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Factor analysis for discrete risk factors

Source: R/univariate.R
factor_analysis.Rd

Performs a factor analysis for discrete risk factors in an insurance portfolio. The following summary statistics are calculated:

  • frequency = number of claims / exposure

  • average severity = severity / number of claims

  • risk premium = severity / exposure

  • loss ratio = severity / premium

  • average premium = premium / exposure

univariate_summary() is kept as a compatibility alias for factor_analysis(). It uses the same standard-evaluation interface and is not deprecated.

univariate() is deprecated as of version 0.8.0. Please use factor_analysis() instead with standard evaluation (SE), i.e. column names as character strings.

Usage

factor_analysis(
  df,
  x,
  severity = NULL,
  nclaims = NULL,
  exposure = NULL,
  premium = NULL,
  by = NULL
)

univariate_summary(
  df,
  x,
  severity = NULL,
  nclaims = NULL,
  exposure = NULL,
  premium = NULL,
  by = NULL
)

univariate(
  df,
  x,
  severity = NULL,
  nclaims = NULL,
  exposure = NULL,
  premium = NULL,
  by = NULL
)

Arguments

df

A data.frame with the insurance portfolio.

x

Character vector: column(s) in df with the risk factor(s).

severity

Character, column in df with claim amounts (default = NULL).

nclaims

Character, column in df with number of claims (default = NULL).

exposure

Character, column in df with exposures (default = NULL).

premium

Character, column in df with premiums (default = NULL).

by

Character vector of column(s) in df to group by in addition to x.

Value

A data.table of class "univariate" with summary statistics.

Details

The function computes summary statistics for discrete risk factors.

  • Frequency: number of claims / exposure

  • Average severity: severity / number of claims

  • Risk premium: severity / exposure

  • Loss ratio: severity / premium

  • Average premium: premium / exposure

If one or more input arguments are not specified, the related statistics are omitted from the results.

Migration from univariate()

The function univariate() is deprecated as of version 0.8.0 and replaced by factor_analysis(). In addition to the name change, the interface has also changed:

  • univariate() used non-standard evaluation (NSE), so column names could be passed unquoted (e.g. x = area).

  • factor_analysis() uses standard evaluation (SE), so column names must be passed as character strings (e.g. x = "area").

This makes the function easier to use in programmatic workflows.

univariate() is still available for backward compatibility but will emit a deprecation warning and will be removed in a future release.

Author

Martin Haringa

Examples

## --- New usage (SE) ---
factor_analysis(MTPL2,
                x = "area",
                severity = "amount",
                nclaims = "nclaims",
                exposure = "exposure",
                premium = "premium")
#> # A tibble: 4 × 10
#>    area  amount nclaims exposure premium frequency average_severity risk_premium
#>   <int>   <int>   <int>    <dbl>   <int>     <dbl>            <dbl>        <dbl>
#> 1     2 4063270      98    819.    51896    0.120            41462.        4964.
#> 2     3 7945311     113    765.    49337    0.148            70312.       10386.
#> 3     1 6896187     146   1066.    65753    0.137            47234.        6471.
#> 4     0    6922       1     13.3     902    0.0751            6922          520.
#> # ℹ 2 more variables: loss_ratio <dbl>, average_premium <dbl>

## --- Deprecated usage (NSE) ---
univariate(MTPL2,
           x = area,
           severity = amount,
           nclaims = nclaims,
           exposure = exposure,
           premium = premium)
#> # A tibble: 4 × 10
#>    area  amount nclaims exposure premium frequency average_severity risk_premium
#>   <int>   <int>   <int>    <dbl>   <int>     <dbl>            <dbl>        <dbl>
#> 1     2 4063270      98    819.    51896    0.120            41462.        4964.
#> 2     3 7945311     113    765.    49337    0.148            70312.       10386.
#> 3     1 6896187     146   1066.    65753    0.137            47234.        6471.
#> 4     0    6922       1     13.3     902    0.0751            6922          520.
#> # ℹ 2 more variables: loss_ratio <dbl>, average_premium <dbl>