CCF vs ccf：有什么区别？

Question

我想了解 feasts::CCF 与 base::ccf 之间的区别，以及为什么它们在 reprex 中产生不同的结果（NA 是否与它有关？）

## Data
df <- structure(list(date = structure(c(1590919200, 1590922800, 1590926400, 
1590930000, 1590933600, 1590937200, 1590940800, 1590944400, 1590948000, 
1590951600, 1590955200, 1590958800, 1590962400, 1590966000, 1590969600, 
1590973200, 1590976800, 1590980400, 1590984000, 1590987600, 1590991200, 
1590994800, 1590998400, 1591002000), tzone = "UTC", class = c("POSIXct", 
"POSIXt")), x = c(12.61, 14.2, 13.37, 16.68, 13.35, 11.42, 16.51, 
11.78, 12.18, 13.67, 14.12, 13.2, 11.24, 10.76, 12.93, 16.48, 
20.65, 14.55, NA, NA, NA, NA, NA, NA), y = c(459.07, 496.83, 
511.17, 510.99, 511.22, 511.16, 511.22, 511.08, 511.14, 511.24, 
511.21, 511.03, 511.13, 511.23, 511.1, 511.11, 511.34, 510.98, 
511.18, 509.62, 511.09, 510.89, 505.53, 497.52)), class = "data.frame", row.names = c(NA, 
-24L), spec = structure(list(cols = list(date = structure(list(
    format = ""), class = c("collector_datetime", "collector"
)), cp.sum = structure(list(), class = c("collector_double", 
"collector")), vru_gc_h2s_ppmv = structure(list(), class = c("collector_double", 
"collector"))), default = structure(list(), class = c("collector_guess", 
"collector")), skip = 1L), class = "col_spec"))

## Base R
ccf(df$x, df$y, lag.max = 12, plot = TRUE, na.action = na.pass)

## Feasts
library(tsibble)
library(feasts)

df  %>% 
  as_tsibble(index = date) %>%
  fill_gaps() %>% 
  CCF(x, y, lag_max = 12) %>%
  autoplot()

Answer 1

是的，结果取决于 NA 的处理方式。如果使用 na.omit 而不是 na.pass，ccf 给出与 CCF 相同的结果。

编辑

但是 - 如果您查看 CCF() 的源代码，在 RStudio 中使用例如 View(CCF)，您会发现它使用基础 ccf:

compute_ccf <- function(.data, value1, value2, ...) {
  value1 <- enexpr(value1)
  value2 <- enexpr(value2)
  ccf <- ccf(x = eval_tidy(value1, data = .data), y = eval_tidy(value2, 
    data = .data), plot = FALSE, ...)
  lag <- as.numeric(ccf$lag)
  tibble(lag = lag, ccf = as.numeric(ccf$acf))
}

因此您可以将 CCF() 另存为新函数（使用新名称）并对其进行编辑以将 na.action = na.pass 添加到 ccf 调用中。

ccf_napass <- ccf(df$x, df$y, lag.max = 12, na.action = na.pass, plot = FALSE)
ccf_naomit <- ccf(df$x, df$y, lag.max = 12, na.action = na.omit, plot = FALSE)

CCF_feasts <- df %>% 
  tsibble::as_tsibble(index = date) %>%
  tsibble::fill_gaps() %>% 
  feasts::CCF(x, y, lag_max = 12) %>% 
  dplyr::pull(ccf)

data.frame(ccf_napass = ccf_napass$acf, ccf_naomit = ccf_naomit$acf, CCF_feasts)

     ccf_napass   ccf_naomit   CCF_feasts
1  -0.001339325 -0.010819818 -0.010819818
2   0.031219857  0.005546548  0.005546548
3   0.041227368 -0.005382620 -0.005382620
4  -0.010115336 -0.019916634 -0.019916634
5  -0.091096987 -0.022542801 -0.022542801
6  -0.169062715 -0.017967752 -0.017967752
7  -0.069318178 -0.023075644 -0.023075644
8  -0.007847907 -0.043090132 -0.043090132
9  -0.008233668 -0.064749235 -0.064749235
10 -0.018144372 -0.070301161 -0.070301161
11 -0.002914065 -0.054108236 -0.054108236
12  0.035133485  0.028140497  0.028140497
13  0.133520812  0.118747777  0.118747777
14 -0.011428760 -0.010055235 -0.010055235
15 -0.021989205 -0.019475560 -0.019475560
16 -0.289253595 -0.257125853 -0.257125853
17  0.122887924  0.109172907  0.109172907
18  0.186626990  0.165904850  0.165904850
19 -0.217882808 -0.193637291 -0.193637291
20  0.280088738  0.249009757  0.249009757
21  0.204005890  0.181525595  0.181525595
22  0.036459851  0.032663381  0.032663381
23  0.017956562  0.016224802  0.016224802
24  0.179025309  0.159451149  0.159451149
25  0.418716273  0.372680241  0.372680241

CCF vs ccf：有什么区别？

CCF vs ccf: What;s the Difference?

r

time-series

tidyverse

tsibble