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Re: st: scalar question
I don't think this suggested approach will handle missing values properly
since it will treat them as zeros added to the row sums and give no
indication of a reduced number of observations.
Getting back to the original approach. I'm not sure if you wouldn't be
better off with a reshape long to get what you want, but if you keep with
the current approach, I would change the middle of the loop to :
summarize V`i', meanonly
scalar average_V`i'= r(mean)
scalar z= z + cond( average_V`i'<. , average_V`i',0)
The cond() function will avoid adding a missing value into your sum (but it
will replace it with 0). You may want to count how many missing values you
end up with (in another scalar counter). I also added the meanonly topion
to the summarize command to make it faster
Michael Blasnik
----- Original Message -----
"Radu Ban" <[email protected]> wrote
you can try the -egen rsum- command and then take the mean, as the sum
of means is equal to mean of sums. for example:
egen rowsum = rsum(V*)
summarize rowsum
scalar z = r(mean)
drop rowsum
2006/7/21, Jeffrey W Ladewig <[email protected]>:
I am using a simple while loop statement (see below) to add the mean of
each
variable in a series. The program runs fine except if one of the
variables
contains all missing values (there are reasons why I need to keep the
variables). If, for instance, the 500th variable contains all missing
values, then the scalar (i.e., average_V`i') for the 500th variable
equals a
missing value (of course), but the additive scalar (i.e., z) from that
point
forward only reports missing values. That is, my additive scalar stops
adding. I have been programming a bypass around each of these
problematic
variables, but is there a command or something that I could use instead?
Thanks!
Example:
scalar z = 0
local i = 1
while `i' <= 1000 {
summarize V`i'
scalar average_V`i'= r(mean)
scalar z= z + average_V`i'
local i = `i' + 1
}
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