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...(e.g., k ~ 225 for n ~ 4500, where n is time series length). This ignores occasional low- side outliers, and, after baseline subtraction, I can re-adjust any negative values to zero. But runquantile's computation time proves exceedingly long for my large datasets, particularly if I set the endrule parameter to 'func'. Here is what caTools author Jarek Tuszynski says about relative speeds of various running-window functions: - runmin, runmax, runmean run at O(n) - runmean(..., alg="exact") can have worst case speed of O(n^2) for some small data vectors, but a...

Hello I have a 3.5 million elements numeric vector x. I'm trying to calculate the rolling std dev of the previous 144 elements. rsd144<-runsd(x, 144, center=0, endrule="NA") this crashes R (ie on the console disappears and the Rgui.exe process is not there anymore) with smaller vectors, the crash does not occur. regards, [[alternative HTML version deleted]]

...        elements[i]<-length(i:(i + windowSize - 1))       }   }   return (list(result=out , numberOfElements=elements, windowSize=windowSize )) } do_sliding_for_a_window_duty_cycle_alternative <- function(DataToAnalyse, windowSize) {     result= runmean(rowMeans(DataToAnalyse),windowSize,endrule="trim",alg="C")     return( list(result= result, windowSize=windowSize)) } DataToAnalyse<-matrix(data=round(seq(1:100000000)),nrow=10000,byrow=TRUE) # How much time they need to runmean system.time(a<-do_sliding_for_a_window_duty_cycle_alternative(DataToAnalyse,5...

...)] <- > sin(4*pi*x/length(x)) + rnorm(length(x)) > y2[seq(2,length(x2),2)] <- runif(length(x),min=-5,max=5) > #=============================================================== > > #=robust & smooth fit=========================================== > y3 <- runmed(y2,51,endrule="median") #first round of running > median y4 <- smooth.spline(x2,y3,df=10) #second round of > smoothing splines > #=============================================================== > > #=ploting data================================================== > plot(x2,y2,p...