Characteristics of 30,000 policyholders in a Motor Third Party Liability (MTPL) portfolio.

Description

A dataset containing the age, number of claims, exposure, claim amount, power, bm, and region of 30,000 policyholders.

Usage

MTPL

Format

A data frame with 30,000 rows and 7 variables:

age_policyholder

age of policyholder, in years.

nclaims

number of claims.

exposure

exposure, for example, if a vehicle is insured as of July 1 for a certain year, then during that year, this would represent an exposure of 0.5 to the insurance company.

amount

claim amount in Euros.

power

engine power of vehicle (in kilowatts).

bm

level occupied in the 23-level (0-22) bonus-malus scale (the higher the level occupied, the worse the claim history).

zip

region indicator (0-3).

Author(s)

Martin Haringa

Source

The data is derived from the portfolio of a large Dutch motor insurance company.

Characteristics of 3,000 policyholders in a Motor Third Party Liability (MTPL) portfolio.

Description

A dataset containing the area, number of claims, exposure, claim amount, exposure, and premium of 3,000 policyholders

Usage

MTPL2

Format

A data frame with 3,000 rows and 6 variables:

customer_id

customer id

area

region where customer lives (0-3)

nclaims

number of claims

amount

claim amount (severity)

exposure

exposure

premium

earned premium

Author(s)

Martin Haringa

Source

The data is derived from the portfolio of a large Dutch motor insurance company.

Add predictions to a data frame

Description

Add model predictions and confidence bounds to a data frame.

Usage

add_prediction(data, ..., var = NULL, conf_int = FALSE, alpha = 0.1)

Arguments

Value

data.frame

Examples

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

# Include confidence bounds
mtpl_pred_ci <- add_prediction(MTPL, mod1, conf_int = TRUE)

Automatically create a ggplot for objects obtained from bootstrap_rmse()

Description

Takes an object produced by bootstrap_rmse(), and plots the simulated RMSE

Usage

## S3 method for class 'bootstrap_rmse'
autoplot(object, fill = NULL, color = NULL, ...)

Arguments

Value

a ggplot object

Author(s)

Martin Haringa

Automatically create a ggplot for objects obtained from check_residuals()

Description

Takes an object produced by check_residuals(), and produces a uniform quantile-quantile plot.#'

Usage

## S3 method for class 'check_residuals'
autoplot(object, show_message = TRUE, ...)

Arguments

Value

a ggplot object

Author(s)

Martin Haringa

Automatically create a ggplot for objects obtained from construct_tariff_classes()

Description

Takes an object produced by construct_tariff_classes(), and plots the fitted GAM. In addition the constructed tariff classes are shown.

Usage

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

Arguments

Value

a ggplot object

Author(s)

Martin Haringa

Examples

## Not run: 
library(ggplot2)
library(dplyr)
x <- fit_gam(MTPL,
nclaims = nclaims, x = age_policyholder, exposure = exposure) |>
   construct_tariff_classes()
autoplot(x, show_observations = TRUE)

## End(Not run)

Automatically create a ggplot for objects obtained from fit_gam()

Description

Takes an object produced by fit_gam(), and plots the fitted GAM.

Usage

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

Arguments

Value

a ggplot object

Author(s)

Martin Haringa

Examples

## Not run: 
library(ggplot2)
library(dplyr)
fit_gam(MTPL, nclaims = nclaims, x = age_policyholder,
        exposure = exposure) |>
   autoplot(show_observations = TRUE)

## End(Not run)

Automatically create a ggplot for objects obtained from restrict_coef()

Description

Takes an object produced by restrict_coef(), and produces a line plot with a comparison between the restricted coefficients and estimated coefficients obtained from the model.

Usage

## S3 method for class 'restricted'
autoplot(object, ...)

Arguments

Value

Object of class ggplot2

Author(s)

Martin Haringa

Examples

freq <- glm(nclaims ~ bm + zip, weights = power, family = poisson(),
 data = MTPL)
zip_df <- data.frame(zip = c(0,1,2,3), zip_rst = c(0.8, 0.9, 1, 1.2))
freq |>
  restrict_coef(restrictions = zip_df) |>
  autoplot()

Automatically create a ggplot for objects obtained from rating_factors()

Description

Takes an object produced by rating_factors(), and plots the available input.

