insurancerating (version 0.8.2)

as_gt: Convert an object to a gt table

Description

Generic presentation helper. Methods return a gt table for objects where a formatted reporting table is more useful than another plot.

Create a formatted gt table from an object returned by assess_excess_threshold(). The original object remains a regular data.frame subclass; as_gt() is only used when a presentation table is needed for a report, tariff note or pricing review.

Format the coefficient-level result from bootstrap_coefficients() as a gt table. The table retains the requested link or exponentiated scale and shows how many bootstrap estimates were available for each coefficient.

Create a formatted gt table from an object returned by rating_table(). Risk factors are presented as row groups, while fitted model effects are shown as relativities or coefficients depending on the scale selected in rating_table().

Usage

as_gt(x, ...)
# S3 method for threshold_assessment
as_gt(
  x,
  claims = TRUE,
  loss = FALSE,
  premium = TRUE,
  locale = "nl-NL",
  loss_decimals = 0,
  premium_decimals = 0,
  ratio_decimals = 1,
  color_last_column = TRUE,
  title = NULL,
  subtitle = NULL,
  ...
)
# S3 method for bootstrap_coefficients
as_gt(
  x,
  scale = c("link", "exponentiated", "relativity"),
  confidence = 0.95,
  interval = c("percentile", "normal"),
  locale = "nl-NL",
  estimate_decimals = 3,
  success_decimals = 1,
  title = NULL,
  subtitle = NULL,
  ...
)
# S3 method for rating_table
as_gt(
  x,
  significance = NULL,
  show_effect_spanner = NULL,
  model_labels = NULL,
  locale = "nl-NL",
  estimate_decimals = 3,
  exposure_decimals = 0,
  missing_text = "–",
  title = NULL,
  subtitle = NULL,
  ...
)

Value

A gt_tbl object for supported methods.

Arguments

x

A supported object to convert, such as a threshold_assessment, rating_table, bootstrap_coefficients or refinement_audit object.

...

Arguments passed to methods.

claims

Logical. If TRUE, include claim-count columns.

loss

Logical. If TRUE, include loss amount columns. The default is FALSE to keep the threshold comparison compact.

premium

Logical. If TRUE, include risk-premium and premium-reduction columns.

locale

Character. Locale used to format model effects and exposure, for example "nl-NL" or "en-US".

loss_decimals, premium_decimals, ratio_decimals

Non-negative whole numbers controlling displayed decimals for loss amounts, premium amounts and percentage ratios.

color_last_column

Logical. If TRUE, color the final displayed column from white to yellow so the highest values stand out in the presentation table.

title

Optional character. Table title. If NULL, no title is added.

subtitle

Optional character. Table subtitle. If NULL, no subtitle is added.

scale

Character string. "link" reports coefficients on their fitted GLM scale. "exponentiated" applies exp() to every original and bootstrap coefficient. "relativity" is an alias for "exponentiated"; this interpretation is most direct for a log-link GLM. For a logit-link model, exponentiated coefficients are odds ratios rather than response probabilities.

confidence

Numeric scalar between 0 and 1 giving the confidence level.

interval

Character string. "percentile" uses empirical bootstrap quantiles. "normal" uses the original estimate plus or minus a normal quantile times the bootstrap standard error.

estimate_decimals

Non-negative whole number. Number of decimals shown for fitted coefficients or relativities.

success_decimals

Non-negative whole number. Number of decimals for the success-rate percentage.

significance

Optional logical. If NULL, use the significance setting stored on x. If TRUE, append the stored significance stars to the model effects and add the significance-level note. If FALSE, show fitted effects without stars.

show_effect_spanner

Optional logical. If NULL, show the "Relativities" or "Coefficients" spanner when multiple models are present and omit it for a single model. Use TRUE or FALSE to override this behaviour.

model_labels

Optional character vector with display labels for the fitted models. By default, each model object name is used unchanged. An unnamed vector is matched to the model columns in their existing order. A named vector can map model object names to labels, for example c(freq = "Frequency", sev = "Severity").

exposure_decimals

Non-negative whole number. Number of decimals shown for the exposure column, when available.

missing_text

Single character string used to display missing values. The default is an en dash ("\\u2013") so structural missing values, such as exposure for the intercept, are visually distinct from observed zero values.

Author

Martin Haringa

Details

The first column of a rating_table identifies the model risk factor. as_gt() uses this column as groupname_col and sets row_group_as_column = TRUE. Levels belonging to the same risk factor are therefore kept together in a compact format suitable for a tariff note, model review or technical appendix.

Risk-factor and level order are taken directly from rating_table(). Use its risk_factor_order, level_order, numeric_level_order, reference_first and order_model arguments to determine the order before formatting the table.

With significance = TRUE, significance stars are appended to the fitted effects and the significance levels are shown below the table. This requires an object originally created with rating_table(significance = TRUE), because p-value information is deliberately not retained when significance is disabled during table construction. Significance stars are a statistical diagnostic and should be interpreted together with exposure, effect size, model stability and actuarial relevance.

In the underlying rating_table, estimates and significance indicators are stored in separate est_* and signif_* columns. as_gt() merges each pair only for display. The estimates therefore remain numeric in the source object, including when several models are presented in one table.

Examples

portfolio <- data.frame(
  policy_id = 1:10,
  sector = rep(c("Industry", "Retail"), each = 5),
  claim_count = c(
    0, 1, 1, 1, 1,
    0, 1, 1, 1, 1
  ),
  claim_amount = c(
    0, 25000, 120000, 50000, 175000,
    0, 40000, 90000, 150000, 300000
  ),
  policy_years = rep(1, 10)
)

thresholds <- assess_excess_threshold(
  data = portfolio,
  claim_amount = "claim_amount",
  thresholds = c(25000, 50000, 100000, 150000),
  exposure = "policy_years",
  group = "sector",
  claim_count = "claim_count"
)

if (requireNamespace("gt", quietly = TRUE)) {
  as_gt(thresholds)
}

portfolio <- MTPL
portfolio$zip <- as.factor(portfolio$zip)

frequency_model <- glm(
  nclaims ~ bm + zip + offset(log(exposure)),
  family = poisson(),
  data = portfolio
)

fitted_tariff <- rating_table(
  frequency_model,
  model_data = portfolio,
  exposure = "exposure",
  significance = TRUE
)

if (requireNamespace("gt", quietly = TRUE)) {
  as_gt(fitted_tariff)
  as_gt(fitted_tariff, model_labels = "Frequency model")
  as_gt(fitted_tariff, significance = FALSE, locale = "en-US")
}