Just look at the code:
> weighted.mean
function (x, w, na.rm = FALSE)
{
if (missing(w))
w <- rep.int(1, length(x))
if (is.integer(w))
w <- as.numeric(w)
if (na.rm) {
w <- w[i <- !is.na(x)]
x <- x[i]
}
sum(x * w)/sum(w)
}
<environment: namespace:stats>
So the differences are:
- missing values handling
- weight normalization
- the difference between t(x) %*% w and sum(x * w) (I'd say the latter is
more efficient)
Here's an example:
> x <- rnorm(5e6)
> w <- runif(x)
> w <- w / sum(w)
> system.time(sum(x * w), gcFirst=T)
[1] 0.17 0.03 0.20 NA NA> system.time(s1 <- sum(x * w), gcFirst=T)
[1] 0.19 0.01 0.20 NA NA> system.time(s2 <- t(x) %*% w, gcFirst=T)
[1] 0.30 0.01 0.33 NA NA> system.time(s3 <- crossprod(x, w), gcFirst=T)
[1] 0.04 0.00 0.04 NA NA> c(s1, s2, s3)
[1] -0.0008922782 -0.0008922782 -0.0008922782
[This is w/o using an optimized BLAS. With an optimized BLAS, the two
latter ones might be significantly faster than what's seen here.]
Andy
> From: Ranjan S. Muttiah
>
> What is the difference between the above two operations ?
>
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