Usage

## S3 method for class 'riskfactor'
autoplot(
  object,
  risk_factors = NULL,
  ncol = 1,
  labels = TRUE,
  dec.mark = ",",
  ylab = "rate",
  fill = NULL,
  color = NULL,
  linetype = FALSE,
  ...
)

Arguments

Value

a ggplot2 object

Author(s)

Martin Haringa

Examples

library(dplyr)
df <- MTPL2 |>
  mutate(across(c(area), as.factor)) |>
  mutate(across(c(area), ~biggest_reference(., exposure)))

mod1 <- glm(nclaims ~ area + premium, offset = log(exposure),
 family = poisson(), data = df)
mod2 <- glm(nclaims ~ area, offset = log(exposure), family = poisson(),
 data = df)

x <- rating_factors(mod1, mod2, model_data = df, exposure = exposure)
autoplot(x)

Automatically create a ggplot for objects obtained from smooth_coef()

Description

Takes an object produced by smooth_coef(), and produces a plot with a comparison between the smoothed coefficients and estimated coefficients obtained from the model.

Usage

## S3 method for class 'smooth'
autoplot(object, ...)

Arguments

Value

Object of class ggplot2

Author(s)

Martin Haringa

Automatically create a ggplot for objects obtained from fit_truncated_dist()

Description

Takes an object produced by fit_truncated_dist(), and plots the available input.

Usage

## S3 method for class 'truncated_dist'
autoplot(
  object,
  geom_ecdf = c("point", "step"),
  xlab = NULL,
  ylab = NULL,
  ylim = c(0, 1),
  xlim = NULL,
  print_title = TRUE,
  print_dig = 2,
  print_trunc = 2,
  ...
)

Arguments

Value

a ggplot2 object

Author(s)

Martin Haringa

Automatically create a ggplot for objects obtained from univariate()

Description

Takes an object produced by univariate(), and plots the available input.

Usage

## S3 method for class 'univariate'
autoplot(
  object,
  show_plots = 1:9,
  ncol = 1,
  background = TRUE,
  labels = TRUE,
  sort = FALSE,
  sort_manual = NULL,
  dec.mark = ",",
  color = "dodgerblue",
  color_bg = "lightskyblue",
  label_width = 10,
  coord_flip = FALSE,
  show_total = FALSE,
  total_color = NULL,
  total_name = NULL,
  rotate_angle = NULL,
  custom_theme = NULL,
  remove_underscores = FALSE,
  ...
)

Arguments

Value

a ggplot2 object

Author(s)

Marc Haine, Martin Haringa

Examples

library(ggplot2)
x <- univariate(MTPL2, x = area, severity = amount, nclaims = nclaims,
exposure = exposure)
autoplot(x)
autoplot(x, show_plots = c(6,1), background = FALSE, sort = TRUE)

# Group by `zip`
xzip <- univariate(MTPL, x = bm, severity = amount, nclaims = nclaims,
exposure = exposure, by = zip)
autoplot(xzip, show_plots = 1:2)

Set reference group to the group with largest exposure

Description

This function specifies the first level of a factor to the level with the largest exposure. Levels of factors are sorted using an alphabetic ordering. If the factor is used in a regression context, then the first level will be the reference. For insurance applications it is common to specify the reference level to the level with the largest exposure.

Usage

biggest_reference(x, weight)

Arguments

Value

a factor of the same length as x

Author(s)

Martin Haringa

References

Kaas, Rob & Goovaerts, Marc & Dhaene, Jan & Denuit, Michel. (2008). Modern Actuarial Risk Theory: Using R. doi:10.1007/978-3-540-70998-5.

Examples

## Not run: 
library(dplyr)
df <- chickwts |>
mutate(across(where(is.character), as.factor)) |>
mutate(across(where(is.factor), ~biggest_reference(., weight)))

## End(Not run)

Bootstrapped RMSE

Description

Generate n bootstrap replicates to compute n root mean squared errors.

Usage

bootstrap_rmse(
  model,
  data,
  n = 50,
  frac = 1,
  show_progress = TRUE,
  rmse_model = NULL
)

Arguments

Details

To test the predictive ability of the fitted model it might be helpful to determine the variation in the computed RMSE. The variation is calculated by computing the root mean squared errors from n generated bootstrap replicates. More precisely, for each iteration a sample with replacement is taken from the data set and the model is refitted using this sample. Then, the root mean squared error is calculated.

