plot_rates_vs_trait_data_for_focal_time: Plot rates vs. trait data for a given focal time
In deepSTRAPP: Test for Differences in Diversification Rates over Time
plot_rates_vs_trait_data_for_focal_time R Documentation
Plot rates vs. trait data for a given focal time
Description
Plot rates vs. trait data 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 rates vs. trait data 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_rates_vs_trait_data_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).
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
Numerical. (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
Vector of character string. 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_rates_vs_trait_data_df
Logical. Whether to include in the output the data.frame of mean rates per trait values/states/ranges computed for
each posterior sample at the focal time. Default is FALSE.
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 return_updated_trait_data_with_Map = 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.
-
return_updated_trait_data_with_Map must be set to TRUE so that the trait data used to compute the tests are returned among the outputs
under $updated_trait_data_with_Map_over_time. Alternatively, and more efficiently, extract_trait_data_melted_df can be set to TRUE
so that trait data are already 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 $diversification_data_df_over_time.
For plotting all time-steps at once, see plot_rates_vs_trait_data_over_time().
Value
The function returns a list with at least one element.
-
rates_vs_trait_ggplot An object of classes gg and ggplot. This is a ggplot that can be displayed
on the console with print(output$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 modify for aesthetics using the ggplot2 grammar.
If the trait data are 'continuous', the plot is a scatter plot showing how diversification rates varies with trait values.
If the trait data are 'categorical' or 'biogeographic', the plot is a boxplot showing diversification rates per states/ranges.
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 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.
The p-value of the associated STRAPP test.
Optional summary data.frame:
-
mean_rates_vs_trait_data_df A data.frame with three columns providing the $mean_rates and $trait_value
observed along branches at focal_time. Rates are averaged across all BAMM posterior samples.
This is the raw data used to draw the plot. Included if return_mean_rates_vs_trait_data_df = TRUE.
If a PDF_file_path is provided, the function will also generate a PDF file of the plot.
Author(s)
Maël Doré
See Also
Associated functions in deepSTRAPP: run_deepSTRAPP_for_focal_time() plot_rates_vs_trait_data_over_time()
Examples
if (deepSTRAPP::is_dev_version())
{
# ----- Example 1: Continuous trait ----- #
# Load fake trait df
data(Ponerinae_trait_tip_data, package = "deepSTRAPP")
# Load phylogeny with old calibration
data(Ponerinae_tree_old_calib, package = "deepSTRAPP")
# Load the BAMM_object summarizing 1000 posterior samples of BAMM
data(Ponerinae_BAMM_object_old_calib, package = "deepSTRAPP")
## This dataset is only available in development versions installed from GitHub.
# It is not available in CRAN versions.
# Use remotes::install_github(repo = "MaelDore/deepSTRAPP") to get the latest development version.
## Prepare trait data
# Extract continuous trait data as a named vector
Ponerinae_cont_tip_data <- setNames(object = Ponerinae_trait_tip_data$fake_cont_tip_data,
nm = Ponerinae_trait_tip_data$Taxa)
# Select a color scheme from lowest to highest values
color_scale = c("darkgreen", "limegreen", "orange", "red")
# Get Ancestral Character Estimates based on a Brownian Motion model
# To obtain values at internal nodes
Ponerinae_ACE <- phytools::fastAnc(tree = Ponerinae_tree_old_calib, x = Ponerinae_cont_tip_data)
# (May take several minutes to run)
# Run a Stochastic Mapping based on a Brownian Motion model
# to interpolate values along branches and obtain a "contMap" object
Ponerinae_contMap <- phytools::contMap(Ponerinae_tree_old_calib, x = Ponerinae_cont_tip_data,
res = 100, # Number of time steps
plot = FALSE)
# Plot contMap = stochastic mapping of continuous trait
plot_contMap(contMap = Ponerinae_contMap,
color_scale = color_scale)
