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Re: st: ranking with weights
--
Cindy-- The weights are not likely to be frequency weights (fweights)
--they are probability weights (pweights), possibly post-stratified.
If they are whole numbers than someone has rounded them. You till
haven't answered the question: why do you want to rank the
households? Quantities calculated in samples are estimates of
population quantities. What population quantities are you trying to
estimate with the ranks? If you are trying to estimate percentiles,
the -pctile- command will take pweights.
-Steve
On Dec 2, 2008, at 2:16 PM, Cindy Gao wrote:
Thanks for your reply.
The observations (analytic units) are households. Expenditure is
the monthly expenditure of household. This is household survey
data. The weights are frequency weights, to weight the sample to
the whole country. The weights are likely to vary across for
example regions, to compensate for oversampling or undersampling.
Basically I need to rank all households according to their
expenditure, from lowest to highest. But, I must take account of
the weightings. If for example there are 2 households with the same
expenditure, they must be ranked the same and this rank must take
account of weightings. If there were no ties (households with same
expenditure), I could achieve mission by generating a variable
"rank", like -g rank=sum(weight)-. The problem comes because of
ties. If i could -expand- my dataset using weights, then i could
simply say -egen rank =rank(expenditure)- ; the problem is that
dataset is too large for this.
---- Original Message ----
From: Steven Samuels <[email protected]>
Cindy, What are the analytic units (people? regions?). What are
the "weights"? What is "expenditure"? How is it measured. What do
you mean that some regions are "less sampled" than others. It's
not clear, for example, if this is a sample, and if so, of what?
So, please describe the study design in detail. Last question:
what is the purpose of the ranking?
-On Dec 2, 2008, at 12:54 PM, Cindy Gao wrote:
I am trying to find a way to rank weighted data (since the egen
function -rank- does not work with weights). A simple way would be
order the data in terms of variable that I have interest in
(monthly expenditure) and then create a new variable like -g
rank1=sum(weight)-. But, there is problem. Some of my observations
are "tied" as they have the same level of expenditure. Using the
simple method I mention means that some observations are ranked
above others even though they have same level of expenditure. This
is a problem as the weights are large so you find that 2
observations are ranked with bug gap in between even though same
level of expenditure. It is even bigger problem because the
weights might be correlated with some other variables I am
interested in (like region, since some regions are less sampled
than other). I also try multiplying the expenditure ranking by the
weight, but this gives wrong results (for example they do not add
up to weighted
total). Can anyone help? In other words, I would like for all
observations with same expenditure to have same rank, which I
assume would be some average of all the weighted observations
having that same expenditure.. I include a sample dataset below:
expenditure weighting rank rank1
weighted_rank
10 341 1
341 341
12 1065 2.5 1406 ???
12 98 2.5 1504
15 254 4 1758
.
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