Value

A list with components

Author(s)

Martin Haringa

Examples

## Not run: 
mod1 <- glm(nclaims ~ age_policyholder, data = MTPL,
    offset = log(exposure), family = poisson())

# Use all records in MTPL
x <- bootstrap_rmse(mod1, MTPL, n = 80, show_progress = FALSE)
print(x)
autoplot(x)

# Use 80% of records to test whether predictive ability depends on which 80%
# is used. This might for example be useful in case portfolio contains large
# claim sizes
x_frac <- bootstrap_rmse(mod1, MTPL, n = 50, frac = .8,
 show_progress = FALSE)
autoplot(x_frac) # Variation is quite small for Poisson GLM

## End(Not run)

Check overdispersion of Poisson GLM

Description

Check Poisson GLM for overdispersion.

Usage

check_overdispersion(object)

Arguments

Details

A dispersion ratio larger than one indicates overdispersion, this occurs when the observed variance is higher than the variance of the theoretical model. If the dispersion ratio is close to one, a Poisson model fits well to the data. A p-value < .05 indicates overdispersion. Overdispersion > 2 probably means there is a larger problem with the data: check (again) for outliers, obvious lack of fit. Adopted from performance::check_overdispersion().

Value

A list with dispersion ratio, chi-squared statistic, and p-value.

Author(s)

Martin Haringa

References

• Bolker B et al. (2017): GLMM FAQ.

Examples

x <- glm(nclaims ~ area, offset = log(exposure), family = poisson(),
  data = MTPL2)
check_overdispersion(x)

Check model residuals

Description

Detect overall deviations from the expected distribution.

Usage

check_residuals(object, n_simulations = 30)

Arguments

Details

Misspecifications in GLMs cannot reliably be diagnosed with standard residual plots, and GLMs are thus often not as thoroughly checked as LMs. One reason why GLMs residuals are harder to interpret is that the expected distribution of the data changes with the fitted values. As a result, standard residual plots, when interpreted in the same way as for linear models, seem to show all kind of problems, such as non-normality, heteroscedasticity, even if the model is correctly specified. check_residuals() aims at solving these problems by creating readily interpretable residuals for GLMs that are standardized to values between 0 and 1, and that can be interpreted as intuitively as residuals for the linear model. This is achieved by a simulation-based approach, similar to the Bayesian p-value or the parametric bootstrap, that transforms the residuals to a standardized scale. This explanation is adopted from DHARMa::simulateResiduals().

It might happen that in the fitted model for a data point all simulations have the same value (e.g. zero), this returns the error message Error in approxfun: need at least two non-NA values to interpolate*. If that is the case, it could help to increase the number of simulations.

Value

Invisibly returns the p-value of the test statistics. A p-value < 0.05 indicates a significant deviation from expected distribution.

Author(s)

Martin Haringa

References

Dunn, K. P., and Smyth, G. K. (1996). Randomized quantile residuals. Journal of Computational and Graphical Statistics 5, 1-10.

Gelman, A. & Hill, J. Data analysis using regression and multilevel/hierarchical models Cambridge University Press, 2006

Hartig, F. (2020). DHARMa: Residual Diagnostics for Hierarchical (Multi-Level / Mixed) Regression Models. R package version 0.3.0. https://CRAN.R-project.org/package=DHARMa

Examples

## Not run: 
m1 <- glm(nclaims ~ area, offset = log(exposure), family = poisson(),
data = MTPL2)
check_residuals(m1, n_simulations = 50) |> autoplot()

## End(Not run)

Construct model points from Generalized Linear Model

Description

construct_model_points() is used to construct model points from generalized linear models, and must be preceded by model_data(). construct_model_points() can also be used in combination with a data.frame.

Usage

construct_model_points(
  x,
  exposure = NULL,
  exposure_by = NULL,
  agg_cols = NULL,
  drop_na = FALSE
)

Arguments

Value

data.frame

Author(s)

Martin Haringa

Examples

## Not run: 
# With data.frame
library(dplyr)
mtcars |>
 select(cyl, vs) |>
 construct_model_points()

mtcars |>
  select(cyl, vs, disp) |>
  construct_model_points(exposure = disp)

mtcars |>
 select(cyl, vs, disp, gear) |>
 construct_model_points(exposure = disp, exposure_by = gear)

mtcars |>
 select(cyl, vs, disp, gear, mpg) |>
 construct_model_points(exposure = disp, exposure_by = gear,
   agg_cols = list(mpg))

# With glm
library(datasets)
data1 <- warpbreaks |>
 mutate(jaar = c(rep(2000, 10), rep(2010, 44))) |>
 mutate(exposure = 1) |>
 mutate(nclaims = 2)

pmodel <- glm(breaks ~ wool + tension, data1, offset = log(exposure),
 family = poisson(link = "log"))

model_data(pmodel) |>
 construct_model_points()

model_data(pmodel) |>
 construct_model_points(agg_cols = list(nclaims))

model_data(pmodel) |>
 construct_model_points(exposure = exposure, exposure_by = jaar) |>
 add_prediction(pmodel)
 
## End(Not run)

Construct insurance tariff classes

Description

Constructs insurance tariff classes to fitgam objects produced by fit_gam. The goal is to bin the continuous risk factors such that categorical risk factors result which capture the effect of the covariate on the response in an accurate way, while being easy to use in a generalized linear model (GLM).

