and box for each state/range. Names = states/ranges; values = colors. If NULL (default), the default ggplot2 color palette (scales::hue_pal()) will be used. Only for categorical and biogeographic data.
display_plot: Logical. Whether to display the plot generated in the R console. Default is TRUE.
PDF_file_path: Character string. If provided, the plot will be saved in a PDF file following the path provided here. The path must end with ".pdf".
return_mean_data_per_branches_df: Logical. Whether to include in the output the data.frame of mean rates per trait values/states/ranges of all branches computed over all time-steps and used for the plots. Default is FALSE.

Value

The function returns a list with at least one element.

rates_vs_trait_ggplots A list of objects of classes gg and ggplot ordered as in $time_steps. Each element corresponds to a ggplot for a given focal_time. They can be displayed on the console with print(output$rates_vs_trait_ggplots[[i]]). They correspond to the plots being displayed on the console one by one when the function is run, if display_plot = TRUE, and can be further modified for aesthetics using the ggplot2 grammar.

If the trait data are 'continuous', the plots are scatter plots showing how mean diversification rates vary with mean trait values across branches. If the trait data are 'categorical' or 'biogeographic', the plots are boxplots showing mean diversification rates per state/ranges per branch.

Each plot also displays summary statistics for the STRAPP test associated with the data displayed:

An observed statistic computed across the mean traits/ranges and rates values shown on the plot. This is not the statistic of the STRAPP test itself, which is conducted across stochastic maps X BAMM posterior samples (i.e., it is a distribution, not a unique value).
The quantile of null statistic distribution at the significance 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.
The p-value of the associated STRAPP test.

Optional summary data.frame:

mean_data_per_branches_df A data.frame with four columns providing the $mean_trait_value (continuous) / $states (categorical) / $ranges (biogeographic) and $mean_rates computed along branches ($tip_ID) at the different focal_time. Rates/Traits are averaged for each stochastic maps X BAMM posterior samples used for testing, in accordance with the uncertainty_strategy selected when running deepSTRAPP. This is the raw data used to draw each plot for each focal_time. Included if return_mean_data_per_branches_df = TRUE.

If a PDF_file_path is provided, the function will also generate a unique PDF file with one plot/page per $time_steps.

Details

The main input deepSTRAPP_outputs is the typical output of run_deepSTRAPP_over_time(). It provides information on results of a STRAPP test performed over multiple time-steps.

Plots are built based on both trait data and diversification data as extracted for the time-steps. Such data are recorded in the outputs of a deepSTRAPP run carried out with run_deepSTRAPP_over_time().

extract_trait_data_melted_df must be set to TRUE so that trait data are returned in a melted data.frame among the outputs under $trait_data_df_over_time.
extract_diversification_data_melted_df must be set to TRUE so that the diversification rates are returned among the outputs under $diversification_data_df_over_time.
return_STRAPP_results must be set to TRUE so that the STRAPP results objects recording the summary statistics for the STRAPP tests are returned among the outputs under $STRAPP_results_over_time.

For plotting a single focal_time, see plot_rates_vs_trait_data_for_focal_time().

Author

Maël Doré

Examples

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

 # Load fake 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")

 # Get Ancestral Character Estimates based on a Brownian Motion model
 # To obtain values at internal nodes
 Ponerinae_ACE <- phytools::fastAnc(tree = Ponerinae_tree_old_calib, x = Ponerinae_cont_tip_data)

  # (May take several minutes to run)
 # Run a Stochastic Mapping based on a Brownian Motion model
 # to interpolate values along branches and obtain a "contMap" object
 Ponerinae_contMap <- phytools::contMap(Ponerinae_tree_old_calib, x = Ponerinae_cont_tip_data,
                                        res = 100, # Number of time steps
                                        plot = FALSE)
 # Plot contMap = stochastic mapping of continuous trait
 plot_contMap(contMap = Ponerinae_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_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,
    uncertainty_strategy = "rates_only",
    return_perm_data = TRUE,
    # Need to be set to TRUE to save trait data
    extract_trait_data_melted_df = TRUE,
    # Need to be set to TRUE to save diversification data
    extract_diversification_data_melted_df = TRUE,
    return_STRAPP_results = TRUE,
    return_updated_BAMM_object = TRUE,
    verbose = TRUE,
    verbose_extended = TRUE) 

 ## Load directly trait data 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)

 ## Plot for all time-steps
 rates_vs_trait_outputs <- plot_rates_vs_trait_data_over_time(
    deepSTRAPP_outputs = Ponerinae_deepSTRAPP_cont_old_calib_0_40,
    color_scale = c("grey80", "purple"), # Adjust color scale
    display_plot = TRUE,
    # PDF_file_path = "./plot_rates_vs_trait_0_40My.pdf",
    return_mean_data_per_branches_df = TRUE)

 ## Print plot for time step 3 = 10 My
 print(rates_vs_trait_outputs$rates_vs_trait_ggplots[[3]])

 ## Explore melted data.frame of rates and trait data
 head(rates_vs_trait_outputs$mean_data_per_branches_df)

 # ----- Example 2: Categorical data ----- #

 ## 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 = "ARD",
    nb_simulations = 100,
    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$densityMaps,
    ace = Ponerinae_cat_3lvl_data_old_calib$ace,
    tip_data = Ponerinae_cat_3lvl_tip_data,
    nb_simulations = 100,
    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,
    rate_type = "net_diversification",
    uncertainty_strategy = "paired",
    seed = 1234, # Set for reproducibility
    alpha = 0.10, # Select a generous level of significance for the sake of the example
    posthoc_pairwise_tests = TRUE,
    return_perm_data = TRUE,
    # Need to be set to TRUE to save trait data
    extract_trait_data_melted_df = TRUE,
    # Need to be set to TRUE to save diversification data
    extract_diversification_data_melted_df = TRUE,
    return_STRAPP_results = TRUE,
    return_updated_BAMM_object = 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"))
 ## 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)

 # Adjust color scheme
 colors_per_states <- c("orange", "dodgerblue", "red")
 names(colors_per_states) <- c("arboreal", "subterranean", "terricolous")

 ## Plot for all time-steps
 rates_vs_trait_outputs <- plot_rates_vs_trait_data_over_time(
    deepSTRAPP_outputs = Ponerinae_deepSTRAPP_cat_3lvl_old_calib_0_40,
    colors_per_levels = colors_per_states, # Adjust color scheme
    display_plot = TRUE,
    # PDF_file_path = "./plot_rates_vs_trait_0_40My.pdf",
    return_mean_data_per_branches_df = TRUE)

 ## Print plot for time step 3 = 10 My
 print(rates_vs_trait_outputs$rates_vs_trait_ggplots[[3]])

 ## Explore melted data.frame of rates and states
 head(rates_vs_trait_outputs$mean_data_per_branches_df)
}