plot_rates_vs_trait_data_over_time: Plot rates vs. trait data over time-steps
In deepSTRAPP: Test for Differences in Diversification Rates over Time
plot_rates_vs_trait_data_over_time R Documentation
Plot rates vs. trait data over time-steps
Description
Plot rates vs. trait data as extracted for all trait_data_dftime-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_rates_vs_trait_data_df = FALSE
)
Arguments
deepSTRAPP_outputs
List of elements generated with run_deepSTRAPP_over_time()
that runs the whole deepSTRAPP workflow over multiple time-steps.
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 over all time-steps. Default is FALSE.
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().
-
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 a single focal_time, see plot_rates_vs_trait_data_for_focal_time().
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 modify for aesthetics using the ggplot2 grammar.
If the trait data are 'continuous', the plots are scatter plots showing how diversification rates varies with trait values.
If the trait data are 'categorical' or 'biogeographic', the plots are boxplots 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 the different focal_time. Rates are averaged across all BAMM posterior samples.
This is the raw data used to draw each plot for each focal_time. Included if return_mean_rates_vs_trait_data_df = TRUE.
If a PDF_file_path is provided, the function will also generate a unique PDF file with one plot/page per $time_steps.
Author(s)
Maël Doré
See Also
Associated functions in deepSTRAPP: run_deepSTRAPP_over_time() plot_rates_vs_trait_data_for_focal_time()
Examples
if (deepSTRAPP::is_dev_version())
{
# ----- Example 1: Continuous trait ----- #
# Load fake trait df
data(Ponerinae_trait_tip_data, package = "deepSTRAPP")
# Load phylogeny with old calibration
data(Ponerinae_tree_old_calib, package = "deepSTRAPP")
# Load the BAMM_object summarizing 1000 posterior samples of BAMM
data(Ponerinae_BAMM_object_old_calib, package = "deepSTRAPP")
## This dataset is only available in development versions installed from GitHub.
# It is not available in CRAN versions.
# Use remotes::install_github(repo = "MaelDore/deepSTRAPP") to get the latest development version.
## Prepare trait data
# Extract continuous trait data as a named vector
Ponerinae_cont_tip_data <- setNames(object = Ponerinae_trait_tip_data$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 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,
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_trait_data_with_Map = TRUE,
return_updated_BAMM_object = TRUE,
verbose = TRUE,
verbose_extended = TRUE)
## Load directly trait data output
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.
# 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_rates_vs_trait_data_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$mean_rates_vs_trait_data_df)
# ----- Example 2: Categorical data ----- #
## Load data
# Load trait df
data(Ponerinae_trait_tip_data, package = "deepSTRAPP")
# Load phylogeny
data(Ponerinae_tree_old_calib, package = "deepSTRAPP")
# Load the BAMM_object summarizing 1000 posterior samples of BAMM
data(Ponerinae_BAMM_object_old_calib, package = "deepSTRAPP")
## This dataset is only available in development versions installed from GitHub.
# It is not available in CRAN versions.
# Use remotes::install_github(repo = "MaelDore/deepSTRAPP") to get the latest development version.
## Prepare trait data
# Extract categorical data with 3-levels
Ponerinae_cat_3lvl_tip_data <- setNames(object = Ponerinae_trait_tip_data$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$ace,
tip_data = Ponerinae_cat_3lvl_tip_data,
trait_data_type = "categorical",
BAMM_object = Ponerinae_BAMM_object_old_calib,
# nb_time_steps = nb_time_steps,
time_range = time_range,
time_step_duration = time_step_duration,
rate_type = "net_diversification",
seed = 1234, # Set for reproducibility
alpha = 0.10, # Select a generous level of significance for the sake of the example
posthoc_pairwise_tests = TRUE,
return_perm_data = TRUE,
# Need to be set to TRUE to save trait data
extract_trait_data_melted_df = TRUE,
# Need to be set to TRUE to save diversification data
extract_diversification_data_melted_df = TRUE,
return_STRAPP_results = TRUE,
return_updated_trait_data_with_Map = TRUE,
return_updated_BAMM_object = TRUE,
verbose = TRUE,
verbose_extended = TRUE)
## Load directly deepSTRAPP output
data(Ponerinae_deepSTRAPP_cat_3lvl_old_calib_0_40, package = "deepSTRAPP")
## This dataset is only available in development versions installed from GitHub.
# It is not available in CRAN versions.
# Use remotes::install_github(repo = "MaelDore/deepSTRAPP") to get the latest development version.
# Explore output
str(Ponerinae_deepSTRAPP_cat_3lvl_old_calib_0_40, max.level = 1)
# Adjust color scheme
colors_per_states <- c("orange", "dodgerblue", "red")
names(colors_per_states) <- c("arboreal", "subterranean", "terricolous")
## Plot for all time-steps
rates_vs_trait_outputs <- plot_rates_vs_trait_data_over_time(
deepSTRAPP_outputs = Ponerinae_deepSTRAPP_cat_3lvl_old_calib_0_40,
colors_per_levels = colors_per_states, # Adjust color scheme
display_plot = TRUE,
# PDF_file_path = "./plot_rates_vs_trait_0_40My.pdf",
return_mean_rates_vs_trait_data_df = TRUE)
## Print plot for time step 3 = 10 My
print(rates_vs_trait_outputs$rates_vs_trait_ggplots[[3]])
## Explore melted data.frame of rates and states
head(rates_vs_trait_outputs$mean_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_over_time | R Documentation |
Plot rates vs. trait data over time-steps
Description
Plot rates vs. trait data as extracted for all trait_data_dftime-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_rates_vs_trait_data_df = FALSE
)
Arguments
deepSTRAPP_outputs |
List of elements generated with |
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 over all time-steps. Default is |
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().
