similar to: How to compute Rolling analysis of Standard Deviation using ZOO package?

Guys: 1).When I using the 'quantile' function, I get really confused. Here is what I met: > x<-zoo(rnorm(500,0,1)) > quantile(x,0.8) 400 1.060258 > c=rnorm(500,0,1) > quantile(c,0.8) 80% 0.9986075 why do the results display different? Is that because of the different type of the class? 2).And I want to use the 'rollapply' function to compute a

I'd like to use vectorization to take a 4 point moving window on standard deviation on the close column and create another variable (st.dev) in the dataframe. Here's the dataframe head(xyz) Date Close 1 2011-01-28 56.42 2 2011-01-27 57.37 3 2011-01-26 56.48 4 2011-01-25 56.39 5 2011-01-24 55.74 6 2011-01-21 55.46 So the first 3 elements to the new st.dev column would be zero

Hi again, I am wondering there is any function for 'zoo' time series, where I can apply a user defined function rolling window basis, wherein window size is ever increasing i.e. not fixed. For example, let say I have below user defined function and a zoo time series : > library(zoo) > UDF = function(x) sum(x) > TS = zoo(rnorm(10), seq(as.Date('2017-01-01'),

Hi. I'm comparing output from rollapply.zoo, as produced by two versions of R and package zoo. I'm illustrating with an example from a R-help posting 'Zoo - bug ???' dated 2010-07-13. My question is not about the first version, or the questions raised in that posting, because the behaviour is as documented. I'm puzzled as to why na.rm no longer is passed to mean, i.e. why

Hi folks, I am confused whether the following is a bug or it is fine Here is the explanation a <- zoo(c(NA,1:9),1:10) Now If I do rollapply(a,FUN=mean,width=3,align="right") I get > rollapply(a,FUN=mean,width=3,align="right") 3 4 5 6 7 8 9 10 NA NA NA NA NA NA NA NA But I shouldn't be getting NA right ? i.e for index 10 I should get (1/3)*(9+8+7)

Use a `width` of integer index locations. And you likely want = "right" (or rollapplyr(), as I used). R> set.seed(21) R> x <- rnorm(10) R> rs <- rollapplyr(x, seq_along(x), sum) R> cs <- cumsum(x) R> identical(rs, cs) [1] TRUE On Thu, Aug 10, 2017 at 1:28 PM, Christofer Bogaso <bogaso.christofer at gmail.com> wrote: > Hi again, > > I am

# Hi there, # I am trying to apply a function over a moving-window for a large number of multivariate time-series that are grouped in a nested set of factors. I have spent a few days searching for solutions with no luck, so any suggestions are much appreciated. # The data I have are for the abundance dynamics of multiple species observed in multiple fixed plots at multiple sites. (I total I

Dear Colleagues, I have faced with the problem that function rollaply with rolling window for calculation of volatility doesn't give the all results of calculations. I have run the rolling window for calculation in Excel and obtained that the number of outputs for Excel is 36 and for R is 18. The total number of observations is 37. In the attachment you can find pdf of the Excel and Excel

Hi Joshua, thanks for your prompt reply. However as I said, sum() function I used here just for demonstrating the problem, I have other custom function to implement, not necessarily sum() I am looking for a generic solution for above problem. Any better idea? Thanks, On Fri, Aug 11, 2017 at 12:04 AM, Joshua Ulrich <josh.m.ulrich at gmail.com> wrote: > Use a `width` of integer index

Replace "sum" with your custom function's name. I don't see any reason why that wouldn't work, and the problem with my solution is not clear in your response. r <- rollapplyr(x, seq_along(x), yourCustomFunctionGoesHere) On Thu, Aug 10, 2017 at 1:39 PM, Christofer Bogaso <bogaso.christofer at gmail.com> wrote: > Hi Joshua, thanks for your prompt reply. However

Here is a relatively simple script (with comments as to the logic interspersed): # Some of these libraries are probably not needed here, but leaving them in place harms nothing: library(tseries) library(xts) library(quantmod) library(fGarch) library(fTrading) library(ggplot2) # Set the working directory, where the data file is located, and read the raw data

zoo's rollapply() function appears to be extremely useful for plugging in a function on-the-fly to run over a window. With inline, there is a lot more coding and room for error, and the code is less portable because the user has to have R compiling set up or it won't work. However, rollapply() seems to be really slow. Several orders of magnitude slower than inline, in fact. I don't

library(zoo) library(chron) t1 <- chron("1/1/2006", "00:00:00") t2 <- chron("1/31/2006", "23:45:00") deltat <- times("00:15:00") tt <- seq(t1, t2, by = times("00:15:00")) d <- sample(33:700, 2976, replace=TRUE) sin.zoo <- zoo(d,tt) #there are ninety six reading in a day d.max <- rollapply(sin.zoo, width=96, FUN=max)

Good morning, I have discovered what I believe to be a performance regression between Zoo 1.6x and Zoo 1.7-6 in the application of rollapply. On zoo 1.6x, rollapply of my function over my data takes about 20 minutes. Using 1.7-6, the same code takes about 6 hours. R --version R version 2.13.1 (2011-07-08) Copyright (C) 2011 The R Foundation for Statistical Computing ISBN 3-900051-07-0 Platform:

Dear R users. I have zoo object "size_june" containing market-capital values: > dim(size_june) # market-cap data of 625 firms for 20 years [1] 20 625 > class(size_june) [1] "zoo" > size_june # colnames = "size.firmcode" size.34020 size.4710 size.11050 size.10660 size.9540 size.8060 size.16160 size.8080 size.9280 1988-06-30 NA

Hi, First, let me thank Jaroslaw for making this survey. I find it quite illuminating. Now the questions: * the #1 solution below (based on cumsum) is numerically unstable. Specifically if you do the runmean on a positive vector you can easily get negative numbers due to rounding errors. Does anyone see a modification which is free of this deficiency? * is it possible to optimize the

Dear all, I have spent the last few days on a seemingly simple and previously documented rolling regression. I have a 60 year data set organized in a ts matrix. The matrix has 5 columns; cash_ret, epy1, ism1, spread1, unemp1 I have been able to come up with the following based on previous help threads. It seems to work fine. The trouble is I get regression coefficients but need the immediate

Try this: > ?convolve > x<-rnorm(1000) > y<-rep(1,20) > z<-convolve(x,y,type="filter") > plot(x,type="l") > str(z) num [1:981] 6.31 7.28 8.16 7.39 4.65 ... > lines(c(rep(0,10),z,rep(0,10)),col="yellow",lwd=3) > lines(c(rep(0,10),z,rep(0,10))/length(y),col="red",lwd=3) #running mean You wrote: Does anyone know an easy way

Hi I would like to understand how to extend the function (FUN) I am using in rollapply below. ###################################### With the following simplified data, test1 yields parameters for a rolling regression data = data.frame(Xvar=c(70.67,70.54,69.87,69.51,70.69,72.66,72.65,73.36), Yvar =c(78.01,77.07,77.35,76.72,77.49,78.70,77.78,79.58)) data.z = zoo(d) test1 =

Hi everyone, I am trying to apply linear regression to adjacent columns in a matrix (i.e. col1~col2; col3~col4; etc.). The columns in my matrix come with identifiers at the top of each column, but when I try to use these identifiers to reference the columns in the regression function using rollapply(), the columns are not recognised and the regression breaks down. Is there a more robust way to