add_prediction: Add model predictions to a pricing data set

In insurancerating: Actuarial Tools for Insurance Pricing Models

Add model predictions to a pricing data set

Description

add_prediction() adds predictions from one or more fitted glm models to a data frame.

In pricing workflows, this is often used to bring frequency and severity model output together on the same portfolio. For example, predicted claim frequency and predicted average claim amount can be multiplied to create a pure premium proxy before further tariff refinement.

The function is deliberately small: it does not refit models or decide how predictions should be combined. It only adds model predictions, and optionally confidence intervals, using clear output column names.

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.

Details

Predictions are calculated on the response scale using stats::predict(..., type = "response"). For GLMs with a log link, such as Poisson frequency models or Gamma severity models, the added columns are therefore already on the original scale.

If confidence = TRUE, lower and upper confidence interval columns are added next to each prediction column. The default interval suffixes are "lower" and "upper".

Predictions containing missing values are retained. If one or more NA predictions are produced, the function issues a warning with the affected prediction columns and number of missing predictions. This is typically caused by missing predictor values in data or by predictor values outside the domain supported by the fitted model.

Value

A data.frame containing the original data and additional columns for model predictions. If confidence = TRUE, confidence interval columns are added as well.

Author(s)

Martin Haringa

Examples

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

# Add predicted claim frequency
mtpl_pred <- add_prediction(MTPL, mod1, predictions = "pred_frequency")

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

# Combine frequency and severity predictions into a pure premium proxy
freq <- glm(nclaims ~ bm + zip,
            data = MTPL,
            offset = log(exposure),
            family = poisson())

sev <- glm(amount ~ bm + zip,
           data = MTPL[MTPL$amount > 0, ],
           weights = nclaims,
           family = Gamma(link = "log"))

premium_proxy <- add_prediction(
  MTPL,
  freq,
  sev,
  predictions = c("pred_frequency", "pred_severity")
)

premium_proxy$pred_pure_premium <-
  premium_proxy$pred_frequency * premium_proxy$pred_severity

insurancerating documentation built on July 30, 2026, 5:09 p.m.