Add Model Predictions to a Data Frame — add_prediction • insurancerating

Adds predictions (and optionally confidence intervals) from one or more glm models to a data frame.

Usage

add_prediction(
  data,
  ...,
  predictions = NULL,
  prefix = "pred",
  confidence = FALSE,
  interval_names = c("lower", "upper"),
  alpha = 0.1,
  var = NULL,
  conf_int = NULL
)

Arguments

data: A data.frame containing the new data for which predictions should be generated.
...: One or more fitted model objects of class "glm".
predictions: Optional character vector giving names for the new prediction columns. Must have the same length as the number of models supplied. If NULL (default), names are generated automatically using prefix, the model response, and the model object name.
prefix: Character. Prefix used for automatically generated prediction column names. Default is "pred".
confidence: Logical. If TRUE, add confidence intervals for predictions. Default is FALSE.
interval_names: Character vector of length two. Names appended to the prediction column name for lower and upper confidence interval bounds. Default is c("lower", "upper").
alpha: Numeric between 0 and 1. Controls the miscoverage level for interval estimates. Default is 0.10, corresponding to a 90% confidence interval.
var: Deprecated. Use predictions instead.
conf_int: Deprecated. Use confidence instead.

Value

A data.frame containing the original data along with additional columns for model predictions (and confidence intervals if requested).

Author

Martin Haringa

Examples

mod1 <- glm(nclaims ~ age_policyholder,
            data = MTPL,
            offset = log(exposure),
            family = poisson())

# Add predicted values
mtpl_pred <- add_prediction(MTPL, mod1)

# Add predicted values with confidence bounds
mtpl_pred_ci <- add_prediction(MTPL, mod1, confidence = TRUE)