Hi:
Here's one approach:
a=matrix(1:50,nrow=10)
a2=floor(jitter(a,amount=50))
# Write a function to combine the columns of interest
# into a data frame and fit a linear model
regfn <- function(k) {
rdf <- data.frame(x = a[k, ], y = a2[k, ])
lm(y ~ x, data = rdf)
}
# Use lapply() to
The apply function also works with multi-dimensional arrays, I think
this is what you want to achieve using a 3d array:
aaa <- array(NA, dim = c(2, dim(a)))
aaa[1,,] <- a
aaa[2,,] <- a2
apply(aaa, 3, function(x)lm(x[1,]~x[2,]))
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R-help@r-project.org m
I hope someone experience with plyr package comes and helps because this
sounds like what it does well, but for your specific example something like
this works:
A = rbind(a,a2)
q = apply(A,2,function(x) {lm(x[1:nrow(a)] ~ x[-(1:nrow(a))])})
but yeah, that's pretty rough so I hope someone can come
I realize this should be simple, but even after reading over the several
help pages several times, I still cannot decide between the myriad "apply"
functions to address it. I simply want to apply a function to all the rows
(or columns) of the same index from two (or more) identically sized arrays
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