contstructs start stop formulation of event time data after a variable in the data.set. Similar to SurvSplit of the survival package but can also split after random time given in data frame.
Usage
event.split(
data,
time = "time",
status = "status",
cuts = "cuts",
name.id = "id",
name.start = "start",
cens.code = 0,
order.id = TRUE,
time.group = FALSE
)Arguments
- data
data to be split
- time
time variable.
- status
status variable.
- cuts
cuts variable or numeric cut (only one value)
- name.id
name of id variable.
- name.start
name of start variable in data, start can also be numeric "0"
- cens.code
code for the censoring.
- order.id
order data after id and start.
- time.group
make variable "before"."cut" that keeps track of wether start,stop is before (1) or after cut (0).
Examples
set.seed(1)
d <- data.frame(event=round(5*runif(5),2),start=1:5,time=2*1:5,
status=rbinom(5,1,0.5),x=1:5)
d
#> event start time status x
#> 1 1.33 1 2 1 1
#> 2 1.86 2 4 1 2
#> 3 2.86 3 6 1 3
#> 4 4.54 4 8 1 4
#> 5 1.01 5 10 0 5
d0 <- event.split(d,cuts="event",name.start=0)
d0
#> event start time status x start.0 id
#> 1 1.33 1 1.33 0 1 0.00 1
#> 1.1 1.33 1 2.00 1 1 1.33 1
#> 2 1.86 2 1.86 0 2 0.00 2
#> 2.1 1.86 2 4.00 1 2 1.86 2
#> 3 2.86 3 2.86 0 3 0.00 3
#> 3.1 2.86 3 6.00 1 3 2.86 3
#> 4 4.54 4 4.54 0 4 0.00 4
#> 4.1 4.54 4 8.00 1 4 4.54 4
#> 5 1.01 5 1.01 0 5 0.00 5
#> 5.1 1.01 5 10.00 0 5 1.01 5
dd <- event.split(d,cuts="event")
dd
#> event start time status x id
#> 1 1.33 1.00 1.33 0 1 1
#> 1.1 1.33 1.33 2.00 1 1 1
#> 2 1.86 2.00 4.00 1 2 2
#> 3 2.86 3.00 6.00 1 3 3
#> 4 4.54 4.00 4.54 0 4 4
#> 4.1 4.54 4.54 8.00 1 4 4
#> 5 1.01 5.00 10.00 0 5 5
ddd <- event.split(dd,cuts=3.5)
ddd
#> event start time status x id cut.3.5
#> 1 1.33 1.00 1.33 0 1 1 3.5
#> 1.1 1.33 1.33 2.00 1 1 1 3.5
#> 2 1.86 2.00 3.50 0 2 2 3.5
#> 2.1 1.86 3.50 4.00 1 2 2 3.5
#> 3 2.86 3.00 3.50 0 3 3 3.5
#> 3.1 2.86 3.50 6.00 1 3 3 3.5
#> 4 4.54 4.00 4.54 0 4 4 3.5
#> 4.1 4.54 4.54 8.00 1 4 4 3.5
#> 5 1.01 5.00 10.00 0 5 5 3.5
event.split(ddd,cuts=5.5)
#> event start time status x id cut.3.5 cut.5.5
#> 1 1.33 1.00 1.33 0 1 1 3.5 5.5
#> 1.1 1.33 1.33 2.00 1 1 1 3.5 5.5
#> 2 1.86 2.00 3.50 0 2 2 3.5 5.5
#> 2.1 1.86 3.50 4.00 1 2 2 3.5 5.5
#> 3 2.86 3.00 3.50 0 3 3 3.5 5.5
#> 3.1 2.86 3.50 5.50 0 3 3 3.5 5.5
#> 3.1.1 2.86 5.50 6.00 1 3 3 3.5 5.5
#> 4 4.54 4.00 4.54 0 4 4 3.5 5.5
#> 4.1 4.54 4.54 5.50 0 4 4 3.5 5.5
#> 4.1.1 4.54 5.50 8.00 1 4 4 3.5 5.5
#> 5 1.01 5.00 5.50 0 5 5 3.5 5.5