Usage

construct_tariff_classes(
  object,
  alpha = 0,
  niterations = 10000,
  ntrees = 200,
  seed = 1
)

Arguments

Details

Evolutionary trees are used as a technique to bin the fitgam object produced by fit_gam into risk homogeneous categories. This method is based on the work by Henckaerts et al. (2018). See Grubinger et al. (2014) for more details on the various parameters that control aspects of the evtree fit.

Value

A list of class constructtariffclasses with components

Author(s)

Martin Haringa

References

Antonio, K. and Valdez, E. A. (2012). Statistical concepts of a priori and a posteriori risk classification in insurance. Advances in Statistical Analysis, 96(2):187–224. doi:10.1007/s10182-011-0152-7.

Grubinger, T., Zeileis, A., and Pfeiffer, K.-P. (2014). evtree: Evolutionary learning of globally optimal classification and regression trees in R. Journal of Statistical Software, 61(1):1–29. doi:10.18637/jss.v061.i01.

Henckaerts, R., Antonio, K., Clijsters, M. and Verbelen, R. (2018). A data driven binning strategy for the construction of insurance tariff classes. Scandinavian Actuarial Journal, 2018:8, 681-705. doi:10.1080/03461238.2018.1429300.

Wood, S.N. (2011). Fast stable restricted maximum likelihood and marginal likelihood estimation of semiparametric generalized linear models. Journal of the Royal Statistical Society (B) 73(1):3-36. doi:10.1111/j.1467-9868.2010.00749.x.

Examples

## Not run: 
library(dplyr)
fit_gam(MTPL, nclaims = nclaims,
x = age_policyholder, exposure = exposure) |>
   construct_tariff_classes()

## End(Not run)

Fisher's natural breaks classification

Description

The function provides an interface to finding class intervals for continuous numerical variables, for example for choosing colours for plotting maps.

Usage

fisher(vec, n = 7, diglab = 2)

Arguments

Details

The "fisher" style uses the algorithm proposed by W. D. Fisher (1958) and discussed by Slocum et al. (2005) as the Fisher-Jenks algorithm. This function is adopted from the classInt package.

Value

Vector with clustering

Author(s)

Martin Haringa

References

Bivand, R. (2018). classInt: Choose Univariate Class Intervals. R package version 0.2-3. https://CRAN.R-project.org/package=classInt

Fisher, W. D. 1958 "On grouping for maximum homogeneity", Journal of the American Statistical Association, 53, pp. 789–798. doi: 10.1080/01621459.1958.10501479.

Generalized additive model

Description

Fits a generalized additive model (GAM) to continuous risk factors in one of the following three types of models: the number of reported claims (claim frequency), the severity of reported claims (claim severity) or the burning cost (i.e. risk premium or pure premium).

Usage

fit_gam(
  data,
  nclaims,
  x,
  exposure,
  amount = NULL,
  pure_premium = NULL,
  model = "frequency",
  round_x = NULL
)

Arguments

Details

The 'frequency' specification uses a Poisson GAM for fitting the number of claims. The logarithm of the exposure is included as an offset, such that the expected number of claims is proportional to the exposure.

The 'severity' specification uses a lognormal GAM for fitting the average cost of a claim. The average cost of a claim is defined as the ratio of the claim amount and the number of claims. The number of claims is included as a weight.

The 'burning' specification uses a lognormal GAM for fitting the pure premium of a claim. The pure premium is obtained by multiplying the estimated frequency and the estimated severity of claims. The word burning cost is used here as equivalent of risk premium and pure premium. Note that the functionality for fitting a GAM for pure premium is still experimental (in the early stages of development).

Value

A list with components

Author(s)

Martin Haringa

References

Antonio, K. and Valdez, E. A. (2012). Statistical concepts of a priori and a posteriori risk classification in insurance. Advances in Statistical Analysis, 96(2):187–224. doi:10.1007/s10182-011-0152-7.

Grubinger, T., Zeileis, A., and Pfeiffer, K.-P. (2014). evtree: Evolutionary learning of globally optimal classification and regression trees in R. Journal of Statistical Software, 61(1):1–29. doi:10.18637/jss.v061.i01.

