[R] C/C++/Fortran Rolling Window Regressions
jeremiah rounds
roundsjeremiah at gmail.com
Thu Jul 21 23:43:54 CEST 2016
I agree that when appropriate Kalman Filter/Smoothing the higher-quality
way to go about estimating a time-varying coefficient (given that is what
they do), and I have noted that both the R package "dlm" and the function
"StructTS" handle these problems quickly. I am working on that in
parallel.
One of the things I am unsure about with Kalman Filters is how to estimate
variance parameters when the process is unusual in some way that isn't in
the model and it is not feasible to adjust the model by-hand. dlm's dlmMLE
seems to produce non-sense (not because of the author's work but because of
assumptions). At least with moving window regressions after the unusual
event is past your window the influence of that event is gone. That
isn't really a question for this group it is more about me reading more.
When I get that "how to handle all the strange things big data throws at
you" worked out for Kalman Filters, I will go back to those because I
certainly like what I see when everything is right. There is a plethora of
related topics right? Bayesian Model Averaging, G-ARCH models for
heteroscedasticity, etc.
Anyway... roll::roll_lm, cheers!
Thanks,
Jeremiah
On Thu, Jul 21, 2016 at 2:08 PM, Mark Leeds <markleeds2 at gmail.com> wrote:
> Hi Jermiah: another possibly faster way would be to use a kalman filtering
> framework. I forget the details but duncan and horne have a paper which
> shows how a regression can be re-computed each time a new data point is
> added .I
> forget if they handle taking one off of the back also which is what you
> need.
>
> The paper at the link below isn't the paper I'm talking about but it's
> reference[1] in that paper. Note that this suggestion might not be a better
> approach than the various approaches already suggested so I wouldn't go
> this route unless you're very interested.
>
>
> Mark
>
> https://www.le.ac.uk/users/dsgp1/COURSES/MESOMET/ECMETXT/recurse.pdf
>
>
>
>
>
>
> On Thu, Jul 21, 2016 at 4:28 PM, Gabor Grothendieck <
> ggrothendieck at gmail.com> wrote:
>
>> I would be careful about making assumptions regarding what is faster.
>> Performance tends to be nonintuitive.
>>
>> When I ran rollapply/lm, rollapply/fastLm and roll_lm on the example
>> you provided rollapply/fastLm was three times faster than roll_lm. Of
>> course this could change with data of different dimensions but it
>> would be worthwhile to do actual benchmarks before making assumptions.
>>
>> I also noticed that roll_lm did not give the same coefficients as the
>> other two.
>>
>> set.seed(1)
>> library(zoo)
>> library(RcppArmadillo)
>> library(roll)
>> z <- zoo(matrix(rnorm(10), ncol = 2))
>> colnames(z) <- c("y", "x")
>>
>> ## rolling regression of width 4
>> library(rbenchmark)
>> benchmark(fastLm = rollapplyr(z, width = 4,
>> function(x) coef(fastLm(cbind(1, x[, 2]), x[, 1])),
>> by.column = FALSE),
>> lm = rollapplyr(z, width = 4,
>> function(x) coef(lm(y ~ x, data = as.data.frame(x))),
>> by.column = FALSE),
>> roll_lm = roll_lm(coredata(z[, 1, drop = F]), coredata(z[, 2, drop =
>> F]), 4,
>> center = FALSE))[1:4]
>>
>>
>> test replications elapsed relative
>> 1 fastLm 100 0.22 1.000
>> 2 lm 100 0.72 3.273
>> 3 roll_lm 100 0.64 2.909
>>
>> On Thu, Jul 21, 2016 at 3:45 PM, jeremiah rounds
>> <roundsjeremiah at gmail.com> wrote:
>> > Thanks all. roll::roll_lm was essentially what I wanted. I think
>> maybe
>> > I would prefer it to have options to return a few more things, but it is
>> > the coefficients, and the remaining statistics you might want can be
>> > calculated fast enough from there.
