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Re: st: RE: How to use Postfile without using a new Stata dataset
Thanks to Austin for patching up my hasty response.
Jeph Herrin wrote:
I think your third approach is certainly not
slower than using a -postfile-, but in any event
this will be I think faster and cleaner than
anything below:
quietly {
gene pvalue=.
local number = r(N)
forvalues i = 1/`number' {
genhwi a[`i'] b[`i'] c[`i']
replace pvalue = r(p_exact) in `i'
}
}
hth,
Jeph
[email protected] wrote:
Thank you so much for your quick reply, Nick.
First off, your title is contradictory. The
whole point of -postfile- is to create a new
dataset.
Sorry, Nick, I will get there, sooner or later. I am still learning
Stata.
I don't have a clear sense of your aim, but
it's not evident that you need alternatives
to -postfile-.
Yes, I do need. Why?
My objetive is to use an approach that enables one to calculate some
statistics and report them in the current dataset.
For example, let�s assume that I have the following data set:
a b c study
26 85 41 1
12 56 23 2
45 85 96 3
45 86 91 4
My objetive is to obtain some statistic from -genhwi-, say, a P value of
the exact approach. My data set should look like this:
a b c study pvalue
26 85 41 1 0.056
12 56 23 2 0.862
45 85 96 3 0.996
45 86 91 4 0.005
However, one needs to type for each observation the number of counts of
each variable to run -genhwi- (and, of course, this is similar for
several
other programs I need to run) and to fill the dataset manually.
Currently,
since I am not fluent in Stata, I am aware of only three approaches to
obtain what I want:
First approach - Using postfile
-----------------BEGIN------------------------
postfile HWE pvalue using nickexample, replace
quietly summarize a
local number = r(N)
forvalues i = 1/`number' {
quietly summarize a if _n==`i'
local a = r(mean)
quietly summarize b if _n==`i'
local b = r(mean)
quietly summarize c if _n==`i'
local c = r(mean)
genhwi `a' `b' `c'
post HWE (r(p_exact))
}
postclose HWE
clear
use nickexample
describe
-----------------END---------------------------
Second approach - Using tempname
-----------------BEGIN------------------------
tempname M
quietly {
quietly summarize a
local number = r(N)
forvalues i = 1/`number' {
quietly summarize a if _n==`i'
local a = r(mean)
quietly summarize b if _n==`i'
local b = r(mean)
quietly summarize c if _n==`i'
local c = r(mean)
genhwi `a' `b' `c'
matrix `M' = nullmat(`M') \ (r(p_exact))
}
}
svmat double `M' , name(pvalue)
-----------------END---------------------------
Third approach - replacing observations
-----------------BEGIN------------------------
quietly {
gene pvalue=.
quietly summarize a
local number = r(N)
forvalues i = 1/`number' {
quietly summarize a if _n==`i'
local a = r(mean)
quietly summarize b if _n==`i'
local b = r(mean)
quietly summarize c if _n==`i'
local c = r(mean)
genhwi `a' `b' `c'
replace pvalue = r(p_exact) in `i'
}
}
-----------------END---------------------------
The first approach creates a new datased. That�s a problem for me. I do
not want that. The second alternative works only for up to 11,000
observations (I work with more than 100,000 observations) and, the third
one, although extremely straightforward in principle, is very
time-consuming.
Thx for any comment.
Tiago
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