Difference between Hmisc wtd.var and SAS proc Mean generated weighted variance - r

Question

I'm getting different results from R and SAS when I try to calculate a weighted variance. Does anyone know what might be causing this difference?
I create vectors of weights and values and I then calculate the weighted variance using the
Hmisc library wtd.var function:
library(Hmisc)
wt <- c(5, 5, 4, 1)
x <- c(3.7,3.3,3.5,2.8)
wtd.var(x,weights=wt)
I get an answer of:
[1] 0.0612381
But if I try to reproduce these results in SAS I get a quite different result:
data test;
input wt x;
cards;
5 3.7
5 3.3
4 3.5
1 2.8
;
run;
proc means data=test var;
var x;
weight wt;
run;
Results in an answer of
0.2857778

Answer 1

You probably have a difference in how the variance is calculated. SAS gives you an option, VARDEF, which may help here.
proc means data=test var vardef=WDF;
var x;
weight wt;
run;
That on your dataset gives a variance similar to r. Both are 'right', depending on how you choose to calculate the weighted variance. (At my shop we calculate it a third way, of course...)
Complete text from PROC MEANS documentation:
VARDEF=divisor specifies the divisor to use in the calculation of the
variance and standard deviation. The following table shows the
possible values for divisor and associated divisors.
Possible Values for VARDEF=
Value Divisor Formula for Divisor
DF degrees of freedom n - 1
N number of observations n
WDF sum of weights minus one ([Sigma]iwi) - 1
WEIGHT | WGT sum of weights [Sigma]iwi
The procedure computes the variance as CSS/Divisor, where CSS
is the corrected sums of squares and equals Sum((Xi-Xbar)^2). When you
weight the analysis variables, CSS equals sum(Wi*(Xi-Xwbar)^2), where
Xwbar is the weighted mean.
Default: DF Requirement: To compute the standard error of the mean,
confidence limits for the mean, or the Student's t-test, use the
default value of VARDEF=.
Tip: When you use the WEIGHT statement and
VARDEF=DF, the variance is an estimate of Sigma^2, where the
variance of the ith observation is Sigma^2/wi and wi is the
weight for the ith observation. This method yields an estimate of the
variance of an observation with unit weight.
Tip: When you use the
WEIGHT statement and VARDEF=WGT, the computed variance is
asymptotically (for large n) an estimate of Sigma^2/wbar, where
wbar is the average weight. This method yields an asymptotic
estimate of the variance of an observation with average weight.