I am trying to compute a rolling covariance between a set of data (each column of my x variable) and one other (y variable) in R. I thought I could use one of the apply functions, but can not find how to roll two set of inputs at the same time. Here is what I tried :
set.seed(1)
x<-matrix(rnorm(500),nrow=100,ncol=5)
y<-rnorm(100)
rollapply(x,width=5,FUN= function(x) {cov(x,y)})
z<-cbind(x,y)
rollapply(z,width=5, FUN=function(x){cov(z,z[,6])})
But none is doing what I would like. One solution I found is to use a for loop, but wondering if I can be more efficient in R than :
dResult<-matrix(nrow=96,ncol=5)
for(iLine in 1:96){
for(iCol in 1:5){
dResult[iLine,iCol]=cov(x[iLine:(iLine+4),iCol],y[iLine:(iLine+4)])
}
}
which gives me the expected result :
head(dResult)
[,1] [,2] [,3] [,4] [,5]
[1,] 0.32056460 0.05281386 -1.13283586 -0.01741274 -0.01464430
[2,] -0.03246014 0.78631603 -0.34309778 0.29919297 -0.22243572
[3,] -0.16239479 0.56372428 -0.27476604 0.39007645 0.05461355
[4,] -0.56764687 0.09847672 0.11204244 0.78044096 -0.01980684
[5,] -0.43081539 0.01904417 0.01282632 0.35550327 0.31062580
[6,] -0.28890607 0.03967327 0.58307743 0.15055881 0.60704533
set.seed(1)
x<-as.data.frame(matrix(rnorm(500),nrow=100,ncol=5))
y<-rnorm(100)
library(zoo)
covResult = sapply(x,function(alpha) {
cov_value = rollapply(cbind(alpha,y),width=5,FUN = function(beta) cov(beta[,1],beta[,2]),by.column=FALSE,align="right")
return(cov_value)
})
head(covResult)
# V1 V2 V3 V4 V5
#[1,] 0.32056460 0.05281386 -1.13283586 -0.01741274 -0.01464430
#[2,] -0.03246014 0.78631603 -0.34309778 0.29919297 -0.22243572
#[3,] -0.16239479 0.56372428 -0.27476604 0.39007645 0.05461355
#[4,] -0.56764687 0.09847672 0.11204244 0.78044096 -0.01980684
#[5,] -0.43081539 0.01904417 0.01282632 0.35550327 0.31062580
#[6,] -0.28890607 0.03967327 0.58307743 0.15055881 0.60704533
Also check out:
library(PerformanceAnalytics)
?chart.rollingCorrelation