Hi all,
Is there a way to generate a new variable with those scalars  instead of
just displaying it??
For example gen a=e(N) * e(r2) doesn't work..
Rajesh
-----Original Message-----
From: [email protected]
[mailto:[email protected]] On Behalf Of Richard Williams
Sent: 10 February 2006 16:06
To: [email protected]
Subject: Re: st: question from a new user
At 10:20 AM 2/10/2006, Rudy Fichtenbaum wrote:
>I am a relatively new user of Stata having used SAS for many years.
>I know that when you run a regression in Stata it produces a whole
>series of results that are stored under e( ) e.g. R squared, N, RSS
>etc. The problem I am having is I can't figure out how to access
>those stored results. I want to use the stored results to calculate
>an LM statistic. I can do this manually using the results and the
>display command but there must be a way of programing Stata to do this
task.
>
>Here is what I want to do. I run a restricted regression and then
>obtain the residuals. Next I regress the residuals on the
>independent variables from the unrestricted model. N times the R
>square from this equation is an LM statistic.
>
>Rudy
Welcome to Stata Rudy.  First off though, kill the little vcf
attachments when you post since they screw up some people's
email!  Here is a simple example.  Basically, you just find out how
the quantities you want are stored and then use them in your calculations.
. webuse auto
(1978 Automobile Data)
. quietly reg weight length
. ereturn list
scalars:
                  e(N) =  74
               e(df_m) =  1
               e(df_r) =  72
                  e(F) =  613.2727965692631
                 e(r2) =  .8949323534212077
               e(rmse) =  253.6640262138552
                e(mss) =  39461306.8283367
                e(rss) =  4632871.550041688
               e(r2_a) =  .8934730805520578
                 e(ll) =  -513.6524782518598
               e(ll_0) =  -597.0190609278627
macros:
              e(title) : "Linear regression"
             e(depvar) : "weight"
                e(cmd) : "regress"
         e(properties) : "b V"
            e(predict) : "regres_p"
              e(model) : "ols"
          e(estat_cmd) : "regress_estat"
matrices:
                  e(b) :  1 x 2
                  e(V) :  2 x 2
functions:
             e(sample)
. display e(N) * e(r2)
66.224994
-------------------------------------------
Richard Williams, Notre Dame Dept of Sociology
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