## Set focal time to 10 Mya
focal_time <- 10
## Run deepSTRAPP on net diversification rates for focal time = 10 Mya.
Ponerinae_deepSTRAPP_cont_old_calib_10My <- run_deepSTRAPP_for_focal_time(
contMap = Ponerinae_contMap,
ace = Ponerinae_ACE,
tip_data = Ponerinae_cont_tip_data,
trait_data_type = "continuous",
BAMM_object = Ponerinae_BAMM_object_old_calib,
focal_time = focal_time,
rate_type = "net_diversification",
return_perm_data = TRUE,
# Need to be set to TRUE to save diversification data
extract_diversification_data_melted_df = TRUE,
# Need to be set to TRUE to save trait data
return_updated_trait_data_with_Map = TRUE,
return_updated_BAMM_object = TRUE)
## Explore output
str(Ponerinae_deepSTRAPP_cont_old_calib_10My, max.level = 1)
# ----- Plot histogram of STRAPP overall test results from run_deepSTRAPP_for_focal_time() ----- #
# Get plot
rates_vs_trait_output <- plot_rates_vs_trait_data_for_focal_time(
deepSTRAPP_outputs = deepPonerinae_deepSTRAPP_cont_old_calib_10My,
color_scale = c("grey80", "orange"),
display_plot = TRUE,
# PDF_file_path = "./plot_rates_vs_trait_10My.pdf"
return_mean_rates_vs_trait_data_df = TRUE)
# Adjust aesthetics a posteriori
rates_vs_trait_ggplot_adj <- rates_vs_trait_output$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$mean_rates_vs_trait_data_df)
# ----- Plot histogram of STRAPP overall test results from run_deepSTRAPP_over_time() ----- #
## Load directly outputs from run_deepSTRAPP_over_time()
data(Ponerinae_deepSTRAPP_cont_old_calib_0_40, package = "deepSTRAPP")
## This dataset is only available in development versions installed from GitHub.
# It is not available in CRAN versions.
# Use remotes::install_github(repo = "MaelDore/deepSTRAPP") to get the latest development version.
# Select focal_time = 10My
focal_time <- 10
# Get plot
rates_vs_trait_output <- plot_rates_vs_trait_data_for_focal_time(
deepSTRAPP_outputs = Ponerinae_deepSTRAPP_cont_old_calib_0_40,
focal_time = focal_time,
color_scale = c("grey80", "purple"),
display_plot = TRUE)
# PDF_file_path = "./plot_rates_vs_trait_10My.pdf"
# Adjust aesthetics a posteriori
rates_vs_trait_ggplot_adj <- rates_vs_trait_output$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,
trait_data_type = "categorical",
BAMM_object = Ponerinae_BAMM_object_old_calib,
focal_time = focal_time,
rate_type = "net_diversification",
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
return_updated_trait_data_with_Map = 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_rates_vs_trait_data_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_rates_vs_trait_data_df)
}
deepSTRAPP documentation built on Jan. 20, 2026, 1:06 a.m.
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| plot_rates_vs_trait_data_for_focal_time | R Documentation |
Plot rates vs. trait data for a given focal time
Description
Plot rates vs. trait data 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 rates vs. trait data 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_rates_vs_trait_data_df = FALSE
)
Arguments
deepSTRAPP_outputs |
List of elements generated with |
focal_time |
Numerical. (Optional) If |
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 |
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 |
color_scale |
Vector of character string. List of colors to use to build the color scale with |
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 |
display_plot |
Logical. Whether to display the plot generated in the R console. Default is |
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_rates_vs_trait_data_df |
Logical. Whether to include in the output the data.frame of mean rates per trait values/states/ranges computed for
each posterior sample at the focal time. Default is |
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 return_updated_trait_data_with_Map = 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.
-
return_updated_trait_data_with_Mapmust be set toTRUEso that the trait data used to compute the tests are returned among the outputs under$updated_trait_data_with_Map_over_time. Alternatively, and more efficiently,extract_trait_data_melted_dfcan be set toTRUEso that trait data are already returned in a melted data.frame among the outputs under$trait_data_df_over_time. -
extract_diversification_data_melted_dfmust be set toTRUEso that the diversification rates are returned among the outputs under$diversification_data_df_over_time.
