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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