mcs_mileage.default {weibulltools}

Simulation of Unknown Covered Distances using a Monte Carlo Approach

Description

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'.

Usage

## Default S3 method:
mcs_mileage(
  x,
  time,
  status = NULL,
  id = paste0("ID", seq_len(length(time))),
  distribution = c("lognormal", "exponential"),
  ...
)

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.

status

Optional argument. If used, it must contain binary data (0 or 1) indicating whether a unit is a right censored observation (= 0) or a failure (= 1).

If status is provided, class wt_reliability_data is assigned to the output of mcs_mileage, which enables the direct application of estimate_cdf on distances.

id

Identification of every unit.

distribution

Supposed distribution of the annual mileage.

...

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

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

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.

See Also

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

Examples

# Example 1 - Reproducibility of drawn random numbers:
set.seed(1234)
mcs_distances <- mcs_mileage(
  x = field_data$mileage,
  time = field_data$dis,
  status = field_data$status,
  id = field_data$vin,
  distribution = "lognormal"
)

# Example 2 - MCS for distances with exponential annual mileage distribution:
mcs_distances_2 <- mcs_mileage(
  x = field_data$mileage,
  time = field_data$dis,
  status = field_data$status,
  id = field_data$vin,
  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)