Convert an object to a gt table — as

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 class '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 class '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 class '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,
  ...
)

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.

Value

A gt_tbl object for supported methods.

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.

Author

Martin Haringa

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)
}


  
      Threshold
      Policy Years
      
        Claims
      
      
        Risk premium
      
    
Count
      Above threshold
      Before
      After
      Reduction
      Reduction (%)
    
Industry
25.000
5
4
3
74.000
20.000
54.000
73,0%
50.000
5
4
2
74.000
35.000
39.000
52,7%
100.000
5
4
2
74.000
55.000
19.000
25,7%
150.000
5
4
1
74.000
69.000
5.000
6,8%
Retail
25.000
5
4
4
116.000
20.000
96.000
82,8%
50.000
5
4
3
116.000
38.000
78.000
67,2%
100.000
5
4
2
116.000
66.000
50.000
43,1%
150.000
5
4
1
116.000
86.000
30.000
25,9%

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")
}


  Risk factor
      Level
      frequency_model
      Exposure
    
(Intercept)
(Intercept)
0.142
–
zip
0
1.000
207
1
1.025
11,081
2
0.924
7,783
3
0.976
7,588
bm
bm
0.998
–

	Threshold	Policy Years	Claims		Risk premium
	Threshold	Policy Years	Count	Above threshold	Before	After	Reduction	Reduction (%)
Industry	25.000	5	4	3	74.000	20.000	54.000	73,0%
	50.000	5	4	2	74.000	35.000	39.000	52,7%
	100.000	5	4	2	74.000	55.000	19.000	25,7%
	150.000	5	4	1	74.000	69.000	5.000	6,8%
Retail	25.000	5	4	4	116.000	20.000	96.000	82,8%
	50.000	5	4	3	116.000	38.000	78.000	67,2%
	100.000	5	4	2	116.000	66.000	50.000	43,1%
	150.000	5	4	1	116.000	86.000	30.000	25,9%

Risk factor	Level	frequency_model	Exposure
(Intercept)	(Intercept)	0.142	–
zip	0	1.000	207
	1	1.025	11,081
	2	0.924	7,783
	3	0.976	7,588
bm	bm	0.998	–