loss {brms} | R Documentation |

## Cumulative Insurance Loss Payments

### Description

This dataset, discussed in Gesmann & Morris (2020), contains
cumulative insurance loss payments over the course of ten years.

### Usage

loss

### Format

A data frame of 55 observations containing information
on the following 4 variables.

- AY
Origin year of the insurance (1991 to 2000)

- dev
Deviation from the origin year in months

- cum
Cumulative loss payments

- premium
Achieved premiums for the given origin year

### Source

Gesmann M. & Morris J. (2020). Hierarchical Compartmental Reserving
Models. *CAS Research Papers*.

### Examples

## Not run:
# non-linear model to predict cumulative loss payments
fit_loss <- brm(
bf(cum ~ ult * (1 - exp(-(dev/theta)^omega)),
ult ~ 1 + (1|AY), omega ~ 1, theta ~ 1,
nl = TRUE),
data = loss, family = gaussian(),
prior = c(
prior(normal(5000, 1000), nlpar = "ult"),
prior(normal(1, 2), nlpar = "omega"),
prior(normal(45, 10), nlpar = "theta")
),
control = list(adapt_delta = 0.9)
)
# basic summaries
summary(fit_loss)
conditional_effects(fit_loss)
# plot predictions per origin year
conditions <- data.frame(AY = unique(loss$AY))
rownames(conditions) <- unique(loss$AY)
me_loss <- conditional_effects(
fit_loss, conditions = conditions,
re_formula = NULL, method = "predict"
)
plot(me_loss, ncol = 5, points = TRUE)
## End(Not run)

[Package

*brms* version 2.15.0

Index]