autoplot.tariff_effect: Inspect smooth risk-factor effects and tariff-segment...

In insurancerating: Actuarial Tools for Insurance Pricing Models

Inspect smooth risk-factor effects and tariff-segment boundaries

Description

Plot the smooth effect estimated by risk_factor_gam() or inspect that same effect together with candidate boundaries returned by derive_tariff_segments(). Both methods use the same curve, confidence interval, observation and axis layers.

Usage

## S3 method for class 'tariff_segments'
autoplot(
  object,
  confidence = FALSE,
  color_gam = "steelblue",
  show_observations = FALSE,
  color_splits = "grey50",
  size_points = 1,
  color_points = "black",
  rotate_labels = FALSE,
  remove_outliers = NULL,
  conf_int = NULL,
  x_stepsize = NULL,
  show_segments = TRUE,
  ...
)

## S3 method for class 'riskfactor_gam'
autoplot(
  object,
  confidence = FALSE,
  color_gam = "steelblue",
  show_observations = FALSE,
  x_stepsize = NULL,
  size_points = 1,
  color_points = "black",
  rotate_labels = FALSE,
  remove_outliers = NULL,
  conf_int = NULL,
  ...
)

Arguments

object	An object returned by risk_factor_gam() or derive_tariff_segments().
confidence	Logical. If TRUE, add pointwise 95 percent confidence intervals where finite values are available.
color_gam	Colour for the fitted GAM line.
show_observations	Logical. If TRUE, add the aggregated observed experience used for fitting.
color_splits	Colour for segment boundaries. Used only for a tariff_segments object.
size_points	Numeric point size for observed experience.
color_points	Colour for observed experience.
rotate_labels	Logical. If TRUE, rotate x-axis labels by 45 degrees.
remove_outliers	Optional single numeric upper display limit for observed points. The fitted curve remains unchanged.
conf_int	Deprecated. Use confidence instead.
x_stepsize	Optional positive numeric step size for x-axis tick marks. If NULL, breaks are determined automatically.
show_segments	Logical. For a tariff_segments object, show the candidate segment boundaries when TRUE. Default is TRUE.
...	Additional arguments reserved for method compatibility.

Details

The fitted line is shown on its natural response scale: claim frequency, average severity or risk premium. Optional observed points represent portfolio experience aggregated at the continuous risk-factor values used for fitting.

For a tariff_segments object, vertical lines show the derived interval boundaries. These lines support actuarial review of where the continuous effect changes sufficiently to motivate a categorical tariff treatment. Set show_segments = FALSE to inspect only the underlying smooth curve.

Confidence intervals describe uncertainty in the fitted curve conditional on the selected GAM specification. They do not include uncertainty from model selection, omitted risk factors or future portfolio changes. Segment boundaries do not by themselves demonstrate that adjacent segments are statistically or commercially distinct. Exposure, claim volume, temporal stability and operational tariff constraints should be considered separately.

remove_outliers affects displayed observed points only. It does not remove observations from the fitted GAM or alter the prediction curve or segment boundaries.

Value

A ggplot2 object.

Author(s)

Martin Haringa

See Also

risk_factor_gam(), derive_tariff_segments(), add_tariff_segments()

Examples

## Not run: 
fit <- risk_factor_gam(
  MTPL,
  risk_factor = "age_policyholder",
  claim_count = "nclaims",
  exposure = "exposure"
)

# Inspect the continuous effect before deriving tariff segments.
autoplot(fit, confidence = TRUE, show_observations = TRUE)

segments <- derive_tariff_segments(
  fit,
  segmentation_penalty = 10,
  seed = 1
)

# Inspect the same effect with the candidate segment boundaries.
autoplot(segments, confidence = TRUE, show_observations = TRUE)
autoplot(segments, show_segments = FALSE)

## End(Not run)

insurancerating documentation built on Sept. 1, 2026, 5:08 p.m.