Displaying 4 results from an estimated 4 matches for "endrule".
2007 Feb 20
1
baseline fitters
...(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 av...
2011 Jan 29
1
runsd {caTools} crashes R 64bit on winxp64bit with a very large vector
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]]
2012 Mar 03
1
Sliding Window in R (solved)
... 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,50...
2006 Mar 16
1
running median and smoothing splines for robust surface f itting
...)] <-
> 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,pc...