Predict continuous values for a new dataset using a regression model

This function predicts continuous values for new data using a fitted regression model.

Usage

# S3 method for regression_model_fit
predict(object, newdata, sub_indices = NULL, ...)

Arguments

object

A fitted model object of class regression_model_fit.

newdata

New data to predict on, either as a matrix or a NeuroVec or NeuroSurfaceVector object.

sub_indices

A vector of indices used to subset rows of `newdata` (optional).

...

Additional arguments to be passed to the underlying prediction function.

Value

A list containing predicted continuous values with class attributes "regression_prediction", "prediction", and "list".

Examples

# Assuming `fitted_model` is a fitted model object of class `regression_model_fit`
new_data <- iris_dataset$test_data
#> Error in eval(expr, envir, enclos): object 'iris_dataset' not found
predictions <- predict(fitted_model, new_data)
#> Error in eval(expr, envir, enclos): object 'fitted_model' not found