Question

我希望使用R中的运行窗口计算12个变量的相关系数。

我的数据存储在动物园对象中，％m。％d。％Y％H：％M：％S索引，12个变量中的每一个都有1343个观察值。我不知道我将要使用的窗口大小，但如果需要我可以更改它。

@Joshua Ulrich已发布here如何使用rollapplyr计算滚动相关性，但此示例只有两个变量。由于我的R经验有限，我不确定如何使用其中一个应用族函数来运行所有12个变量的相关性。

任何指针都会非常感激

我的数据如下：

> head(wideRawXTS)
                    DO0182U09A3 DO0182U09B3 DO0182U09C3 DO0182U21A1 DO0182U21A2 DO0182U21A3 DO0182U21B1 DO0182U21B2 DO0182U21B3
2017-01-20 16:30:00     -101.50     -103.37     -103.86     -104.78     -104.95     -105.33     -102.50      -99.43     -104.05
2017-01-20 16:45:00     -101.32     -102.75     -104.22     -104.51     -103.94     -105.29     -102.82     -101.99     -103.94
2017-01-20 17:00:00     -101.45     -103.30     -103.93     -104.70     -104.82     -105.13     -103.72     -103.95     -104.25
2017-01-20 17:15:00     -100.91      -95.92      -99.22     -103.83     -104.72     -105.19     -103.57     -101.36     -104.09
2017-01-20 17:30:00     -100.91     -103.04     -104.09     -102.15     -104.91     -105.18     -103.88     -104.09     -103.96
2017-01-20 17:45:00     -100.97     -103.67     -104.12     -105.07     -104.23      -97.48     -103.92     -103.89     -104.01
                    DO0182U21C1 DO0182U21C2 DO0182U21C3
2017-01-20 16:30:00     -104.51     -104.42     -105.17
2017-01-20 16:45:00     -104.74     -104.65     -105.25
2017-01-20 17:00:00     -105.02     -105.04     -105.32
2017-01-20 17:15:00     -103.90     -102.95     -105.16
2017-01-20 17:30:00     -104.75     -105.07     -105.23
2017-01-20 17:45:00     -105.08     -105.14     -104.89

#Importing Data
rawDF <- read.csv("RTWP_DO0182_14Day_Raw.csv",
                  header = F,
                  col.names = c("Period Start Time",
                                "PLMNname",
                                "RNCname",
                                "WBTSname",
                                "WBTSID",
                                "WCELname",
                                "WCELID",
                                "Overload",
                                "AverageRTWP",
                                "DC_RTWP_High_PRX",
                                "RTWP_Threshold"),
                  colClasses = c("character",
                                 "NULL", #drops PLMN name
                                 "NULL", #drops RNC name
                                 "character", 
                                 "integer",
                                 "character",
                                 "integer",
                                 "NULL", #drops Overload
                                 "numeric",
                                 "NULL", #drops DC_RTWP_High_PRX
                                 "NULL"), #drops RTWP_Threshold
                  strip.white=TRUE,
                  na.strings = c("NA", 
                                 "NULL"),
                  as.is = T,
                  skip=2)

#convert period.start.time to POSIXlt
rawDF$Period.Start.Time <- as.POSIXlt(strptime(rawDF$Period.Start.Time, 
                                               format="%m.%d.%Y %H:%M:%S"))

#dcast the long data frame to a wide data frame
wideRawDF <- dcast(rawDF, 
                   Period.Start.Time ~ WCELname,
                   value.var = "AverageRTWP")

#assign the date times as rownames for converting to XTS
rownames(wideRawDF) = wideRawDF[[1]]

#drops the duplicate Period Start Time column since date times are rownames
wideRawDF$Period.Start.Time <- NULL

#Convert wideRawDF to XTS time series
wideRawDF <- as.xts(wideRawDF)

#NA values replaced by interpolated values
wideRawDF <- na.approx(wideRawDF, na.rm = FALSE)

#DF is centered by subtracting the column means and scaled by dividing the
#centered columns by their standard deviations
wideRawDFscaled <- scale(wideRawDF, center = TRUE, scale = TRUE)

#window <- 20
#cors <- combn(names(wideRawDFscaled),2,
#              function(p) rollapply(wideRawDFscaled, window ,
#                                    function(x) cor(x[,p[1]],x[,p[2]]),
#                                    by.column = FALSE))
#colnames(cors) <- combn(names(wideRawDFscaled),2,paste,collapse=".")

#Running window of correlation coefficients
Cor <- function(x) {
  corr <- cor(wideRawDFscaled)
  out <- as.data.frame.table(corr)[lower.tri(corr), ]
  with(out, setNames(Freq, paste(Var1, Var2)))
}
slidingCor <- rollapplyr(wideRawDFscaled, 6, Cor, by.column = FALSE)

Answer 1

您可以使用combn将关联应用于每对列。调整您引用的答案：

window <- 20
cors <- combn(colnames(wideRawXTS),2,
              function(p) rollapply(wideRawXTS, window  ,
                                    function(x) cor(x[,p[1]],x[,p[2]]), 
                                    by.column=FALSE))
colnames(cors) <- combn(colnames(wideRawXTS),2, paste, collapse=".")

Answer 2

假设在Note中可重复显示3列输入并使用宽度为3的窗口进行说明，定义接受矩阵的相关函数.PHONY并计算其相关矩阵，提取下三角形部分以消除冗余并添加列名。现在使用Cor：

rollapplyr

，并提供：

library(xts)

Cor <- function(x) {
    corr <- cor(x)
    out <- as.data.frame.table(corr)[lower.tri(corr), ]
    with(out, setNames(Freq, paste(Var1, Var2)))
}
rollapplyr(xx, 3, Cor, by.column = FALSE)

注意：以可重现的形式输入DO0182U09B3.DO0182U09A3 DO0182U09C3.DO0182U09A3 DO0182U09C3.DO0182U09B3 2017-01-20 16:30:00 NA NA NA 2017-01-20 16:45:00 NA NA NA 2017-01-20 17:00:00 0.98573 -0.99613 -0.99671 2017-01-20 17:15:00 0.98629 0.95983 0.99297 2017-01-20 17:30:00 0.52664 0.47475 0.99820 2017-01-20 17:45:00 0.56204 0.50460 0.99769：

xx

R中时间序列相关系数的运行窗口

2 个答案: