weibulltools (version 2.1.0)

dist_mileage: Parameter Estimation of an Annual Mileage Distribution

Description

This function models a mileage random variable on an annual basis with respect to a supposed continuous distribution. First, the distances are calculated for one year (365 days) using a linear relationship between the distance and operating time. Second, the parameter(s) of the assumed distribution are estimated with maximum likelihood. See 'Details' for more information.

Usage

dist_mileage(x, ...)
# S3 method for wt_mcs_mileage_data
dist_mileage(x, distribution = c("lognormal", "exponential"), ...)

Value

A list with class wt_mileage_estimation which contains:

• coefficients : A named vector of estimated parameter(s).

• miles_annual : A numeric vector of element-wise computed annual distances using the linear relationship described in 'Details'.

• distribution : Specified distribution.

Arguments

x

A tibble of class wt_mcs_mileage_data returned by mcs_mileage_data.

...

Further arguments passed to or from other methods. Currently not used.

distribution

Supposed distribution of the annual mileage.

Details

The distribution parameter(s) is (are) determined on the basis of complete cases, i.e. there is no NA (row-wise) in one of the related columns mileage and time. Distances and operating times less than or equal to zero are not considered as well.

Assumption of linear relationship: Imagine a component in a vehicle has endured a distance of 25000 kilometers (km) in 500 days (d), the annual distance of this unit is $$25000 km \cdot (\frac{365 d} {500 d}) = 18250 km$$

Examples

# MCS data preparation:
mcs_tbl <- mcs_mileage_data(
  field_data,
  mileage = mileage,
  time = dis,
  status = status,
  id = vin
)

# Example 1 - Assuming lognormal annual mileage distribution:
params_mileage_annual <- dist_mileage(
  x = mcs_tbl,
  distribution = "lognormal"
)

# Example 2 - Assuming exponential annual mileage distribution:
params_mileage_annual_2 <- dist_mileage(
  x = mcs_tbl,
  distribution = "exponential"
)