[R] Variance-covariance matrix

[Pascal Oettli]

Hi Giorgio,

No need for a package. Please check function var (?var).

Regards,
Pascal


On Mon, May 11, 2015 at 3:17 PM, Giorgio Garziano
<giorgio.garziano at ericsson.com> wrote:
> Hi Tsjerk,
>
> Yes, I understand your point. Thanks for drawing my attention on that aspect.
>
> Let me then rephrase my question.
>
> I would need some R package function able to compute the variance-covariance matrix
> for multivariate series as defined at:
>
>         http://stattrek.com/matrix-algebra/covariance-matrix.aspx
>
>
> About what outlined in the book reference I mentioned, I shall open a separate thread
> in the case.
>
> Thanks.
>
> ---
>
> Giorgio
>
> Genoa, Italy
>
> From: Tsjerk Wassenaar [mailto:tsjerkw at gmail.com]
> Sent: domenica 10 maggio 2015 22:31
> To: Giorgio Garziano
> Cc: r-help at r-project.org
> Subject: Re: [R] Variance-covariance matrix
>
> Hi Giorgio,
>
> This is for a multivariate time series. x1 is variable 1 of the observation vector x, x2, variable 2, etc. If you need x(i) and x(i+1), etc, then you're looking for the autocovariance/autocorrelation matrix, which is a quite different thing (and David showed the way). You can easily see that you don't have N-1 degrees of freedom per entry, because you have fewer 'observations' for larger lag times.
>
> Cheers,
>
> Tsjerk
>
>
>
> On Sun, May 10, 2015 at 10:25 PM, Giorgio Garziano <giorgio.garziano at ericsson.com<mailto:giorgio.garziano at ericsson.com>> wrote:
> Hi Tsjerk,
>
> Yes, seriously.
>
> Time series:
>
> X = [x1, x2, x3, ....,xn]
>
> The variance-covariance matrix is V matrix:
>
>             V    =
>
>
> Σ x12 / (N-1)
>
> Σ x1 x2 / (N-1)
>
> . . .
>
> Σ x1 xn / (N-1)
>
> Σ x2 x1 / (N-1)
>
> Σ x22 / (N-1)
>
> . . .
>
> Σ x2 xn / (N-1)
>
> . . .
>
> . . .
>
> . . .
>
> . . .
>
> Σ xn x1 / (N-1)
>
> Σ xn x2 / (N-1)
>
> . . .
>
> Σ xn2 / (N-1)
>
>
>
>
> Reference: “Time series and its applications – with R examples”, Springer,
>      $7.8 “Principal Components” pag. 468, 469
>
> Cheers,
>
> Giorgio
>
>
> From: Tsjerk Wassenaar [mailto:tsjerkw at gmail.com<mailto:tsjerkw at gmail.com>]
> Sent: domenica 10 maggio 2015 22:11
>
> To: Giorgio Garziano
> Cc: r-help at r-project.org<mailto:r-help at r-project.org>
> Subject: Re: [R] Variance-covariance matrix
>
> Hi Giorgio,
>
> For a univariate time series? Seriously?
>
> data <- rnorm(10,2,1)
> as.matrix(var(data))
>
> Cheers,
>
> Tsjerk
>
>
> On Sun, May 10, 2015 at 9:54 PM, Giorgio Garziano <giorgio.garziano at ericsson.com<mailto:giorgio.garziano at ericsson.com>> wrote:
> Hi,
>
> Actually as variance-covariance matrix I mean:
>
>         http://stattrek.com/matrix-algebra/covariance-matrix.aspx
>
> that I compute by:
>
>         data <- rnorm(10,2,1)
>         n <- length(data)
>         data.center <- scale(data, center=TRUE, scale=FALSE)
>         var.cov.mat <- (1/(n-1)) * data.center %*% t(data.center)
>
> --
> Giorgio Garziano
>
>
> -----Original Message-----
> From: David Winsemius [mailto:dwinsemius at comcast.net<mailto:dwinsemius at comcast.net>]
> Sent: domenica 10 maggio 2015 21:27
> To: Giorgio Garziano
> Cc: r-help at r-project.org<mailto:r-help at r-project.org>
> Subject: Re: [R] Variance-covariance matrix
>
>
> On May 10, 2015, at 4:27 AM, Giorgio Garziano wrote:
>
>> Hi,
>>
>> I am looking for a R package providing with variance-covariance matrix computation of univariate time series.
>>
>> Please, any suggestions ?
>
> If you mean the auto-correlation function, then the stats package (loaded by default at startup) has facilities:
>
> ?acf
> # also same help page describes partial auto-correlation function
> #Auto- and Cross- Covariance and -Correlation Function Estimation
>
> --
>
> David Winsemius
> Alameda, CA, USA
>
> ______________________________________________
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>
>
>
> --
> Tsjerk A. Wassenaar, Ph.D.
>
>
>
> --
> Tsjerk A. Wassenaar, Ph.D.
>
>         [[alternative HTML version deleted]]
>
> ______________________________________________
> R-help at r-project.org mailing list -- To UNSUBSCRIBE and more, see
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> and provide commented, minimal, self-contained, reproducible code.



-- 
Pascal Oettli
Project Scientist
JAMSTEC
Yokohama, Japan