Displaying 2 results from an estimated 2 matches for "mywinfun".
2005 Apr 02
1
Survey of "moving window" statistical functions - still looking f or fast mad function
...e previous cell y[1] =
sum(x[1:k]); # find the first sum y = cumsum(y) # apply precomputed
differences return(y/k) # return mean not sum }
2. filter(x, rep(1/k,k), sides=2, circular=T) - (stats package)
3. kernapply(x, kernel("daniell", m), circular=T)
4. apply(embed(x,k), 1, mean)
5. mywinfun <- function(x, k, FUN=mean, ...) { # suggested in news
group n <- length(x) A <- rep(x, length=k*(n+1)) dim(A) <- c(n+1, k)
sapply(split(A, row(A)), FUN, ...)[1:(n-k+1)] }
6. rollFun(x, k, FUN=mean) - (fSeries package)
7. rollMean(x, k) - (fSeries package)
8. SimpleMeanLoop = functio...
2004 Oct 08
1
Survey of "moving window" statistical functions - still looking f or fast mad function
...= cumsum(y) # apply precomputed
differences
return(y/k) # return mean not sum
}
2. filter(x, rep(1/k,k), sides=2, circular=T) - (stats package)
3. kernapply(x, kernel("daniell", m), circular=T)
4. apply(embed(x,k), 1, mean)
5. mywinfun <- function(x, k, FUN=mean, ...)
{ # suggested in news group
n <- length(x)
A <- rep(x, length=k*(n+1))
dim(A) <- c(n+1, k)
sapply(split(A, row(A)), FUN, ...)[1:(n-k+1)]
}
6. rollFun(x, k, FUN=mean) - (fSeries package)
7. rollMean(x, k) - (fSeries package)...