Plot rates vs. trait data for a given focal time

Plot rates vs. trait data of branches as extracted for a given focal time. Data are extracted from the output of a deepSTRAPP run carried out with run_deepSTRAPP_for_focal_time() or run_deepSTRAPP_over_time()).

Returns a single plot showing mean rates vs. trait data extracted across all branches for a given focal time. 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, the plot will be saved directly in a PDF file.

Usage

plot_rates_vs_trait_data_for_focal_time(
  deepSTRAPP_outputs,
  focal_time = NULL,
  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_for_focal_time(), that summarize the results of a STRAPP test for a specific time in the past (i.e. the focal_time). 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. deepSTRAPP_outputs can also be extracted from the output of run_deepSTRAPP_over_time() that runs the whole deepSTRAPP workflow over multiple time-steps.
focal_time: Numeric. (Optional) If deepSTRAPP_outputs comprises results over multiple time-steps (i.e., output of run_deepSTRAPP_over_time(), this is the time of the STRAPP test targeted for plotting.
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 at the focal time and used for the plot. Default is FALSE.

Value

The function returns a list with at least one element.

rates_vs_trait_ggplot An object of classes gg and ggplot. This is a ggplot that can be displayed on the console with print(output$rates_vs_trait_ggplot). It corresponds to the plot being displayed on the console 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 plot is a scatter plot showing how mean diversification rates vary with mean trait values across branches. If the trait data are 'categorical' or 'biogeographic', the plot is a boxplot showing mean diversification rates by states/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_values (continuous) / $states (categorical) / $ranges (biogeographic) and $mean_rates computed along branches ($tip_ID) at 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 the plot. Included if return_mean_data_per_branches_df = TRUE.

If a PDF_file_path is provided, the function will also generate a PDF file of the plot.

Details

The main input deepSTRAPP_outputs is the typical output of run_deepSTRAPP_for_focal_time(). It provides information on results of a STRAPP test performed at a given focal_time.

Plots are built based on both trait data and diversification data as extracted for the given focal_time. Such data are recorded in the outputs of a deepSTRAPP run carried out with run_deepSTRAPP_for_focal_time() when extract_trait_data_melted_df = TRUE for trait data, and extract_diversification_data_melted_df = TRUE for diversification data. Please ensure to select those arguments when running deepSTRAPP.

Alternatively, the main input deepSTRAPP_outputs can be the output of run_deepSTRAPP_over_time(), providing results of STRAPP tests over multiple time-steps. In this case, you must provide a focal_time to select the unique time-step used for plotting.

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 in a melted data.frame 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 all time-steps at once, see plot_rates_vs_trait_data_over_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 focal time to 10 Mya
 focal_time <- 10

 ## Run deepSTRAPP on net diversification rates for focal time = 10 Mya.

 Ponerinae_deepSTRAPP_cont_old_calib_10My <- run_deepSTRAPP_for_focal_time(
    contMap = Ponerinae_contMap,
    ace = Ponerinae_ACE,
    tip_data = Ponerinae_cont_tip_data,
    trait_data_type = "continuous",
    BAMM_object = Ponerinae_BAMM_object_old_calib,
    focal_time = focal_time,
    rate_type = "net_diversification",
    uncertainty_strategy = "rates_only",
    return_perm_data = TRUE,
    # Need to be set to TRUE to save diversification data
    extract_diversification_data_melted_df = TRUE,
    # Need to be set to TRUE to save trait data
    extract_trait_data_melted_df = TRUE,
    return_updated_BAMM_object = TRUE)

