univariate: Univariate analysis for discrete risk factors

In MHaringa/actuarialpricing: Analytic Insurance Rating Techniques

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

df	data.frame with insurance portfolio
x	column in df with risk factor, or use vec_ext() for use with an external vector (see examples)
severity	column in df with severity (default is NULL)
nclaims	column in df with number of claims (default is NULL)
exposure	column in df with exposure (default is NULL)
premium	column in df with premium (default is NULL)
by	list of column(s) in df to group by

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))

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