bootstrap_rmse: Bootstrapped RMSE

In insurancerating: Analytic Insurance Rating Techniques

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

model	a model object
data	data used to fit model object
n	number of bootstrap replicates (defaults to 50)
frac	fraction used in training set if cross-validation is applied (defaults to 1)
show_progress	show progress bar (defaults to TRUE)
rmse_model	numeric RMSE to show as vertical dashed line in autoplot() (defaults to NULL)

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

rmse_bs	numerical vector with n root mean squared errors
rmse_mod	root mean squared error for fitted (i.e. original) model

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)

insurancerating documentation built on Dec. 28, 2022, 2:53 a.m.