 ## Explore output
 str(Ponerinae_deepSTRAPP_cont_old_calib_10My, max.level = 1)

 # ----- Plot scatterplot of rates vs. trait values from run_deepSTRAPP_for_focal_time() ----- #

 # Get plot
 rates_vs_trait_output <- plot_rates_vs_trait_data_for_focal_time(
    deepSTRAPP_outputs = Ponerinae_deepSTRAPP_cont_old_calib_10My,
    color_scale = c("grey80", "orange"),
    display_plot = TRUE,
    # PDF_file_path = "./plot_rates_vs_trait_10My.pdf"
    return_mean_data_per_branches_df = TRUE)
 # Adjust aesthetics a posteriori
 rates_vs_trait_ggplot_adj <- rates_vs_trait_output$rates_vs_trait_ggplot +
    ggplot2::theme(plot.title = ggplot2::element_text(color = "red", size = 15))
 print(rates_vs_trait_ggplot_adj)

 # Explore melted data.frame of mean rates and trait values extracted for the given focal time.
 head(rates_vs_trait_output$mean_data_per_branches_df) 

 # ----- Plot scatterplot of rates vs. trait values from run_deepSTRAPP_over_time() ----- #

 ## Load directly outputs from run_deepSTRAPP_over_time()
 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.

 # Select focal_time = 10My
 focal_time <- 10

 # Get plot
 rates_vs_trait_output <- plot_rates_vs_trait_data_for_focal_time(
    deepSTRAPP_outputs = Ponerinae_deepSTRAPP_cont_old_calib_0_40,
    focal_time = focal_time,
    color_scale = c("grey80", "purple"),
    display_plot = TRUE)
    # PDF_file_path = "./plot_rates_vs_trait_10My.pdf"

 # Adjust aesthetics a posteriori
 rates_vs_trait_ggplot_adj <- rates_vs_trait_output$rates_vs_trait_ggplot +
     ggplot2::theme(plot.title = ggplot2::element_text(color = "red", size = 15))
 print(rates_vs_trait_ggplot_adj)


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

 ## Load data

 # Load phylogeny
 data(Ponerinae_tree, package = "deepSTRAPP")
 # Load trait df
 data(Ponerinae_trait_tip_data, 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_states = colors_per_states,
    evolutionary_models = "ARD", # Use default ARD model
    nb_simulations = 100, # Reduce number of simulations to save time
    seed = 1234, # Seet seed for reproducibility
    return_best_model_fit = TRUE,
    return_model_selection_df = TRUE,
    plot_map = FALSE)

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

 ## Set focal time to 10 Mya
 focal_time <- 10

 ## Run deepSTRAPP on net diversification rates for focal time = 10 Mya.

 Ponerinae_deepSTRAPP_cat_3lvl_old_calib_10My <- run_deepSTRAPP_for_focal_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,
    focal_time = focal_time,
    rate_type = "net_diversification",
    uncertainty_strategy = "paired",
    posthoc_pairwise_tests = TRUE,
    return_perm_data = TRUE,
    # Need to be set to TRUE to save diversification data
    extract_diversification_data_melted_df = TRUE,
    # Need to be set to TRUE to save trait data
    extract_trait_data_melted_df = TRUE,
    return_updated_BAMM_object = TRUE)

 ## Explore output
 str(Ponerinae_deepSTRAPP_cat_3lvl_old_calib_10My, max.level = 1)

 ## Plot rates vs. states
 rates_vs_trait_output <- plot_rates_vs_trait_data_for_focal_time(
    deepSTRAPP_outputs = Ponerinae_deepSTRAPP_cat_3lvl_old_calib_10My,
    focal_time = 10,
    select_trait_levels = c("arboreal", "terricolous"), # Select only two levels
    colors_per_levels = colors_per_states[c("arboreal", "terricolous")], # Adjust colors
    display_plot = TRUE,
    # PDF_file_path = "./plot_rates_vs_trait_10My.pdf",
    return_mean_data_per_branches_df = TRUE)

 # Adjust aesthetics a posteriori
 rates_vs_trait_ggplot_adj <- rates_vs_trait_output$rates_vs_trait_ggplot +
    ggplot2::theme(plot.title = ggplot2::element_text(color = "red", size = 15))
 print(rates_vs_trait_ggplot_adj)

 # Explore melted data.frame of mean rates and states extracted for the given focal time.
 head(rates_vs_trait_output$mean_data_per_branches_df) 
 }