Hi,
I have the following dataset (simplified for example).
__DATA__
300.35 200.25 104.30
22.00 31.12 89.99
444.50 22.10 43.00
22.10 200.55 66.77
Now from that I wish to do the following:
1. Compute variance of each row
2. Pick top-2 row with highest variance
3. Store those selected rows for further processing
To achieve this, I tried to: a) read the table and compute
variance for each row, b) append variance with its original
row in a vector, c) store a vector into multidimentional array (matrix),
d) sort that array. But I am stuck at the step (b).
Can anybody suggest what's the best way to achieve
my aim above?
This is the sample code I have so far (not working).
__BEGIN__
#data <- read.table("testdata.txt")
# Is this a right way to initialize?
all.arr = NULL
for (gi in 1:nofrow) {
gex <- as.vector(data.matrix(data[gi,],rownames.force=FALSE))
#compute variance
gexvar <- var(gex)
# join variance with its original vector
nvec <- c(gexvar,gex)
# I'm stuck here.....This doesn't seem to work
all.arr <- data.frame(nvec)
}
print(all.arr)
__END__
--
Gundala Viswanath
Jakarta - Indonesia
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