autoplot.factor_analysis: Plot observed portfolio experience by risk factor

In insurancerating: Actuarial Tools for Insurance Pricing Models

Plot observed portfolio experience by risk factor

Description

Visualise the exposure and observed actuarial metrics calculated by factor_analysis(). The plots support assessment of portfolio composition, observed frequency, severity, risk premium and loss ratio across risk-factor levels.

The observed patterns are descriptive. They do not adjust for correlations with other risk factors and should therefore not be interpreted as multivariate tariff relativities.

Usage

## S3 method for class 'factor_analysis'
autoplot(
  object,
  metrics = NULL,
  ncol = 1,
  legend_position = c("right", "bottom", "top", "left", "none"),
  show_exposure = TRUE,
  show_exposure_labels = TRUE,
  sort_by_exposure = FALSE,
  level_order = NULL,
  decimal_mark = ",",
  line_color = NULL,
  bar_fill = NULL,
  abbreviate_labels = TRUE,
  label_width = 20,
  label_abbreviations = NULL,
  flip_bars = FALSE,
  show_total = FALSE,
  total_color = NULL,
  total_name = NULL,
  rotate_angle = NULL,
  custom_theme = NULL,
  remove_underscores = FALSE,
  compact_x_axis = TRUE,
  show_plots = NULL,
  background = NULL,
  labels = NULL,
  sort = NULL,
  sort_manual = NULL,
  dec.mark = NULL,
  color = NULL,
  color_bg = NULL,
  coord_flip = NULL,
  remove_x_elements = NULL,
  ...
)

Arguments

object	A factor_analysis or univariate object produced by factor_analysis() or univariate().
metrics	Numeric or character vector specifying which metrics to plot (default is all available metrics). The numeric positions are: 1. Frequency (nclaims / exposure) 2. Average severity (claim_amount / claim_count) 3. Risk premium (claim_amount / exposure) 4. Loss ratio (claim_amount / premium) 5. Average premium (premium / exposure) 6. Exposure 7. Severity 8. Number of claims 9. Premium Character values can be "frequency", "average_severity", "risk_premium", "loss_ratio", "average_premium", "exposure", "claim_amount", "claim_count", and "premium".
ncol	Positive whole number. Number of columns in the plot composition.
legend_position	Character string specifying the legend position. Supported values are "right", "bottom", "top", "left" and "none". A legend is present only when the factor_analysis object was created with group_by, because the resulting group-specific series are distinguished by colour. Without group_by, colours are fixed plot styles and no legend is drawn; in that case this argument has no visible effect.
show_exposure	Show exposure as background bars behind line plots (default = TRUE).
show_exposure_labels	Show labels with the exposure bars (default = TRUE).
sort_by_exposure	Sort risk factor levels into descending order by exposure (default = FALSE).
level_order	Custom order for risk factor levels; character vector (default = NULL).
decimal_mark	Decimal mark; defaults to ",".
line_color	Optional override for line/point color. If NULL (default), colors are taken from the internal palette. If specified, the chosen color is applied to all line-based plots.
bar_fill	Optional override for background bar color. If NULL (default), the background color is taken from the internal palette. If specified, the chosen color is applied to all background bars.
abbreviate_labels	Logical. If TRUE, long risk-factor level labels are shortened to label_width characters. A shortened label ends in one period; for example, "Bouwnijverheid" becomes "Bouwn." when label_width = 6. Only the displayed axis labels are changed.
label_width	Positive whole number of at least 2. Maximum number of characters in automatically shortened level labels.
label_abbreviations	Optional named character vector with explicit display labels, for example c("Bouwnijverheid" = "Bouwn.", "Onroerend goed" = "Onr. goed"). Explicit labels take precedence over automatic shortening.
flip_bars	Logical. If TRUE, flip cartesian coordinates for bar plots (metrics 6 to 9). This option does not affect the line-based plots for metrics 1 to 5.
show_total	Show line for total if by is used (default = FALSE).
total_color	Color for total line (default = "black").
total_name	Legend name for total line (default = NULL).
rotate_angle	Numeric value for angle of labels on the x-axis (degrees).
custom_theme	List with customized theme options.
remove_underscores	Logical; remove underscores from labels (default = FALSE).
compact_x_axis	Logical. When TRUE and ncol == 1, x-axis components are removed from all plots except the last one. The following elements are suppressed: axis.title.x axis.text.x axis.ticks.x This prevents duplicated x-axes in vertically stacked patchwork plots. Defaults to TRUE.
show_plots	Deprecated. Use metrics instead.
background	Deprecated alias for show_exposure.
labels	Deprecated alias for show_exposure_labels.
sort	Deprecated alias for sort_by_exposure.
sort_manual	Deprecated alias for level_order.
dec.mark	Deprecated alias for decimal_mark.
color	Deprecated alias for line_color.
color_bg	Deprecated alias for bar_fill.
coord_flip	Deprecated alias for flip_bars.
remove_x_elements	Deprecated alias for compact_x_axis.
...	Currently unused.

Details

For rate-based metrics, exposure can be shown as background bars so that observed level differences can be interpreted together with portfolio volume. Levels with little exposure or few claims can produce volatile observed metrics and should be assessed accordingly.

When the factor analysis contains one or more by variables, separate observed series are shown. show_total = TRUE adds the aggregate portfolio series for comparison. Sorting and manual level ordering affect only the presentation; the underlying summaries are unchanged.

A rate or ratio is not shown for a risk-factor level when its denominator is zero or missing. For example, average severity is undefined when claim count is zero, and loss ratio is undefined when premium is zero. autoplot() gives one combined warning identifying the affected metrics and levels. Other valid metrics and exposure information remain in the figure.

Value

A patchwork composition containing the requested ggplot panels.

Author(s)

Marc Haine, Martin Haringa

See Also

factor_analysis(), add_portfolio_experience()

Examples

# Plot observed frequency and risk premium
x <- factor_analysis(MTPL2,
                     x = "area",
                     severity = "amount",
                     nclaims = "nclaims",
                     exposure = "exposure")
autoplot(x, metrics = c("frequency", "risk_premium"))

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