[Rcpp-devel] Speeding up nlme::gls with temporal autocorrelation

Dirk Eddelbuettel edd at debian.org
Thu Oct 24 16:16:49 CEST 2019

On 23 October 2019 at 13:34, Paul Thompson wrote:
| Hopefully, you may be able to shed some light on a problem that I have
| regarding ‘speeding up’ the R function nlme::gls which I use for fitting
| models with temporal autocorrelation. In a vignette by Doug Bates and Dirk
| Eddelbuettel (
| https://cran.r-project.org/web/packages/RcppEigen/vignettes/RcppEigen-Introduction.pdf),
| I found that they used RcppEigen to solve some least-squares problems, but
| I wasn’t sure if this could be extended to generalised least squares?
| The current function nlme::gls takes hours to execute and I was hoping to
| use the Rcpp framework to ameliorate this problem, but I have little
| experience in Rcpp or C++ programming and wasn’t sure if this had already
| been tackled. After extensive searching on the web, I haven’t found any
| implementation. Does anyone have any ideas or advice please?
| My model syntax in R is currently as follows:
| myfit <- gls(y~stim1+stim2+t+I(t^2)+I(t^3)+signal+stim1_signal,data=mydata,
| correlation=corAR1(form=~t))
| Some other information, each time series has approximately 20,000
| observations.
| Thank you in advance for any help.

I don't know those models well. So I can only suggest to profile and
measure.  And to look very closely at e.g. examples/FastLM/ in Rcpp (and the
various fastLm() functions in three Rcpp* packages).  Calling to have the
formula in y ~ .... resolved WAY dominates the computation in the fastLm case
(so if in a hurry, ALWAYS run with a matrix and a vector via the alternate
interfaces).  It may be different here.

But it is unlikely that there is a 'free lunch' anywhere. R is a few decades
old, and workhorse functions like that one have been reviewed and reviewwed
and reviewed ...


http://dirk.eddelbuettel.com | @eddelbuettel | edd at debian.org

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