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$id), time = val)
out <- merge(dat, dat., all = TRUE)
do.call("rbind", lapply(split(out, out$id), function (d) {
x <- d[d$time == val, ]
ind <- is.na(x$y)
if (ind && any(ii <- d$time == val - 1)) {
x$y <- d$y[ii]
} else if (ind && any(ii <- d$time == val + 1)) {
x$y <- d$y[ii]
}
x
}))
If you want the output to be a matrix (and not a data.frame), then you
could change the do.call("rbind", lapply(...)) part with t(sapply(...)).
I hope it helps.
Best,
Dimitris
gallon li wrote:
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 observation
at time 3 is also missing, then i want to select observation at time 5.
otherwise i will put a missing value there. the selected set is like
id time y
1 3 15
2 4 11
3 4 16
4 5 13
5 4 NA
...
so the rule is (1) obs at time 4 for each id; (2) if no such obs, then look
for obs at time 3; (3) if no such obs, then look for obs at time 5; (4)
otherwise, NA.
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--
Dimitris Rizopoulos
Assistant Professor
Department of Biostatistics
Erasmus Medical Center
Address: PO Box 2040, 3000 CA Rotterdam, the Netherlands
Tel: +31/(0)10/7043478
Fax: +31/(0)10/7043014
______________________________________________
R-help@r-project.org mailing list
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PLEASE do read the posting guide http://www.R-project.org/posting-guide.html
and provide commented, minimal, self-contained, reproducible code.
