CODEWITHSUNDEEP
rgroup-bycross-correlation
Yve
Yve

Reputation: 51

Cross Correlation (CCF) in R for each ID / by ID

I am quite new to R. My data looks (simplified) like this:

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)

What I want calculate a cross correlation in R in order to find out if affect preceeds paranoia or the other way round. How can I do this? I tried several ways but never succeeded. Thank you in advance!

Upvotes: 3

Views: 1033

Answers (1)

akrun
akrun

Reputation: 887951

We can remove the 'ID's having all of 'Affect' or 'Paranoia' as NA, then replace the remaining NA with 0 (replace_na) and apply the 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

Or using 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

data

df <- data.frame(ID, Affect, Paranoia)

Upvotes: 3

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