Try this. 'by' splits up the data frame into one data frame
per id and then f acts separately on each such sub-dataframe
returning a ts series with NAs for the missings. cbind'ing
those all together gives us this series with one column
per id:
> tt
Time Series:
Start = 1
End = 6
Frequency = 1
one way is the following:
dat <- read.table(textConnection("id time y
1 1 10
1 2 12
1 3 15
1 6 18
2 1 8
2 3 9
2 4 11
2 5 12
3 1 8
3 4 16
4 1 9
4 5 13
5 1 7
5 2 9
5 6 11"), header = TRUE)
closeAllConnections()
val <- 4
dat. <- data.frame(id = unique(dat$id), time = val)
out <- merge(dat, dat., al
I have the following longitudinal data:
id time y
1 1 10
1 2 12
1 3 15
1 6 18
2 1 8
2 3 9
2 4 11
2 5 12
3 1 8
3 4 16
4 1 9
4 5 13
5 1 7
5 2 9
5 6 11
I want to select the observations at time 4. if the observation at time 4 is
missing, then i want to slect the observation at time 3. if the ob
3 matches
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