Henckaerts, R., Antonio, K., Clijsters, M. and Verbelen, R. (2018). A data driven binning strategy for the construction of insurance tariff classes. Scandinavian Actuarial Journal, 2018:8, 681-705. doi:10.1080/03461238.2018.1429300.

Wood, S.N. (2011). Fast stable restricted maximum likelihood and marginal likelihood estimation of semiparametric generalized linear models. Journal of the Royal Statistical Society (B) 73(1):3-36. doi:10.1111/j.1467-9868.2010.00749.x.

Examples

fit_gam(MTPL, nclaims = nclaims, x = age_policyholder,
exposure = exposure)

Fit a distribution to truncated severity (loss) data

Description

Estimate the original distribution from truncated data. Truncated data arise frequently in insurance studies. It is common that only claims above a certain threshold are known.

Usage

fit_truncated_dist(
  y,
  dist = c("gamma", "lognormal"),
  left = NULL,
  right = NULL,
  start = NULL,
  print_initial = TRUE
)

Arguments

Value

fitdist returns an object of class "fitdist"

Author(s)

Martin Haringa

Examples

## Not run: 
# Original observations for severity
set.seed(1)
e <- rgamma(1000, scale = 148099.5, shape = 0.4887023)

# Truncated data (only claims above 30.000 euros)
threshold <- 30000
f <- e[e > threshold]

library(dplyr)
library(ggplot2)
data.frame(value = c(e, f),
variable = rep(c("Original data", "Only claims above 30.000 euros"),
               c(length(e), length(f)))) %>%
               filter(value < 5e5) %>%
               mutate(value = value / 1000) %>%
               ggplot(aes(x = value)) +
               geom_histogram(colour = "white") +
               facet_wrap(~variable, ncol = 1) +
               labs(y = "Number of observations",
                    x = "Severity (x 1000 EUR)")

# scale = 156259.7 and shape = 0.4588. Close to parameters of original
# distribution!
x <- fit_truncated_dist(f, left = threshold, dist = "gamma")

# Print cdf
autoplot(x)

# CDF with modifications
autoplot(x, print_dig = 5, xlab = "loss", ylab = "cdf", ylim = c(.9, 1))

est_scale <- x$estimate[1]
est_shape <- x$estimate[2]

# Generate data from truncated distribution (between 30k en 20 mln)
rg <- rgammat(10, scale = est_scale, shape = est_shape, lower = 3e4,
 upper = 20e6)

# Calculate quantiles
quantile(rg, probs = c(.5, .9, .99, .995))

## End(Not run)

Create a histogram with outlier bins

Description

Visualize the distribution of a single continuous variable by dividing the x axis into bins and counting the number of observations in each bin. Data points that are considered outliers can be binned together. This might be helpful to display numerical data over a very wide range of values in a compact way.

Usage

histbin(
  data,
  x,
  left = NULL,
  right = NULL,
  line = FALSE,
  bins = 30,
  fill = NULL,
  color = NULL,
  fill_outliers = "#a7d1a7"
)

Arguments

Details

Wrapper function around ggplot2::geom_histogram(). The method is based on suggestions from https://edwinth.github.io/blog/outlier-bin/.

Value

a ggplot2 object

Author(s)

Martin Haringa

Examples

histbin(MTPL2, premium)
histbin(MTPL2, premium, left = 30, right = 120, bins = 30)

Get model data

Description

model_data() is used to get data from glm, and must be preceded by update_glm() or glm().

Usage

model_data(x)

Arguments

Value

data.frame

Author(s)

Martin Haringa

Performance of fitted GLMs

Description

Compute indices of model performance for (one or more) GLMs.

Usage

model_performance(...)

Arguments

Details

The following indices are computed:

AIC

Akaike's Information Criterion

BIC

Bayesian Information Criterion

RMSE

Root mean squared error

Adopted from performance::model_performance().

Value

data frame

Author(s)

Martin Haringa

Examples

m1 <- glm(nclaims ~ area, offset = log(exposure), family = poisson(),
          data = MTPL2)
m2 <- glm(nclaims ~ area, offset = log(exposure), family = poisson(),
          data = MTPL2)
model_performance(m1, m2)

Split period to months

Description

The function splits rows with a time period longer than one month to multiple rows with a time period of exactly one month each. Values in numeric columns (e.g. exposure or premium) are divided over the months proportionately.

Usage

period_to_months(df, begin, end, ...)

Arguments

Details

In insurance portfolios it is common that rows relate to periods longer than one month. This is for example problematic in case exposures per month are desired.

