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.zeil...@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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>>
>>
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