This function models a delay (in days) random variable (e.g. in logistic,
registration, report) using a supposed continuous distribution. First, the
element-wise differences in days of both vectors date_1 and date_2 are
calculated and then the parameter(s) of the assumed
distribution is (are) estimated with maximum likelihood. See 'Details' for
more information.
# S3 method for default
dist_delay(..., date_1, date_2, distribution = c("lognormal", "exponential"))Further arguments passed to or from other methods. Currently not used.
A vector of class character or Date, in the format "yyyy-mm-dd",
representing the earlier of the two dates belonging to a particular delay.
Use NA for missing elements.
If more than one delay is to be considered, use a list where the first element is the earlier date of the first delay, the second element is the earlier date of the second delay, and so forth (see 'Examples').
A vector of class character or Date in the format "yyyy-mm-dd".
date_2 is the counterpart of date_1 and is used the same as date_1, just with
the later date(s) of the particular delay(s). Use NA for missing elements.
Supposed distribution of the respective delay.
A list with class wt_delay_estimation which contains:
coefficients : A named vector of estimated parameter(s).
delay : A numeric vector of element-wise computed differences in days.
distribution : Specified distribution.
If more than one delay was considered, the resulting output is a list with class
wt_delay_estimation_list. In this case each list element has class
wt_delay_estimation and the items listed above, are included.
The distribution parameter(s) is (are) determined on the basis of complete
cases, i.e. there is no NA in one of the related vector elements
c(date_1[i], date_2[i]). Time differences less than or equal to zero are
not considered as well.
# Example 1 - Delay in registration:
params_delay_regist <- dist_delay(
date_1 = field_data$production_date,
date_2 = field_data$registration_date,
distribution = "lognormal"
)
# Example 2 - Delay in report:
params_delay_report <- dist_delay(
date_1 = field_data$repair_date,
date_2 = field_data$report_date,
distribution = "exponential"
)
#> Warning: At least one of the date differences is less than or equal to 0 and is ignored for the estimation step!
# Example 3 - Delays in registration and report with same distribution:
params_delays <- dist_delay(
date_1 = list(field_data$production_date, field_data$repair_date),
date_2 = list(field_data$registration_date, field_data$report_date),
distribution = "lognormal"
)
#> Warning: At least one of the date differences is less than or equal to 0 and is ignored for the estimation step!
# Example 4 - Delays in registration and report with different distributions:
params_delays_2 <- dist_delay(
date_1 = list(field_data$production_date, field_data$repair_date),
date_2 = list(field_data$registration_date, field_data$report_date),
distribution = c("lognormal", "exponential")
)
#> Warning: At least one of the date differences is less than or equal to 0 and is ignored for the estimation step!