For plotting all time-steps at once, see plot_rates_vs_trait_data_over_time().
Value
The function returns a list with at least one element.
-
rates_vs_trait_ggplotAn object of classesggandggplot. This is a ggplot that can be displayed on the console withprint(output$rates_vs_trait_ggplot). It corresponds to the plot being displayed on the console when the function is run, ifdisplay_plot = TRUE, and can be further modify for aesthetics using the ggplot2 grammar.
If the trait data are 'continuous', the plot is a scatter plot showing how diversification rates varies with trait values. If the trait data are 'categorical' or 'biogeographic', the plot is a boxplot showing diversification rates per states/ranges.
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 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.
The p-value of the associated STRAPP test.
Optional summary data.frame:
-
mean_rates_vs_trait_data_dfA data.frame with three columns providing the$mean_ratesand$trait_valueobserved along branches atfocal_time. Rates are averaged across all BAMM posterior samples. This is the raw data used to draw the plot. Included ifreturn_mean_rates_vs_trait_data_df = TRUE.
If a PDF_file_path is provided, the function will also generate a PDF file of the plot.
Author(s)
Maël Doré
See Also
Associated functions in deepSTRAPP: run_deepSTRAPP_for_focal_time() plot_rates_vs_trait_data_over_time()
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",
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
return_updated_trait_data_with_Map = TRUE,
return_updated_BAMM_object = TRUE)
## Explore output
str(Ponerinae_deepSTRAPP_cont_old_calib_10My, max.level = 1)
# ----- Plot histogram of STRAPP overall test results from run_deepSTRAPP_for_focal_time() ----- #
# Get plot
rates_vs_trait_output <- plot_rates_vs_trait_data_for_focal_time(
deepSTRAPP_outputs = deepPonerinae_deepSTRAPP_cont_old_calib_10My,
color_scale = c("grey80", "orange"),
display_plot = TRUE,
# PDF_file_path = "./plot_rates_vs_trait_10My.pdf"
return_mean_rates_vs_trait_data_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_rates_vs_trait_data_df)
# ----- Plot histogram of STRAPP overall test results from run_deepSTRAPP_over_time() ----- #
## Load directly outputs from run_deepSTRAPP_over_time()
data(Ponerinae_deepSTRAPP_cont_old_calib_0_40, package = "deepSTRAPP")
## This dataset is only available in development versions installed from GitHub.
# It is not available in CRAN versions.
# Use remotes::install_github(repo = "MaelDore/deepSTRAPP") to get the latest development version.
# 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$ace,
tip_data = Ponerinae_cat_3lvl_tip_data,
trait_data_type = "categorical",
BAMM_object = Ponerinae_BAMM_object_old_calib,
focal_time = focal_time,
rate_type = "net_diversification",
posthoc_pairwise_tests = TRUE,
return_perm_data = TRUE,
# Need to be set to TRUE to save diversification data
extract_diversification_data_melted_df = TRUE,
# Need to be set to TRUE to save trait data
return_updated_trait_data_with_Map = TRUE,
return_updated_BAMM_object = TRUE)
## Explore output
str(Ponerinae_deepSTRAPP_cat_3lvl_old_calib_10My, max.level = 1)
## Plot rates vs. states
rates_vs_trait_output <- plot_rates_vs_trait_data_for_focal_time(
deepSTRAPP_outputs = Ponerinae_deepSTRAPP_cat_3lvl_old_calib_10My,
focal_time = 10,
select_trait_levels = c("arboreal", "terricolous"), # Select only two levels
colors_per_levels = colors_per_states[c("arboreal", "terricolous")], # Adjust colors
display_plot = TRUE,
# PDF_file_path = "./plot_rates_vs_trait_10My.pdf",
return_mean_rates_vs_trait_data_df = TRUE)
# Adjust aesthetics a posteriori
rates_vs_trait_ggplot_adj <- rates_vs_trait_output$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_rates_vs_trait_data_df)
}
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