[R] ccf and covariance

[Bob Farmer]

Thanks to Prof. Ripley and Phil Spector for pointing out that the
autocorrelation functions must use a "nontraditional" definition of
the covariance, involving a denominator of n (instead of n-1) in order
to satisfy an assumption of second-order stationarity in the
(unbiased) covariance estimators of a time series.

In terms of getting at the source code for the (apparently compiled)
"R_acf", however, I've had no luck.  While
https://svn.r-project.org/R/trunk
seems to be able to show me the source code for otherwise obscured (in
the R console) functions like print()
(e.g. https://svn.r-project.org/R/trunk/src/library/base/R/print.R ),
I can't seem to find the C code ("R_acf"?) called in this section:
....
array(.C(R_acf, as.double(x), as.integer(sampleT),
        as.integer(nser), as.integer(lag.max), as.integer(type ==
            "correlation"), acf = double((lag.max + 1) * nser *
            nser), NAOK = TRUE)
....

of acf().
For instance, in https://svn.r-project.org/R/trunk/src/library/stats/src/
there is (seemingly) no "R_acf.C" or "stats.C" file that I would expect to see.
I apologize in advance if this question is elementary or naive -- this
is my first time dealing with the source code.

Thanks again.
--Bob Farmer

On Wed, Apr 23, 2008 at 3:31 PM, Prof Brian Ripley
<ripley at stats.ox.ac.uk> wrote:
> On Wed, 23 Apr 2008, Bob Farmer wrote:
>
>
> > Hi.
> > It's my understanding that a cross-correlation function of vectors x
> > and y at lag zero is equivalent to their correlation (or covariance,
> > depending on how the ccf is defined).
> >
>
>  The ratio of your values is
>
>
> > MASS::fractions(282568.5/259021)
> >
>  [1] 12/11
>
>  ?  Do you recognize it?
>
>  There is an explanation in MASS4, p. 390, for example.
>
>
>
> > If this is true, could somebody please explain why I get an
> > inconsistent result between cov() and ccf(type = "covariance"), but a
> > consistent result between cor() and ccf(type = "correlation")?
> > Or have I misunderstood what is a cross-correlation?
> > (unfortunately, I can't seem to get a look at the ccf code, since I
> > think it's buried in some C function outside of the main environment)
> >
>
>  It is in the R sources, not 'buried' at all - that is what 'Open Source'
> means. You can browse them at https://svn.r-project.org/R/trunk, or download
> them for study.
>
>
>
> >
> > Thanks very much.
> > --Bob Farmer
> > PhD candidate, Dalhousie University
> > Halifax, NS, Canada
> >
> > Example:
> > d1<-data.frame(matrix(ldeaths, nrow = 6, byrow = T))
> > seventy_4<-as.numeric(d1[1,])
> > seventy_5<-as.numeric(d1[2,])
> >
> > ccf(x=seventy_4, y=seventy_5,
> >  plot = F, lag.max = 0, type = "covariance"
> > )
> > cov(seventy_4, seventy_5)  #inconsistent
> >
> > ccf(x=seventy_4, y=seventy_5,
> >  plot = F, lag.max = 0, type = "correlation"
> > )
> > cor(seventy_4, seventy_5)  #consistent
> >
> >
> > ______________________________________________
> > R-help at r-project.org mailing list
> > https://stat.ethz.ch/mailman/listinfo/r-help
> > PLEASE do read the posting guide
> http://www.R-project.org/posting-guide.html
> > and provide commented, minimal, self-contained, reproducible code.
> >
> >
>
>  --
>  Brian D. Ripley,                  ripley at stats.ox.ac.uk
>  Professor of Applied Statistics,  http://www.stats.ox.ac.uk/~ripley/
>  University of Oxford,             Tel:  +44 1865 272861 (self)
>  1 South Parks Road,                     +44 1865 272866 (PA)
>  Oxford OX1 3TG, UK                Fax:  +44 1865 272595
>