# “Manual” calculation of ACF of a time series in R - close but not quite

I'm trying to understand the "mechanism" behind the calculation of ACF values in a time series. As a "prove-it-to-myself exercise" [NOTE: I updated the code in this link to reflect the information in the accepted answer], I am not focusing on the elegance of the code, and I use loops on purpose. The problem is that, although the values I get are close to those generated by acf(), they are not equal, and at some points, not all that close. Is there any conceptual misunderstanding?

As credited on the online notes linked above, the data was generated following this online example.

set.seed(0)                          # To reproduce results
x = seq(pi, 10 * pi, 0.1)            # Creating time-series as sin function.
y = 0.1 * x + sin(x) + rnorm(x)      # Time-series sinusoidal points with noise.
y = ts(y, start = 1800)              # Labeling time axis.
model = lm(y ~ I(1801:2083))         # Detrending (0.1 * x)
st.y = y - predict(model)            # Final de-trended ts (st.y)
ACF = 0                              # Empty vector to capture the auto-correlations.
ACF = cor(st.y, st.y)             # The first entry in the ACF is the cor with itself.
for(i in 1:24){                      # Took 24 points to parallel the output of acf()
lag = st.y[-c(1:i)]                # Introducing lags in the stationary ts.
clipped.y = st.y[1:length(lag)]    # Compensating by reducing length of ts.
ACF[i + 1] = cor(clipped.y, lag)   # Storing each correlation.
}
w = acf(st.y)                        # Extracting values of acf without plotting.
all.equal(ACF, as.vector(w$acf)) # Checking for equality in values. # Pretty close to manual calc: Mean relative difference: 0.03611463"  To get a tangible sense of the relative outputs, this is what the autocorrelations look like calculated manually: 1.0000000 0.3195564 0.3345448 0.2877745 0.2783382 0.2949996 ... ... -0.1262182 -0.1795683 -0.1941921 -0.1352814 -0.2153883 -0.3423663  as opposed to the R acf() function: 1.0000000 0.3187104 0.3329545 0.2857004 0.2745302 0.2907426 ... ... -0.1144625 -0.1621018 -0.1737770 -0.1203673 -0.1924761 -0.3069342  As suggested, this is likely explained comparing to the code in the call within acf() in the line: acf <- .Call(C_acf, x, lag.max, type == "correlation") How can this C_acf function be accessed? I am having similar issues with the PACF, which I presume are related. The code for the PACF values is here. [NOTE: In this case I suspect it is actually a rounding difference]. • Have you taken a look at the C code that R uses to compute the ACF? – Jon Jan 2 '17 at 20:47 • @Jon I looked at the R function, and saw that ultimately it refers the data to the C_acf procedure (?), and looked it up online without success. I am completely unfamiliar with C. – Antoni Parellada Jan 2 '17 at 20:50 • – Scortchi Jan 2 '17 at 22:50 • I downloaded the most recent R source code tarball (R-3.3.2.tar.gz) and searched for "C_acf" but found nothing. I found the acf.R file, which uses C_acf, but not the actual C file. I did however find the pacf C file. This is quite strange. – Jon Jan 3 '17 at 0:01 • @Jon Thanks for looking into it. It seems as though someone has to know the answer; it never helps when a question entails sifting through a chunk of code, like in my OP. It's possible that a more straight to the point question, possibly in SO, could do the trick. – Antoni Parellada Jan 3 '17 at 0:39 ## 1 Answer Apparently cor and acf are not using the same divisor. acf uses as divisor the number of observations and can be reproduced as shown below. For details, you can locate the code of C_acf in the file R-3.3.2/src/library/stats/src/filter.c (procedures acf and acf0): set.seed(0) # To reproduce results x = seq(pi, 10 * pi, 0.1) # Creating time-series as sin function. y = 0.1 * x + sin(x) + rnorm(x) # Time-series sinusoidal points with noise. y = ts(y, start = 1800) # Labeling time axis. model = lm(y ~ I(1801:2083)) # Detrending (0.1 * x) st.y = y - predict(model) lag.max <- 24 ydm <- st.y - mean(st.y) n <- length(ydm) ACF <- rep(NA, lag.max+1) for (tau in seq(0, lag.max)) ACF[tau+1] <- sum(ydm * lag(ydm, -tau)) / n ACF <- ACF/ACF names(ACF) <- paste0("lag", seq.int(0, lag.max)) ACF head(ACF) # lag0 lag1 lag2 lag3 lag4 lag5 # 1.0000000 0.3187104 0.3329545 0.2857004 0.2745302 0.2907426 tail(ACF) # lag19 lag20 lag21 lag22 lag23 lag24 # -0.1144625 -0.1621018 -0.1737770 -0.1203673 -0.1924761 -0.3069342 all.equal(ACF, acf(st.y, lag.max=lag.max, plot=FALSE)$acf[,,1], check.names=FALSE)
#  TRUE

• Thank you. I think you answer the question, and plan on accepting the answer after going over the code with a debugger. I searched online for the file you post, and found it on a fossies.org page. The code there looks rather different - Did you adapt it to R yourself? Is there any other place I should be searching for it? In general, what is the procedure to find these C or Fortran functions? – Antoni Parellada Jan 3 '17 at 13:10
• I didn't follow the C source code. I simply tried to do a a concise and illuminating implementation for illustration of the operations involved. In practice, it is better to use acf, which will for example deal with missing observations. – javlacalle Jan 3 '17 at 13:30
• Very nice! Can you please add some context to the file you quote? I'll accept your answer anyway, but it would be nice to have a bit of an explanation as to how to resolve these situations where R points to some function in C or Fortran. – Antoni Parellada Jan 3 '17 at 13:33
• The C or Fortran code is located in the directory src of the source packages. The package a function belongs to can be found in the header of the documentation (left-hand-side in brackets). In order to find the function in the directory src, it is helpful to use some searching tool such as grep. – javlacalle Jan 3 '17 at 13:33
• Do you access src from within RStudio? I use Windows, and was became acquainted with grep yesterday reading about it on SO, but I couldn't figure it out... – Antoni Parellada Jan 3 '17 at 13:35