[英]Cross-correlation with multiple groups in one data.table
我想計算data.table中時間序列組之間的互相關。 我有這種格式的時間序列數據:
data = data.table( group = c(rep("a", 5),rep("b",5),rep("c",5)) , Y = rnorm(15) )
group Y
1: a 0.90855520
2: a -0.12463737
3: a -0.45754652
4: a 0.65789709
5: a 1.27632196
6: b 0.98483700
7: b -0.44282527
8: b -0.93169070
9: b -0.21878359
10: b -0.46713392
11: c -0.02199363
12: c -0.67125826
13: c 0.29263953
14: c -0.65064603
15: c -1.41143837
每組具有相同數量的觀察結果。 我正在尋找的是一種獲得組間互相關的方法:
group.1 group.2 correlation
a b 0.xxx
a c 0.xxx
b c 0.xxx
我正在編寫一個腳本來對每個組進行子集化並附加交叉相關,但數據大小相當大。 有沒有有效/禪的方式來做到這一點?
這有幫助嗎?
data[,id:=rep(1:5,3)]
dtw = dcast.data.table(data, id ~ group, value.var="Y" )[, id := NULL]
cor(dtw)
另一種方式是:
# data
set.seed(45L)
data = data.table( group = c(rep("a", 5),rep("b",5),rep("c",5)) , Y = rnorm(15) )
# method 2
setkey(data, "group")
data2 = data[J(c("b", "c", "a"))][, list(group2=group, Y2=Y)]
data[, c(names(data2)) := data2]
data[, cor(Y, Y2), by=list(group, group2)]
# group group2 V1
# 1: a b -0.2997090
# 2: b c 0.6427463
# 3: c a -0.6922734
並將這種“其他”方式概括為三個以上的群體......
data = data.table( group = c(rep("a", 5),rep("b",5),rep("c",5),rep("d",5)) ,
Y = rnorm(20) )
setkey(data, "group")
groups = unique(data$group)
ngroups = length(groups)
library(gtools)
pairs = combinations(ngroups,2,groups)
d1 = data[pairs[,1],,allow.cartesian=TRUE]
d2 = data[pairs[,2],,allow.cartesian=TRUE]
d1[,c("group2","Y2"):=d2]
d1[,cor(Y,Y2), by=list(group,group2)]
# group group2 V1
# 1: a b 0.10742799
# 2: a c 0.52823511
# 3: a d 0.04424170
# 4: b c 0.65407400
# 5: b d 0.32777779
# 6: c d -0.02425053
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