-
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 a single focal_time, see plot_rates_vs_trait_data_for_focal_time().
Value
The function returns a list with at least one element.
-
rates_vs_trait_ggplotsA list of objects of classesggandggplotordered as in$time_steps. Each element corresponds to a ggplot for a givenfocal_time. They can be displayed on the console withprint(output$rates_vs_trait_ggplots[[i]]). They correspond to the plots being displayed on the console one by one 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 plots are scatter plots showing how diversification rates varies with trait values. If the trait data are 'categorical' or 'biogeographic', the plots are boxplots 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 at the differentfocal_time. Rates are averaged across all BAMM posterior samples. This is the raw data used to draw each plot for eachfocal_time. Included ifreturn_mean_rates_vs_trait_data_df = TRUE.
If a PDF_file_path is provided, the function will also generate a unique PDF file with one plot/page per $time_steps.
Author(s)
Maël Doré
See Also
Associated functions in deepSTRAPP: run_deepSTRAPP_over_time() plot_rates_vs_trait_data_for_focal_time()
Examples
if (deepSTRAPP::is_dev_version())
{
# ----- Example 1: Continuous trait ----- #
# Load fake trait df
data(Ponerinae_trait_tip_data, package = "deepSTRAPP")
# Load phylogeny with old calibration
data(Ponerinae_tree_old_calib, package = "deepSTRAPP")
# Load the BAMM_object summarizing 1000 posterior samples of BAMM
data(Ponerinae_BAMM_object_old_calib, package = "deepSTRAPP")
## This dataset is only available in development versions installed from GitHub.
# It is not available in CRAN versions.
# Use remotes::install_github(repo = "MaelDore/deepSTRAPP") to get the latest development version.
## Prepare trait data
# Extract continuous trait data as a named vector
Ponerinae_cont_tip_data <- setNames(object = Ponerinae_trait_tip_data$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 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,
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_trait_data_with_Map = TRUE,
return_updated_BAMM_object = TRUE,
verbose = TRUE,
verbose_extended = TRUE)
## Load directly trait data output
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.
# 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_rates_vs_trait_data_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$mean_rates_vs_trait_data_df)
# ----- Example 2: Categorical data ----- #
## Load data
# Load trait df
data(Ponerinae_trait_tip_data, package = "deepSTRAPP")
# Load phylogeny
data(Ponerinae_tree_old_calib, package = "deepSTRAPP")
# Load the BAMM_object summarizing 1000 posterior samples of BAMM
data(Ponerinae_BAMM_object_old_calib, package = "deepSTRAPP")
## This dataset is only available in development versions installed from GitHub.
# It is not available in CRAN versions.
# Use remotes::install_github(repo = "MaelDore/deepSTRAPP") to get the latest development version.
## Prepare trait data
# Extract categorical data with 3-levels
Ponerinae_cat_3lvl_tip_data <- setNames(object = Ponerinae_trait_tip_data$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$ace,
tip_data = Ponerinae_cat_3lvl_tip_data,
trait_data_type = "categorical",
BAMM_object = Ponerinae_BAMM_object_old_calib,
# nb_time_steps = nb_time_steps,
time_range = time_range,
time_step_duration = time_step_duration,
rate_type = "net_diversification",
seed = 1234, # Set for reproducibility
alpha = 0.10, # Select a generous level of significance for the sake of the example
posthoc_pairwise_tests = TRUE,
return_perm_data = TRUE,
# Need to be set to TRUE to save trait data
extract_trait_data_melted_df = TRUE,
# Need to be set to TRUE to save diversification data
extract_diversification_data_melted_df = TRUE,
return_STRAPP_results = TRUE,
return_updated_trait_data_with_Map = TRUE,
return_updated_BAMM_object = TRUE,
verbose = TRUE,
verbose_extended = TRUE)
## Load directly deepSTRAPP output
data(Ponerinae_deepSTRAPP_cat_3lvl_old_calib_0_40, package = "deepSTRAPP")
## This dataset is only available in development versions installed from GitHub.
# It is not available in CRAN versions.
# Use remotes::install_github(repo = "MaelDore/deepSTRAPP") to get the latest development version.
# Explore output
str(Ponerinae_deepSTRAPP_cat_3lvl_old_calib_0_40, max.level = 1)
# Adjust color scheme
colors_per_states <- c("orange", "dodgerblue", "red")
names(colors_per_states) <- c("arboreal", "subterranean", "terricolous")
## Plot for all time-steps
rates_vs_trait_outputs <- plot_rates_vs_trait_data_over_time(
deepSTRAPP_outputs = Ponerinae_deepSTRAPP_cat_3lvl_old_calib_0_40,
colors_per_levels = colors_per_states, # Adjust color scheme
display_plot = TRUE,
# PDF_file_path = "./plot_rates_vs_trait_0_40My.pdf",
return_mean_rates_vs_trait_data_df = TRUE)
## Print plot for time step 3 = 10 My
print(rates_vs_trait_outputs$rates_vs_trait_ggplots[[3]])
## Explore melted data.frame of rates and states
head(rates_vs_trait_outputs$mean_rates_vs_trait_data_df)
}
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