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"), ...)



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.


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.


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.


# 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)