Since insurance premiums are constant over the months, and do not depend on the number of days per month, the function assumes that each month has the same number of days (i.e. 30).

Value

data.frame with same columns as in df, and one extra column called id

Author(s)

Martin Haringa

Examples

library(lubridate)
portfolio <- data.frame(
begin1 = ymd(c("2014-01-01", "2014-01-01")),
end = ymd(c("2014-03-14", "2014-05-10")),
termination = ymd(c("2014-03-14", "2014-05-10")),
exposure = c(0.2025, 0.3583),
premium =  c(125, 150))
period_to_months(portfolio, begin1, end, premium, exposure)

Include reference group in regression output

Description

Extract coefficients in terms of the original levels of the coefficients rather than the coded variables.

Usage

rating_factors(
  ...,
  model_data = NULL,
  exposure = NULL,
  exponentiate = TRUE,
  signif_stars = FALSE,
  round_exposure = 0
)

Arguments

Details

A fitted linear model has coefficients for the contrasts of the factor terms, usually one less in number than the number of levels. This function re-expresses the coefficients in the original coding. This function is adopted from dummy.coef(). Our adoption prints a data.frame as output. Use rating_factors_() for standard evaluation.

Value

data.frame

Author(s)

Martin Haringa

Examples

df <- MTPL2 |>
dplyr::mutate(dplyr::across(c(area), as.factor)) |>
dplyr::mutate(dplyr::across(c(area), ~biggest_reference(., exposure)))

mod1 <- glm(nclaims ~ area + premium, offset = log(exposure),
family = poisson(), data = df)
mod2 <- glm(nclaims ~ area, offset = log(exposure), family = poisson(),
data = df)

rating_factors(mod1, mod2, model_data = df, exposure = exposure)

Reduce portfolio by merging redundant date ranges

Description

Transform all the date ranges together as a set to produce a new set of date ranges. Ranges separated by a gap of at least min.gapwidth days are not merged.

Usage

reduce(df, begin, end, ..., agg_cols = NULL, agg = "sum", min.gapwidth = 5)

Arguments

Details

This function is adopted from IRanges::reduce().

Value

An object of class "reduce". The function summary is used to obtain and print a summary of the results. An object of class "reduce" is a list usually containing at least the following elements:

Author(s)

Martin Haringa

Examples

portfolio <- structure(list(policy_nr = c("12345", "12345", "12345", "12345",
"12345", "12345", "12345", "12345", "12345", "12345", "12345"),
productgroup = c("fire", "fire", "fire", "fire", "fire", "fire",
"fire", "fire", "fire", "fire", "fire"), product = c("contents",
"contents", "contents", "contents", "contents", "contents", "contents",
"contents", "contents", "contents", "contents"),
begin_dat = structure(c(16709,16740, 16801, 17410, 17440, 17805, 17897,
17956, 17987, 18017, 18262), class = "Date"),
end_dat = structure(c(16739, 16800, 16831, 17439, 17531, 17896, 17955,
17986, 18016, 18261, 18292), class = "Date"),
premium = c(89L, 58L, 83L, 73L, 69L, 94L, 91L, 97L, 57L, 65L, 55L)),
row.names = c(NA, -11L), class = "data.frame")

# Merge periods
pt1 <- reduce(portfolio, begin = begin_dat, end = end_dat, policy_nr,
    productgroup, product, min.gapwidth = 5)

# Aggregate per period
summary(pt1, period = "days", policy_nr, productgroup, product)

# Merge periods and sum premium per period
pt2 <- reduce(portfolio, begin = begin_dat, end = end_dat, policy_nr,
    productgroup, product, agg_cols = list(premium), min.gapwidth = 5)

# Create summary with aggregation per week
summary(pt2, period = "weeks", policy_nr, productgroup, product)

Objects exported from other packages

Description

These objects are imported from other packages. Follow the links below to see their documentation.

ggplot2

autoplot

Refitting Generalized Linear Models

Description

refit_glm() is used to refit generalized linear models, and must be preceded by restrict_coef().

Usage

refit_glm(x)

Arguments

Value

Object of class GLM

Author(s)

Martin Haringa

Restrict coefficients in the model

Description

Add restrictions, like a bonus-malus structure, on the risk factors used in the model. restrict_coef() must always be followed by update_glm().

Usage

restrict_coef(model, restrictions)

Arguments

Details

Although restrictions could be applied either to the frequency or the severity model, it is more appropriate to impose the restrictions on the premium model. This can be achieved by calculating the pure premium for each record (i.e. expected number of claims times the expected claim amount), then fitting an "unrestricted" Gamma GLM to the pure premium,and then imposing the restrictions in a final "restricted" Gamma GLM.

