| acfByID | R Documentation | 
This function estimates the autocorrelation over time in a time series by a higher level unit, given by ID.
acfByID(
  xvar,
  timevar,
  idvar,
  data,
  lag.max = 10L,
  na.function = c("na.approx", "na.spline", "na.locf"),
  ...
)
| xvar | A character string giving the variable name of the variable to calculate autocorrelations on. | 
| timevar | A character string giving the variable name of the time variable. | 
| idvar | A character string giving the variable name of the ID variable. Can be missing if only one time series provided, in which case one will be created. | 
| data | A data.table containing the variables used in the formula. This is a required argument. If a data.frame, it will silently coerce to a data.table. If not a data.table or data.frame, it will attempt to coerce, with a message. | 
| lag.max | An integer of the maximum lag to estimate. Must be equal to or greater than the number of observations for all IDs in the dataset. | 
| na.function | A character string giving the name of the function to use to address any missing data. Functions come from the zoo package, and must be one of: “na.approx”, “na.spline”, “na.locf”. | 
| ... | Additional arguments passed to  | 
A data.table of the estimated autocorrelations by ID and lag
## example 1
dat <- data.table::data.table(
  x = sin(1:30),
  time = 1:30,
  id = 1)
acfByID("x", "time", "id", data = dat)
## example 2
dat2 <- data.table::data.table(
  x = c(sin(1:30), sin((1:30)/10)),
  time = c(1:30, 1:30),
  id = rep(1:2, each = 30))
dat2$x[4] <- NA
res <- acfByID("x", "time", "id", data = dat2, na.function = "na.approx")
ggplot2::ggplot(res, ggplot2::aes(factor(Lag), AutoCorrelation)) +
  ggplot2::geom_boxplot()
## clean up
rm(dat, dat2, res)
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