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

[Paul Thompson]

Dear rcpp-dlevel members,

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.


Paul
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