Value

Object of class restricted.

Author(s)

Martin Haringa

See Also

update_glm() for refitting the restricted model, and autoplot.restricted().

Other update_glm: smooth_coef()

Examples

## Not run: 
# Add restrictions to risk factors for region (zip) -------------------------

# Fit frequency and severity model
library(dplyr)
freq <- glm(nclaims ~ bm + zip, offset = log(exposure), family = poisson(),
             data = MTPL)
sev <- glm(amount ~ bm + zip, weights = nclaims,
            family = Gamma(link = "log"),
            data = MTPL |> filter(amount > 0))

# Add predictions for freq and sev to data, and calculate premium
premium_df <- MTPL |>
   add_prediction(freq, sev) |>
   mutate(premium = pred_nclaims_freq * pred_amount_sev)

# Restrictions on risk factors for region (zip)
zip_df <- data.frame(zip = c(0,1,2,3), zip_rst = c(0.8, 0.9, 1, 1.2))

# Fit unrestricted model
burn <- glm(premium ~ bm + zip, weights = exposure,
            family = Gamma(link = "log"), data = premium_df)

# Fit restricted model
burn_rst <- burn |>
  restrict_coef(restrictions = zip_df) |>
  update_glm()

# Show rating factors
rating_factors(burn_rst)

## End(Not run)

Generate data from truncated gamma distribution

Description

Random generation for the truncated Gamma distribution with parameters shape and scale.

Usage

rgammat(n, scale = scale, shape = shape, lower, upper)

Arguments

Value

The length of the result is determined by n.

Author(s)

Martin Haringa

Generate data from truncated lognormal distribution

Description

Random generation for the truncated log normal distribution whose logarithm has mean equal to meanlog and standard deviation equal to sdlog.

Usage

rlnormt(n, meanlog, sdlog, lower, upper)

Arguments

Value

The length of the result is determined by n.

Author(s)

Martin Haringa

Root Mean Squared Error

Description

Compute root mean squared error.

Usage

rmse(object, data)

Arguments

Details

The RMSE is the square root of the average of squared differences between prediction and actual observation and indicates the absolute fit of the model to the data. It can be interpreted as the standard deviation of the unexplained variance, and is in the same units as the response variable. Lower values indicate better model fit.

Value

numeric value

Author(s)

Martin Haringa

Examples

x <- glm(nclaims ~ area, offset = log(exposure), family = poisson(),
 data = MTPL2)
rmse(x, MTPL2)

Find active rows per date

Description

Fast overlap joins. Usually, df is a very large data.table (e.g. insurance portfolio) with small interval ranges, and dates is much smaller with (e.g.) claim dates.

Usage

rows_per_date(
  df,
  dates,
  df_begin,
  df_end,
  dates_date,
  ...,
  nomatch = NULL,
  mult = "all"
)

Arguments

Value

returned class is equal to class of df

Author(s)

Martin Haringa

Examples

library(lubridate)
portfolio <- data.frame(
begin1 = ymd(c("2014-01-01", "2014-01-01")),
end = ymd(c("2014-03-14", "2014-05-10")),
termination = ymd(c("2014-03-14", "2014-05-10")),
exposure = c(0.2025, 0.3583),
premium =  c(125, 150),
car_type = c("BMW", "TESLA"))

## Find active rows on different dates
dates0 <- data.frame(active_date = seq(ymd("2014-01-01"), ymd("2014-05-01"),
by = "months"))
rows_per_date(portfolio, dates0, df_begin = begin1, df_end = end,
dates_date = active_date)

## With extra identifiers (merge claim date with time interval in portfolio)
claim_dates <- data.frame(claim_date = ymd("2014-01-01"),
car_type = c("BMW", "VOLVO"))

### Only rows are returned that can be matched
rows_per_date(portfolio, claim_dates, df_begin = begin1,
   df_end = end, dates_date = claim_date, car_type)

### When row cannot be matched, NA is returned for that row
rows_per_date(portfolio, claim_dates, df_begin = begin1,
   df_end = end, dates_date = claim_date, car_type, nomatch = NA)

Smooth coefficients in the model

Description

Apply smoothing on the risk factors used in the model. smooth_coef() must always be followed by update_glm().

