timeToEvent: Calculate the time-to-next-event or the time-since-date for a...

In rem: Relational Event Models (REM)

Description

Calculate time-to-next-event or time-since-date for a REM data set.

Usage

timeToEvent(time, type = 'time-to-next-event', timeEventPossible = NULL)

Arguments

time	A integer or Date variable reflecting the time of the event. Note: make sure to specify event time not the event sequence in a counting process data set.
type	Either 'time-to-next-event' or 'time-since-date'. type = 'time-to-next-event' calculates the time between the current event and the event closes to the current in the past. type = 'time-since-date' uses the time-variable as well as the timeEventPossible-variable to calculate how much time has passed between the two variables, i.e., how long the event took to come true.
timeEventPossible	An optional integer or Date variable to be used if type = 'time-since-date' is specified.

Details

To come.

Author(s)

Laurence Brandenberger [e-mail]

See Also

rem-package

Examples

## get some random data
dt <- data.frame(
  sender = c('a', 'c', 'd', 'a', 'a', 'f', 'c'), 
  target = c('b', 'd', 'd', 'b', 'b', 'a', 'd'), 
  date = c(rep('10.01.90',2), '11.01.90', '04.01.90',
 '05.01.90', rep('10.01.90',2)),
  start = c(0, 0, 1, 1, 1, 3, 3), 
  end = rep(6, 7), 
  targetAvailableSince = c(rep(-10,6), -2),
  dateTargetAvailable = c(rep('31.12.89',6), '01.01.90')
)

## create event sequence
dt <- eventSequence(dt$date, dateformat = '%d.%m.%y', data = dt,
              type = "continuous", byTime = "daily",
              excludeDate = '07.01.90',
              returnData = TRUE, sortData = TRUE,
              returnDateSequenceData = FALSE)
## also return the sequenceData
dt.seq <- eventSequence(dt$date, dateformat = '%d.%m.%y', data = dt,
                    type = "continuous", byTime = "daily",
                    excludeDate = '07.01.90',
                    returnDateSequenceData = TRUE)

## create counting process data set
dts <- createRemDataset(
	data = dt, sender = dt$sender, target = dt$target, 
	eventSequence = dt$event.seq.cont, 
	eventAttribute = NULL, time = NULL, 
	start = dt$start, startDate = NULL, 
	end = dt$end, endDate = NULL, 
	timeformat = NULL, atEventTimesOnly = TRUE, 
	untilEventOccurrs = TRUE,
	includeAllPossibleEvents = FALSE, 
	possibleEvents = NULL, returnInputData = TRUE)	
## divide up the results: counting process data = 1, original data = 2
dt.rem <- dts[[1]]
dt <- dts[[2]]

## merge all necessary event attribute variables back in
dt.rem$targetAvailableSince <- dt$targetAvailableSince[match(dt.rem$eventID, 
	dt$eventID)]
dt.rem$dateTargetAvailable <- dt$dateTargetAvailable[match(dt.rem$eventID, 
	dt$eventID)]

## add dates to the eventTime
dt.rem$eventDate <- dt.seq$date.sequence[match(dt.rem$eventTime,
	dt.seq$event.sequence)]

## sort the dataframe according to eventTime
dt.rem <- dt.rem[order(dt.rem$eventTime), ]

## 1. numeric, time-to-next-event
dt.rem$timeToNextEvent <- timeToEvent(as.integer(dt.rem$eventTime))

## 2. numeric, time-since
dt.rem$timeSince <- timeToEvent(dt.rem$eventTime, 
                                type = 'time-since-date', 
                                dt.rem$targetAvailableSince)

## 3. Date, time-to-next-event
# since the event sequence excluded 06.01.90 => time to next event differs
# for the two specification with the integr (1) and the Date-variable (2).
# To be consistent, pick the eventTime instead of the Date-variable.
dt.rem$timeToNextEvent2 <- timeToEvent(as.Date(dt.rem$eventDate, '%d.%m.%y'))


## 4. Date, time-since
dt.rem$timeSince2 <- timeToEvent(
	as.Date(dt.rem$eventDate, '%d.%m.%y'),
	type = 'time-since-date', 
	as.Date(dt.rem$dateTargetAvailable, '%d.%m.%y'))

rem documentation built on May 2, 2019, 3:25 a.m.