Simulation of Unknown Covered Distances using a Monte Carlo Approach

Source: R/mcs_mileage.R

This function simulates distances for units where these are unknown.

First, random numbers of the annual mileage distribution, estimated by dist_mileage, are drawn. Second, the drawn annual distances are converted with respect to the actual operating times (in days) using a linear relationship. See 'Details'.

mcs_mileage(x, ...)

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

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.

Value

A list with class wt_mcs_mileage containing the following elements:

  • data : A tibble returned by mcs_mileage_data where two modifications has been made:

    • If the column status exists, the tibble has additional classes wt_mcs_data and wt_reliability_data. Otherwise, the tibble only has the additional class wt_mcs_data (which is not supported by estimate_cdf).

    • The column mileage is renamed to x (to be in accordance with reliability_data) and contains simulated distances for incomplete observations and input distances for the complete observations.

  • sim_data : A tibble with column sim_mileage that holds the simulated distances for incomplete cases and 0 for complete cases.

  • model_estimation : A list returned by dist_mileage.

Details

Assumption of linear relationship: Imagine the distance of the vehicle is unknown. A distance of 3500.25 kilometers (km) was drawn from the annual distribution and the known operating time is 200 days (d). So the resulting distance of this vehicle is $$3500.25 km \cdot (\frac{200 d} {365 d}) = 1917.945 km$$

See also

dist_mileage for the determination of a parametric annual mileage distribution and estimate_cdf for the estimation of failure probabilities.

Examples

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

# Example 1 - Reproducibility of drawn random numbers:
set.seed(1234)
mcs_distances <- mcs_mileage(
  x = mcs_tbl,
  distribution = "lognormal"
)

# Example 2 - MCS for distances with exponential annual mileage distribution:
mcs_distances_2 <- mcs_mileage(
  x = mcs_tbl,
  distribution = "exponential"
)

# Example 3 - MCS for distances with downstream probability estimation:
## Apply 'estimate_cdf()' to *$data:
prob_estimation <- estimate_cdf(
  x = mcs_distances$data,
  methods = "kaplan"
)

## Apply 'plot_prob()':
plot_prob_estimation <- plot_prob(prob_estimation)