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I have zoo object with 10000+ rows. 

> head(tt) A B 2007-01-04 0.005945924 0.0021167475 2007-01-05 -0.004201991 -0.0080020024 2007-01-08 0.001740897 0.0045804104 2007-01-09 0.000000000 -0.0008163931 2007-01-10 -0.004503531 0.0032615812 2007-01-11 -0.005841138 0.0043863282

I have tried variations of the following line, but to no avail.

rollapply(tt, 21, function(x) cor(x[,1],x[,2]))

Every entry gave correlation of 1, looks like it's picking up the 1 off the diagonal of the correlation matrix.

2013-11-25 1 1 2013-11-26 1 1 2013-11-27 1 1 2013-11-29 1 1 2013-12-02 1 1 2013-12-03 1 1

What I really want is -0.4649, like the following

> cor(tt) A B A 1.0000000 -0.4649881 B -0.4649881 1.0000000
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3 Answers 3

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15

For your simple case, you could use TTR::runCor.

set.seed(21) x <- rnorm(30) y <- rnorm(30) z <- zoo(cbind(x,y),Sys.Date()-1:30) tail(rollapplyr(z, 21, function(x) cor(x[,1],x[,2]), by.column=FALSE)) tail(runCor(z[,1],z[,2],21))
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Comments

9

Try something like this:

x<-rnorm(100) y<-rnorm(100) rollapply(data.frame(x,y), 21 ,function(x) cor(x[,1],x[,2]), by.column=FALSE)

In other words, I think you may just need the by.column=FALSE argument. Works with a zoo object too

rollapply(zoo(cbind(x,y),Sys.Date()-1:100), 21 ,function(x) cor(x[,1],x[,2]), by.column=FALSE)

Edit to address a question from the comment about adding another column.

You can specify the columns you want to use in the cor function.

z<-rnorm(100) rollapply(zoo(cbind(x,y,z),Sys.Date()-1:100), 21 ,function(x) cor(x[,1],x[,3]), by.column=FALSE) rollapply(zoo(cbind(x,y,z),Sys.Date()-1:100), 21 ,function(x) cor(x[,2],x[,3]), by.column=FALSE)

by.column=FALSE indicates that the function should not be applied to each column separately. If by.column=TRUE, then the function will be applied to each column separately, and this is the default behavior.

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3 Comments

it works! what exactly does the by.column option do? if i was to have a 3x3 cor matrix, how would one go about picking up the right correlation?
@Jota, thanks for the comments, I asked the same question about more columns to Joshua Ulrich and then saw your amendments so I just deleted it, apoloiges. If I had 100 columns of data is there a more efficient way to run the cor function rather than specifying each pair? Could this be achieved using one of the apply family functions?
You can use this function in rollapply to handle any number of columns: function(x) cor(x)[lower.tri(cor(x))]
0

Here's how to compute 6 month running correlations using tq_transmute_xy from the tidyquant package:

library(tidyquant) x<-rnorm(100) y<-rnorm(100) zoo(cbind(x,y),Sys.Date()-1:100) tq_transmute_xy(x = x, y = y, mutate_fun = runCor, n = 6, col_rename = "rolling.corr.6")
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