如何更有效地计算滚动协方差

Question

我试图在R中计算一组数据(我的x变量的每一列)和另一个(y变量)之间的滚动协方差.我想我可以使用其中一个应用函数,但是找不到如何滚动两个同时设置输入.这是我尝试过的:

 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])})

但没有人做我想做的事.我找到的一个解决方案是使用for循环,但想知道我是否可以在R中更高效:

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)])
    }
}

这给了我预期的结果:

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

Answer 1

这是一个解决方案:rollapply()和sapply():

sapply(1:5, function(j) rollapply(1:100, 5, function(i) cov(x[i, j], y[i])))

我认为它比具有for循环的解决方案更具可读性和更多R-ish,但我检查过microbenchmark它似乎更慢.

Answer 2

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

还可以看看:

library(PerformanceAnalytics)
?chart.rollingCorrelation