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Calculate Performance Metrics for Regression Result

Source: R/performance.R
performance.regression_result.Rd

This function calculates performance metrics for a regression result object, including R-squared, Root Mean Squared Error (RMSE), and Spearman correlation.

Usage

# S3 method for class 'regression_result'
performance(x, split_list = NULL, ...)

Arguments

x

A regression_result object.

split_list

Optional named list of split index groups for computing metrics on sub-groups in addition to the full result.

...

extra args (not used).

Value

A named vector with the calculated performance metrics: R-squared, RMSE, and Spearman correlation.

Details

The function calculates the following performance metrics for the given regression result object: - R-squared: proportion of variance in the observed data that is predictable from the fitted model. - RMSE: root mean squared error, a measure of the differences between predicted and observed values. - Spearman correlation: a measure of the monotonic relationship between predicted and observed values.

See also

regression_result

Examples

res <- regression_result(observed = c(1.0, 2.1, 2.9, 4.2, 5.1),
                         predicted = c(1.2, 1.9, 3.1, 3.8, 5.0),
                         testind = 1:5)
performance(res)
#>        R2      RMSE  spearcor 
#> 0.9786905 0.2408319 1.0000000