[英]Cross Correlation (CCF) in R for each ID / by ID
我對 R 很陌生。我的數據看起來(簡化)是這樣的:
ID <- c(1,1,1,1,2,2,3,3,3,3,4,4,4)
Affect <- c(0.8, 0.5, NA, 0.8, 0.2, 0.1, 0.7, 1.1, 0.9, 0.5, 0.3, NA, 0.9)
Paranoia <- c(0.9, 0.6, 0.4, 0.2, 0.1, NA, 0.3, 0.1, 0.9, 1.5, 0.4, 0.1, 0.6)
df <- cbind(ID, Affect, Paranoia)
我想要計算 R 中的互相關以找出影響是否先於偏執或相反。 我怎樣才能做到這一點? 我嘗試了幾種方法,但從未成功。 先感謝您!
我們可以刪除all
'Affect' 或 'Paranoia' 作為NA
的 'ID',然后用 0 ( replace_na
) replace
剩余的 NA 並應用ccf
library(tseries)
library(dplyr)
library(tidyr)
out <- df %>%
group_by(ID) %>%
filter(!(all(is.na(Affect))|all(is.na(Paranoia)))) %>%
mutate_at(vars(Affect, Paranoia), replace_na, 0) %>%
summarise(ccfout = list(ccf(Affect, Paranoia)))
out$ccfout[[1]]
#
#Autocorrelations of series ‘X’, by lag
# -3 -2 -1 0 1 2 3
#-0.264 -0.078 0.575 0.229 -0.246 -0.521 0.305
out$ccfout[[3]]
#Autocorrelations of series ‘X’, by lag
# -3 -2 -1 0 1 2 3
#-0.163 0.449 0.408 -0.735 -0.490 0.286 0.245
或者使用group_split/map
library(purrr)
df %>%
group_split(ID) %>%
map(~ .x %>%
mutate_at(vars(Affect, Paranoia), replace_na, 0) %>%
{ccf(.$Affect, .$Paranoia)})
#[[1]]
#Autocorrelations of series ‘X’, by lag
# -3 -2 -1 0 1 2 3
#-0.264 -0.078 0.575 0.229 -0.246 -0.521 0.305
#[[2]]
#Autocorrelations of series ‘X’, by lag
#0
#1
#[[3]]
#Autocorrelations of series ‘X’, by lag
# -3 -2 -1 0 1 2 3
#-0.163 0.449 0.408 -0.735 -0.490 0.286 0.245
#[[4]]
#Autocorrelations of series ‘X’, by lag
# -1 0 1
#-0.289 0.954 -0.636
df <- data.frame(ID, Affect, Paranoia)
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