Compares the results of predict() and tidypredict_to_column() functions.

tidypredict_test(model, df = model$model, threshold = 1e-12,
  include_intervals = FALSE, max_rows = NULL, xg_df = NULL)

Arguments

model

An R model or a list with a parsed model. It currently supports lm(), glm() and randomForest() models.

df

A data frame that contains all of the needed fields to run the prediction. It defaults to the "model" data frame object inside the model object.

threshold

The number that a given result difference, between predict() and tidypredict_to_column() should not exceed. For continuous predictions, the default value is 0.000000000001 (1e-12), for categorical predictions, the default value is 0.

include_intervals

Switch to indicate if the prediction intervals should be included in the test. It defaults to FALSE.

max_rows

The number of rows in the object passed in the df argument. Highly recommended for large data sets.

xg_df

A xgb.DMatrix object, required only for XGBoost models. It defaults to NULL recommended for large data sets.

Examples

model <- lm(mpg ~ wt + cyl * disp, offset = am, data = mtcars) tidypredict_test(model)
#> tidypredict test results #> Difference threshold: 1e-12 #> #> All results are within the difference threshold