restrict_coef: Restrict coefficients in the model

In MHaringa/actuarialpricing: Analytic Insurance Rating Techniques

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

model	object of class glm/restricted
restrictions	data.frame with two columns containing restricted data. The first column, with the name of the risk factor as column name, must contain the levels of the risk factor. The second column must contain the restricted coefficients.

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(., zip_df) %>%
  update_glm()

# Show rating factors
rating_factors(burn_rst)

## End(Not run)

MHaringa/actuarialpricing documentation built on Jan. 11, 2024, 1:13 a.m.