On Dec 31, 2010, at 8:05 AM, Sarah wrote:
I'm sorry, I don't think I've made myself clear enough.
Cases have been randomly assigned to one of the two groups, with
certain
probabilities (based on other variables).
So, if there are too many people (i.e., more than 34) assigned to
group 0, I
would like to sample 34 cases from group 0, and give the rest of the
cases a
value 1. My dataframe would contain 40 cases; 34 with mar.y==0 and
the rest
given (or some already had) a value mar.y==1.
If, however, too few cases have been assigned to group 0, I need to
randomly
select cases from group 1 and put them in group 0 (i.e., give them a
value
0). My dataframe would contain the previous selected cases
(mar.y==0), PLUS
cases from group 1 who are now assigned to group 0 (mar.y==0), PLUS
the
remaining cases who stayed in group 1 (mar.y==1).
(In other words, how can I change the value for df$mar.y from 1 to 0
or vice
versa for some cases)?
With the script I've designed, only 34 cases would remain in the
dataframe
(the cases assigned to group 0)...
if (length(which(df$mar.y==0))>34) {
df <- df[sample(which(df$mar.y==0),34), ]
} else {
df <- df[c(which(df$mar.y==0),
sample(which(df$mar.y==1),34-length(which(df$mar.y==0)))), ]
}
Just work on indices.
set.seed(321)
df$newgrp= 0
if (length(which(df$mar.y==0)) > 34) { ## too many in group 0
df$newgrp[ !sample(which(df$mar.y==0),34) ] <- 1 # random excess to
group 1
}
# leave alone the situation with 34 in group 0
if (length(which(df$mar.y==0)) < 34) { ## too few in group 0
df$newgrp[ !sample(which(df$mar.y==1),6) ] <- 0 # random excess to
group 0
# df$newgrp is now your corrected group variable.
--
David.
....while 40 cases are needed.
Thanks for your replies.
Sarah.
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David Winsemius, MD
West Hartford, CT
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