R/mcs_mileage.R
mcs_mileage.default.RdThis 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'.
# S3 method for default
mcs_mileage(
x,
time,
status = NULL,
id = paste0("ID", seq_len(length(time))),
distribution = c("lognormal", "exponential"),
...
)A numeric vector of distances covered. Use NA for missing elements.
A numeric vector of operating times. Use NA for missing elements.
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.
Identification of every unit.
Supposed distribution of the annual mileage.
Further arguments passed to or from other methods. Currently not used.
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 $$3500.25 km \cdot (\frac{200 d} {365 d}) = 1917.945 km$$
dist_mileage for the determination of a parametric annual mileage distribution and estimate_cdf for the estimation of failure probabilities.
# 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)