insurancerating (version 0.7.5)

univariate: 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
)

Value

A data.frame

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

Author

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))