>> >
>> >
>> > On Thu, Jul 21, 2016 at 12:36 PM, Achim Zeileis <
>> Achim.Zeileis at uibk.ac.at>
>> > wrote:
>> >
>> >> Jeremiah,
>> >>
>> >> for this purpose there are the "roll" and "RcppRoll" packages. Both use
>> >> Rcpp and the former also provides rolling lm models. The latter has a
>> >> generic interface that let's you define your own function.
>> >>
>> >> One thing to pay attention to, though, is the numerical reliability.
>> >> Especially on large time series with relatively short windows there is
>> a
>> >> good chance of encountering numerically challenging situations. The QR
>> >> decomposition used by lm is fairly robust while other more
>> straightforward
>> >> matrix multiplications may not be. This should be kept in mind when
>> writing
>> >> your own Rcpp code for plugging it into RcppRoll.
>> >>
>> >> But I haven't check what the roll package does and how reliable that
>> is...
>> >>
>> >> hth,
>> >> Z
>> >>
>> >>
>> >> On Thu, 21 Jul 2016, jeremiah rounds wrote:
>> >>
>> >> Hi,
>> >>>
>> >>> A not unusual task is performing a multiple regression in a rolling
>> window
>> >>> on a time-series. A standard piece of advice for doing in R is
>> >>> something
>> >>> like the code that follows at the end of the email. I am currently
>> using
>> >>> an "embed" variant of that code and that piece of advice is out there
>> too.
>> >>>
>> >>> But, it occurs to me that for such an easily specified matrix
>> operation
>> >>> standard R code is really slow. rollapply constantly returns to R
>> >>> interpreter at each window step for a new lm. All lm is at its
>> heart is
>> >>> (X^t X)^(-1) * Xy, and if you think about doing that with Rcpp in
>> rolling
>> >>> window you are just incrementing a counter and peeling off rows (or
>> >>> columns
>> >>> of X and y) of a particular window size, and following that up with
>> some
>> >>> matrix multiplication in a loop. The psuedo-code for that Rcpp
>> >>> practically writes itself and you might want a wrapper of something
>> like:
>> >>> rolling_lm (y=y, x=x, width=4).
>> >>>
>> >>> My question is this: has any of the thousands of R packages out there
>> >>> published anything like that. Rolling window multiple regressions
>> that
>> >>> stay in C/C++ until the rolling window completes? No sense and
>> writing it
>> >>> if it exist.
>> >>>
>> >>>
>> >>> Thanks,
>> >>> Jeremiah
>> >>>
>> >>> Standard (slow) advice for "rolling window regression" follows:
>> >>>
>> >>>
>> >>> set.seed(1)
>> >>> z <- zoo(matrix(rnorm(10), ncol = 2))
>> >>> colnames(z) <- c("y", "x")
>> >>>
>> >>> ## rolling regression of width 4
>> >>> rollapply(z, width = 4,
>> >>> function(x) coef(lm(y ~ x, data = as.data.frame(x))),
>> >>> by.column = FALSE, align = "right")
>> >>>
>> >>> ## result is identical to
>> >>> coef(lm(y ~ x, data = z[1:4,]))
>> >>> coef(lm(y ~ x, data = z[2:5,]))
>> >>>
>> >>> [[alternative HTML version deleted]]
>> >>>
>> >>> ______________________________________________
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>> >>> 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.
>> >>>
>> >>>
>> >
>> > [[alternative HTML version deleted]]
>> >
>> > ______________________________________________
>> > R-help at r-project.org mailing list -- To UNSUBSCRIBE and more, see
>> > 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.
>>
>>
>>
>> --
>> Statistics & Software Consulting
>> GKX Group, GKX Associates Inc.
>> tel: 1-877-GKX-GROUP
>> email: ggrothendieck at gmail.com
>>
>> ______________________________________________
>> R-help at r-project.org mailing list -- To UNSUBSCRIBE and more, see
>> 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.
>>
>
>
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