`R/mcs_mileage.R`

`mcs_mileage.Rd`

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"), ...)

x | A |
---|---|

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

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

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)