weibulltools (version 2.1.0)

dist_mileage.default: 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

# S3 method for default
dist_mileage(x, time, 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 numeric vector of distances covered. Use NA for missing elements.

time

A numeric vector of operating times. Use NA for missing elements.

distribution

Supposed distribution of the annual mileage.

...

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

Details

The distribution parameter(s) is (are) determined on the basis of complete cases, i.e. there is no NA in one of the related vector elements c(mileage[i], time[i]). 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 $$25000 km \cdot (\frac{365 d} {500 d}) = 18250 km$$

See Also

dist_mileage

Examples

# Example 1 - Assuming lognormal annual mileage distribution:
params_mileage_annual <- dist_mileage(
  x = field_data$mileage,
  time = field_data$dis,
  distribution = "lognormal"
)

# Example 2 - Assuming exponential annual mileage distribution:
params_mileage_annual_2 <- dist_mileage(
  x = field_data$mileage,
  time = field_data$dis,
  distribution = "exponential"
)