I´m also looking for an answer on this question right now.
You can´t use a weight in the moments package, but i found a
weighted.moments()-function in the acid-package ( weighted.moments-function
<https://github.com/cran/acid/blob/master/R/weighted.moments.R> ). If your
data has NA, you can do the following:
#-----------------------------------------------------------------
skew <- function(x,weight){
weight<-weight[!is.na(x)] #delete weight for cases with NA
x<-x[!is.na(x)] # delete NA
acid::weighted.moments(x, w8=weight) #calulate moments
}
skew(mydata$var,weight)
#-----------------------------------------------------------------
I also tried to write a weighted-skew-function by myself:
The result is different from the acid-package: i get a skew of 0.7692313.
Perhaps, because x and length(x) aren´t weighted here. The unweighted skew
was 0.58 btw.
#-----------------------------------------------------------------
skew.wtd <- function(x,weight){
weight<-weight[!is.na(x)]
x<-x[!is.na(x)]
sum.w <- sum(weight)
sum.w2 <- sum(weight^2)
mean.w <- sum(x * weight) / sum(weight)
x.sd.w<-sqrt((sum.w / (sum.w^2 - sum.w2)) * sum(weight * (x - mean.w)^2))
((sum(((x - mean.w)/ x.sd.w)^3))/(length(x) - 1))
}
skew.wtd(mydata$var,weight)
#-----------------------------------------------------------------
Because the acid-package doesn´t give a weighted kurtosis, i tried the
following:
#-----------------------------------------------------------------
kurt <- function(x,weight){
weight<-weight[!is.na(x)]
x<-x[!is.na(x)]
mean.w <- sum(x * weight) / sum(weight)
sum.w <- sum(weight)
sum.w2 <- sum(weight^2)
x.sd.w<-sqrt((sum.w / (sum.w^2 - sum.w2)) * sum(weight * (x - mean.w)^2))
#((sum(((x - mean.w)/(sd(x)))^4))/(length(x) - 1)) #formula A
(((sum(((x - mean(x))/(sd(x)))^4))/(length(x) - 1)) - 3) #formula B
}
kurt(mydata$var,weight)
# weighted Kurtosis is -0.7127631
#-----------------------------------------------------------------
kurtosis<-function(x,weight) {
weight<-weight[!is.na(x)]
x<-x[!is.na(x)]
mean.w <- sum(x * weight) / sum(weight)
sum.w <- sum(weight)
sum.w2 <- sum(weight^2)
x.sd.w<-sqrt((sum.w / (sum.w^2 - sum.w2)) * sum(weight * (x - mean.w)^2))
m4<-mean((x - mean.w)^4) #formula
C
kurt<-m4/(x.sd.w^4)-3
kurt}
kurtosis(mydata$var,weight)
# weighted Kurtosis is -0.5076363
# unweighted Kurtosis was -0.72
#-----------------------------------------------------------------
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