However, I wanted to code my own … My basic VAR looks like this: VAR(dat.bv.1, p = 2, type = "const", season = NULL, exog = NULL) While dat.bv.1 is a time series object containing my endogeneous variables. Googling around soon brought me to ‘rollapply’, which when I tested it seems to be a very versatile function. The key differences here are in how “alignment”, completeness, and steps are assigned. I get the feeling that if a data.table::rollapply function was written utilizing data.table's radix sorting, it could be much faster than anything that is available right now. .before and .after are how left/right alignment are specified and a combination of both … I have found examples using rollapply to calculate rolling window linear regressions, but I have the added complication that I would like to apply these linear regressions to groups within the data set. Alternatively, width can be a list regarded as offsets compared to the current time, see below for details. Anyway let’s see an example. Meaning I'd like to share it with the world so hopefully if someone is looking for the same answers in the future, they can find it more easily than I did. Both zoo and TTR have a number of “roll” and “run” functions, respectively, that are integrated with tidyquant. Hi everyone, I'm making this post because I have spent the last 2-3 hours trying to find or figure out how the heck to do this, and I finally figured it out! data: the data to be used (representing a series of observations). And similar to zoo’s rollapply function I will be specifying function and window. Googling around soon brought me to 'rollapply', which when I tested it seems to be a very versatile function. I know that you can use xts::rollapply function inside of data.table, however, xts::rollapply is still slow. If you haven’t checked out the previous post on period apply functions, you may want to review it to get up to speed. $\begingroup$ I think the reason that you have a problem is that this rollapply idea doesn't make sense. If it's simple statistics you're interested in, you could check out some of the functions in the zoo package. In the second part in a series on Tidy Time Series Analysis, we’ll again use tidyquant to investigate CRAN downloads this time focusing on Rolling Functions. > Hello, > I am fairly new to R and trying to calculate value at risk with exponentially decreasing weights.My function works for a single vector of returns but does not work with rollapply(), which is what I want to use. For some cryptic reason I needed a function that calculates function values on sliding windows of a vector. $\endgroup$ – Michael R. Chernick Aug 15 '12 at 20:38 I want to use rollapply() to get the rolling window coefficients of my vector autoregression VAR() with two variables. width: numeric vector or list. The function I am working on should assig exponentially decreasing weights to the K most recent returns and then order the returns in an ascending order. In the second part in a series on Tidy Time Series Analysis, we’ll again use tidyquant to investigate CRAN downloads this time focusing on Rolling Functions.If you haven’t checked out the previous post on period apply functions, you may want to review it to get up to speed.Both zoo and TTR have a number of “roll” and “run” functions, respectively, that are integrated with tidyquant. In the simplest case this is an integer specifying the window width (in numbers of observations) which is aligned to the original sample according to the align argument. Didier Ruedin’s blog has an elegant solution using the f ilter() function, but for this piece we will look at the rollapply() function in the zoo package. It has rollapply(), which takes an analogous approach to rollify but uses apply instead (so maybe not a big performance increase), and rollmean(), which is a performance-optimised rolling mean. rollify uses purrr under the hood, so I can't imagine it's going to be super performant. However, I wanted to code my own … I have no solution other than to look for something different that may achieve your goals (not sure what they are) and is sensible. To the best of my knowledge the R base package does not have a function to calculate moving averages. For some cryptic reason I needed a function that calculates function values on sliding windows of a vector. .Before and.after are how left/right alignment are specified and a combination of both … Anyway let ’ see. 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