Create consistent mcs_delay_data
based on an existing data.frame
(preferred)
or on multiple equal length vectors.
mcs_delay_data(
data = NULL,
date_1,
date_2,
time,
status = NULL,
id = NULL,
.keep_all = FALSE
)
Either NULL
or a data.frame
. If data is NULL
, date_1
, date_2
,
time
, status
and id
must be vectors containing the data. Otherwise date_1
,
date_2
, time
, status
and id
can be either column names or column positions.
A date 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 for the vector-based approach and a vector of column names or positions for the data-based approach. 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 date 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.
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_delay, which enables the direct application of estimate_cdf
on operating times.
Identification of every unit.
If TRUE
keep remaining variables in data
.
A tibble
with class wt_mcs_delay_data
that is formed for the downstream
Monte Carlo method mcs_delay.
It contains the following columns (if .keep_all = FALSE
):
Column(s) preserving the input of date_1
. For the vector-based approach
with unnamed input, column name(s) is (are) date_1
(date_1.1
, date_1.2
, ...
, date_1.i
).
Column(s) preserving the input of date_2
. For the vector-based approach
with unnamed input, column name(s) is (are) date_2
(date_2.1
, date_2.2
, ...
, date_2.i
).
time
: Input operating times.
status
(optional) :
If is.null(status)
column status
does not exist.
If status
is provided the column contains the entered binary
data (0 or 1).
id
: Identification for every unit.
If .keep_all = TRUE
, the remaining columns of data
are also preserved.
The attributes mcs_start_dates
and mcs_end_dates
hold the name(s) of the
column(s) that preserve the input of date_1
and date_2
.
dist_delay for the determination of a parametric delay distribution and mcs_delay for the Monte Carlo method with respect to delays.
# Example 1 - Based on an existing data.frame/tibble and column names:
mcs_tbl <- mcs_delay_data(
data = field_data,
date_1 = production_date,
date_2 = registration_date,
time = dis,
status = status
)
# Example 2 - Based on an existing data.frame/tibble and column positions:
mcs_tbl_2 <- mcs_delay_data(
data = field_data,
date_1 = 7,
date_2 = 8,
time = 2,
id = 1
)
# Example 3 - Keep all variables of the tibble/data.frame entered to argument data:
mcs_tbl_3 <- mcs_delay_data(
data = field_data,
date_1 = production_date,
date_2 = registration_date,
time = dis,
status = status,
id = vin,
.keep_all = TRUE
)
# Example 4 - For multiple delays (data-based):
mcs_tbl_4 <- mcs_delay_data(
data = field_data,
date_1 = c(production_date, repair_date),
date_2 = c(registration_date, report_date),
time = dis,
status = status
)
# Example 5 - Based on vectors:
mcs_tbl_5 <- mcs_delay_data(
date_1 = field_data$production_date,
date_2 = field_data$registration_date,
time = field_data$dis,
status = field_data$status,
id = field_data$vin
)
# Example 6 - For multiple delays (vector-based):
mcs_tbl_6 <- mcs_delay_data(
date_1 = list(field_data$production_date, field_data$repair_date),
date_2 = list(field_data$registration_date, field_data$report_date),
time = field_data$dis,
status = field_data$status,
id = field_data$vin
)
# Example 7 - For multiple delays (vector-based with named dates):
mcs_tbl_7 <- mcs_delay_data(
date_1 = list(d11 = field_data$production_date, d12 = field_data$repair_date),
date_2 = list(d21 = field_data$registration_date, d22 = field_data$report_date),
time = field_data$dis,
status = field_data$status,
id = field_data$vin
)