Usage

smooth_coef(
  model,
  x_cut,
  x_org,
  degree = NULL,
  breaks = NULL,
  smoothing = "spline",
  k = NULL,
  weights = NULL
)

Arguments

Details

Although smoothing could be applied either to the frequency or the severity model, it is more appropriate to impose the smoothing on the premium model. This can be achieved by calculating the pure premium for each record (i.e. expected number of claims times the expected claim amount), then fitting an "unrestricted" Gamma GLM to the pure premium, and then imposing the restrictions in a final "restricted" Gamma GLM.

Value

Object of class smooth

Author(s)

Martin Haringa

See Also

update_glm() for refitting the smoothed model, and autoplot.smooth().

Other update_glm: restrict_coef()

Examples

## Not run: 
library(insurancerating)
library(dplyr)

# Fit GAM for claim frequency
age_policyholder_frequency <- fit_gam(data = MTPL,
                                      nclaims = nclaims,
                                      x = age_policyholder,
                                      exposure = exposure)

# Determine clusters
clusters_freq <- construct_tariff_classes(age_policyholder_frequency)

# Add clusters to MTPL portfolio
dat <- MTPL |>
  mutate(age_policyholder_freq_cat = clusters_freq$tariff_classes) |>
  mutate(across(where(is.character), as.factor)) |>
  mutate(across(where(is.factor), ~biggest_reference(., exposure)))

# Fit frequency and severity model
freq <- glm(nclaims ~ bm + age_policyholder_freq_cat, offset = log(exposure),
 family = poisson(), data = dat)
sev <- glm(amount ~ bm + zip, weights = nclaims,
 family = Gamma(link = "log"), data = dat |> filter(amount > 0))

# Add predictions for freq and sev to data, and calculate premium
premium_df <- dat |>
  add_prediction(freq, sev) |>
  mutate(premium = pred_nclaims_freq * pred_amount_sev)

# Fit unrestricted model
burn_unrestricted <- glm(premium ~ zip + bm + age_policyholder_freq_cat,
                         weights = exposure,
                         family = Gamma(link = "log"),
                         data = premium_df)

# Impose smoothing and create figure
burn_unrestricted |>
  smooth_coef(x_cut = "age_policyholder_freq_cat",
              x_org = "age_policyholder",
              breaks = seq(18, 95, 5)) |>
  autoplot()

# Impose smoothing and refit model
burn_restricted <- burn_unrestricted |>
  smooth_coef(x_cut = "age_policyholder_freq_cat",
              x_org = "age_policyholder",
              breaks = seq(18, 95, 5)) |>
  update_glm()

# Show new rating factors
rating_factors(burn_restricted)

## End(Not run)

Automatically create a summary for objects obtained from reduce()

Description

Takes an object produced by reduce(), and counts new and lost customers.

Usage

## S3 method for class 'reduce'
summary(object, ..., period = "days", name = "count")

Arguments

Value

data.frame

Univariate analysis for discrete risk factors

Description

Univariate analysis for discrete risk factors in an insurance portfolio. The following summary statistics are calculated:

• frequency (i.e. number of claims / exposure)

• average severity (i.e. severity / number of claims)

• risk premium (i.e. severity / exposure)

• loss ratio (i.e. severity / premium)

• average premium (i.e. premium / exposure)

If input arguments are not specified, the summary statistics related to these arguments are ignored.

Usage

univariate(
  df,
  x,
  severity = NULL,
  nclaims = NULL,
  exposure = NULL,
  premium = NULL,
  by = NULL
)

Arguments

Value

A data.frame

Author(s)

Martin Haringa

Examples

# Summarize by `area`
univariate(MTPL2, x = area, severity = amount, nclaims = nclaims,
           exposure = exposure, premium = premium)

# Summarize by `area`, with column name in external vector
xt <- "area"
univariate(MTPL2, x = vec_ext(xt), severity = amount, nclaims = nclaims,
           exposure = exposure, premium = premium)

# Summarize by `zip` and `bm`
univariate(MTPL, x = zip, severity = amount, nclaims = nclaims,
           exposure = exposure, by = bm)

# Summarize by `zip`, `bm` and `power`
univariate(MTPL, x = zip, severity = amount, nclaims = nclaims,
           exposure = exposure, by = list(bm, power))

Create new offset-term and new formula

Description

Create new offset-term and new formula

Usage

update_formula_add(offset_term, fm_no_offset, add_term)

Arguments

Refitting Generalized Linear Models

Description

update_glm() is used to refit generalized linear models, and must be preceded by restrict_coef().

Usage

update_glm(x, intercept_only = FALSE)

Arguments

Value

Object of class GLM

Author(s)

Martin Haringa