cv_bart: Cross-Validation for BART Model
In glossa: User-Friendly 'shiny' App for Bayesian Species Distribution Models
View source: R/bart_functions.R
cv_bart R Documentation
Cross-Validation for BART Model
Description
This function performs k-fold cross-validation for a Bayesian Additive Regression Trees (BART) model
using presence-absence data and environmental covariate layers. It calculates various performance metrics
for model evaluation.
Usage
cv_bart(data, k = 10, seed = NULL)
Arguments
data
Data frame with a column (named 'pa') indicating presence (1) or absence (0) and columns for the predictor variables.
k
Integer; number of folds for cross-validation (default is 10).
seed
Optional; random seed.
Value
A data frame containing the true positives (TP), false positives (FP), false negatives (FN), true negatives (TN),
and various performance metrics including precision (PREC), sensitivity (SEN), specificity (SPC), false discovery rate (FDR),
negative predictive value (NPV), false negative rate (FNR), false positive rate (FPR), F-score, accuracy (ACC), balanced accuracy (BA),
and true skill statistic (TSS) for each fold.
glossa documentation built on Oct. 15, 2024, 5:08 p.m.
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View source: R/bart_functions.R
| cv_bart | R Documentation |
Cross-Validation for BART Model
Description
This function performs k-fold cross-validation for a Bayesian Additive Regression Trees (BART) model using presence-absence data and environmental covariate layers. It calculates various performance metrics for model evaluation.
Usage
cv_bart(data, k = 10, seed = NULL)
Arguments
data |
Data frame with a column (named 'pa') indicating presence (1) or absence (0) and columns for the predictor variables. |
k |
Integer; number of folds for cross-validation (default is 10). |
seed |
Optional; random seed. |
Value
A data frame containing the true positives (TP), false positives (FP), false negatives (FN), true negatives (TN), and various performance metrics including precision (PREC), sensitivity (SEN), specificity (SPC), false discovery rate (FDR), negative predictive value (NPV), false negative rate (FNR), false positive rate (FPR), F-score, accuracy (ACC), balanced accuracy (BA), and true skill statistic (TSS) for each fold.
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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