#> 5.1 1.01 5.50 10.00 0 5 5 3.5 5.5
### successive cutting for many values
dd <- d
for (cuts in seq(2,3,by=0.3)) dd <- event.split(dd,cuts=cuts)
dd
#> event start time status x cut.2 id cut.2.3 cut.2.6 cut.2.9
#> 1 1.33 1.0 2.0 1 1 2 1 2.3 2.6 2.9
#> 2 1.86 2.0 2.3 0 2 2 2 2.3 2.6 2.9
#> 2.1 1.86 2.3 2.6 0 2 2 2 2.3 2.6 2.9
#> 2.1.1 1.86 2.6 2.9 0 2 2 2 2.3 2.6 2.9
#> 2.1.1.1 1.86 2.9 4.0 1 2 2 2 2.3 2.6 2.9
#> 3 2.86 3.0 6.0 1 3 2 3 2.3 2.6 2.9
#> 4 4.54 4.0 8.0 1 4 2 4 2.3 2.6 2.9
#> 5 1.01 5.0 10.0 0 5 2 5 2.3 2.6 2.9
###########################################################################
### same but for situation with multiple events along the time-axis
###########################################################################
d <- data.frame(event1=1:5+runif(5)*0.5,start=1:5,time=2*1:5,
status=rbinom(5,1,0.5),x=1:5,start0=0)
d$event2 <- d$event1+0.2
d$event2[4:5] <- NA
d
#> event1 start time status x start0 event2
#> 1 1.102987 1 2 0 1 0 1.302987
#> 2 2.088278 2 4 1 2 0 2.288278
#> 3 3.343511 3 6 1 3 0 3.543511
#> 4 4.192052 4 8 0 4 0 NA
#> 5 5.384921 5 10 1 5 0 NA
d0 <- event.split(d,cuts="event1",name.start="start",time="time",status="status")
d0
#> event1 start time status x start0 event2 id
#> 1 1.102987 1.000000 1.102987 0 1 0 1.302987 1
#> 1.1 1.102987 1.102987 2.000000 0 1 0 1.302987 1
#> 2 2.088278 2.000000 2.088278 0 2 0 2.288278 2
#> 2.1 2.088278 2.088278 4.000000 1 2 0 2.288278 2
#> 3 3.343511 3.000000 3.343511 0 3 0 3.543511 3
#> 3.1 3.343511 3.343511 6.000000 1 3 0 3.543511 3
#> 4 4.192052 4.000000 4.192052 0 4 0 NA 4
#> 4.1 4.192052 4.192052 8.000000 0 4 0 NA 4
#> 5 5.384921 5.000000 5.384921 0 5 0 NA 5
#> 5.1 5.384921 5.384921 10.000000 1 5 0 NA 5
###
d00 <- event.split(d0,cuts="event2",name.start="start",time="time",status="status")
d00
#> event1 start time status x start0 event2 id
#> 1 1.102987 1.000000 1.102987 0 1 0 1.302987 1
#> 1.1 1.102987 1.102987 1.302987 0 1 0 1.302987 1
#> 1.1.1 1.102987 1.302987 2.000000 0 1 0 1.302987 1
#> 2 2.088278 2.000000 2.088278 0 2 0 2.288278 2
#> 2.1 2.088278 2.088278 2.288278 0 2 0 2.288278 2
#> 2.1.1 2.088278 2.288278 4.000000 1 2 0 2.288278 2
#> 3 3.343511 3.000000 3.343511 0 3 0 3.543511 3
#> 3.1 3.343511 3.343511 3.543511 0 3 0 3.543511 3
#> 3.1.1 3.343511 3.543511 6.000000 1 3 0 3.543511 3
#> 4 4.192052 4.000000 4.192052 0 4 0 NA 4
#> 4.1 4.192052 4.192052 8.000000 0 4 0 NA 4
#> 5 5.384921 5.000000 5.384921 0 5 0 NA 5
#> 5.1 5.384921 5.384921 10.000000 1 5 0 NA 5