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. 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 $$3500.25 km \cdot (\frac{200 d} {365 d}) = 1917.945 km$$

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)