response_curve_bart: Calculate Response Curve Using BART Model
In glossa: User-Friendly 'shiny' App for Bayesian Species Distribution Models
View source: R/bart_functions.R
response_curve_bart R Documentation
Calculate Response Curve Using BART Model
Description
This function calculates the response curve (functional responses) using a Bayesian Additive Regression Trees (BART) model.
Usage
response_curve_bart(bart_model, data, predictor_names)
Arguments
bart_model
A BART model object obtained from fitting BART ('dbarts::bart').
data
A data frame containing the predictor variables (the design matrix) used in the BART model.
predictor_names
A character vector containing the names of the predictor variables.
Value
A list containing a data frame for each independent variable with mean, 2.5th percentile, 97.5th percentile, and corresponding values of the variables.
glossa documentation built on Oct. 15, 2024, 5:08 p.m.
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View source: R/bart_functions.R
| response_curve_bart | R Documentation |
Calculate Response Curve Using BART Model
Description
This function calculates the response curve (functional responses) using a Bayesian Additive Regression Trees (BART) model.
Usage
response_curve_bart(bart_model, data, predictor_names)
Arguments
bart_model |
A BART model object obtained from fitting BART ('dbarts::bart'). |
data |
A data frame containing the predictor variables (the design matrix) used in the BART model. |
predictor_names |
A character vector containing the names of the predictor variables. |
Value
A list containing a data frame for each independent variable with mean, 2.5th percentile, 97.5th percentile, and corresponding values of the variables.
R Package Documentation
Browse R Packages
We want your feedback!
Note that we can't provide technical support on individual packages. You should contact the package authors for that.
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