data.table 在 R 中的累积计算(例如累积相关性)

Cumulative Calculations (e.g. cumulative correlation) with data.table in R

在 R 中,我有一个 data.table 有两个测量值 redgreen,我想计算它们的累积相关性。

library(data.table)
DT <- data.table(red   = c(1, 2, 3, 4, 5,  6.5, 7.6, 8.7),
                 green = c(2, 4, 6, 8, 10, 12,  14,  16),
                 id    = 1:8)

如何在一个 data.table 命令中获得以下输出?

...
> DT[1:5, cor(red, green)]
[1] 1                     # should go into row 5
> DT[1:6, cor(red, green)]
[1] 0.9970501             # should go into row 6, and so on ...
> DT[1:7, cor(red, green)]
[1] 0.9976889

编辑: 我知道它可以通过循环来解决,但是我的 data.table 有大约 100 万行被分成更小的块,所以循环相当慢,我认为可能还有其他可能性。

创建一个 cumcor 函数怎么样?

library(data.table)

DT <- data.table(red   = c(1, 2, 3, 4, 5,  6.5, 7.6, 8.7),
                 green = c(2, 4, 6, 8, 10, 12,  14,  16),
                 id    = 1:8)

cumcor <- function(x, y, start = 5, ...) {
    c(rep(NA, start - 1), sapply(start:length(x), function(k) cor(x[1:k], y[1:k]), ...))
}

DT[, list(red, green, cumcor = cumcor(red, green))]
   red green    cumcor
1: 1.0     2        NA
2: 2.0     4        NA
3: 3.0     6        NA
4: 4.0     8        NA
5: 5.0    10 1.0000000
6: 6.5    12 0.9970501
7: 7.6    14 0.9976889
8: 8.7    16 0.9983762

请注意上面的 cumcor 函数在开始时需要更多的 QC(xy 具有相同的长度,start 大于 0,等等。 )

根据我对累积方差的类似问题 的回答,您可以找到累积协方差

library(dplyr) # for cummean
cum_cov <- function(x, y){
  n <- 1:length(x)
  res <- cumsum(x*y) - cummean(x)*cumsum(y) - cummean(y)*cumsum(x) + n*cummean(x)*cummean(y)
  res / (n-1)
}

cum_var <- function(x){# copy-pasted from previous answer
    n <- 1:length(x)
    (cumsum(x^2) - n*cummean(x)^2) / (n-1)
}

然后累积相关性

cum_cor <- function(x, y) cum_cov(x, y)/sqrt(cum_var(x)*cum_var(y))
DT[, cumcor:=cum_cor(red, green),]
   red green id    cumcor
1: 1.0     2  1       NaN
2: 2.0     4  2 1.0000000
3: 3.0     6  3 1.0000000
4: 4.0     8  4 1.0000000
5: 5.0    10  5 1.0000000
6: 6.5    12  6 0.9970501
7: 7.6    14  7 0.9976889
8: 8.7    16  8 0.9983762

希望速度够快

x <- rnorm(1e6)
y <- rnorm(1e6)+x
system.time(cum_cor(x, y))
#   user  system elapsed 
#  0.319   0.020   0.339