I doubt its any faster than using a loop but probably less code is:
library(zoo)
z1 - zoo(t(mat1)); z2 - zoo(t(mat2))
idx - 1:ncol(z1)
out - rollapply(cbind(z1, z2), 11, by.column = FALSE,
FUN = function(x) cor(x[,idx],x[,-idx]))
will give you a 10 x 16 multivariate zoo series such that:
out[1, ] is c(cor(t(mat1[,1:11]), t(mat2[,1:11])))
out[2, ] is c(cor(t(mat1[,2:12]), t(mat2[,2:12])))
etc.
and t(out) is a matrix in the orientation you asked for.
Try
library(zoo)
vignette(zoo)
for an intro to zoo.
On 7/24/07, Bernzweig, Bruce (Consultant) [EMAIL PROTECTED] wrote:
Hi all,
Situation:
- I have two matrices each w/ 4 rows and 20 columns.
mat1 - matrix(sample(1:500,80), ncol = 20,
dimnames=list(paste(mat1row, 1:4, sep=),
paste(mat1col, 1:20, sep=)))
mat2 - matrix(sample(501:1000,80), ncol = 20,
dimnames=list(paste(mat2row, 1:4, sep=),
paste(mat2col, 1:20, sep=)))
- Each column represents a value in a time series.
Q: What do I want:
Calculate moving average correlations for each row x row pair:
For each row x row pair I want 10 values representing moving average
correlations for 10 sets of time-values:
cor(mat1[1,1:10], mat2[1,1:10])
cor(mat1[1,2:11], mat2[1,2:11])
...
cor(mat1[1,11:20], mat2[1,11:20])
cor(mat1[1,1:10], mat2[2,1:10])
...
cor(mat1[4,11:20], mat2[4,11:20])
Result would be a 16 (rows) x 10 (col) matrix matMA
ma1, ma2, ..., ma10 for (mat1 row1) x (mat2 row1)
ma1, ma2, ..., ma10 for (mat1 row1) x (mat2 row2)
...
ma1, ma2, ..., ma10 for (mat1 row4) x (mat2 row3)
ma1, ma2, ..., ma10 for (mat1 row4) x (mat2 row4)
I would like to be able to do this without using a for loop
due to the slowness of that method.
Is it possible to iterate through subsets w/o using a for loop?
Thanks,
- Bruce
P
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