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Re: st: Re: Programmatically determining if predictors have been dropped from a model
From
Steve Samuels <[email protected]>
To
[email protected]
Subject
Re: st: Re: Programmatically determining if predictors have been dropped from a model
Date
Thu, 16 Sep 2010 13:32:27 -0400
--
capture the statements or the block of statements that might stop the
run because of a dropped predictor. Capture has no options that I know
of.
Steve
On Thu, Sep 16, 2010 at 1:10 PM, Barth Riley <[email protected]> wrote:
> What particular option with capture would I use?
>
> -----Original Message-----
> From: [email protected]
> [mailto:[email protected]] On Behalf Of Steve Samuels
> Sent: Thursday, September 16, 2010 12:06 PM
> To: [email protected]
> Subject: Re: st: Re: Programmatically determining if predictors have been
> dropped from a model
>
> --
> Barth, if your only goal is to keep the -do file- going, how about
> -capture- or -capture noisily-?
>
> Steve
>
> Steven J. Samuels
> [email protected]
>
> On Thu, Sep 16, 2010 at 12:53 PM, Joseph Coveney <[email protected]>
> wrote:
>> Barth Riley wrote:
>>
>> I am running a series of Monte Carlo simulations using logistic
> regression.
>> Occasionally, the regression analysis will drop a variable (due to
>> collinearity, the variable having only one value, or the variable
> perfectly
>> predicting the outcome). I would like to know when a variable has been
>> dropped in order to prevent my do file from crashing (i.e., when I call
> test
>> <indep. Var> and the variable doesn't exit in the model). I have tried
> using
>> both logistic and logit functions and neither provide information on the
>> variables left in the model (i.e., via return list), nor does the confirm
>> function work with variables as they exist in a model. Does anyone have
> any
>> suggestions?
>>
>>
> ----------------------------------------------------------------------------
> ----
>>
>> Isn't that kind of information in the coefficient vector (coefficients
> fixed to
>> zero, and column equation names containing the letter "o" for "omitted"),
> and
>> other ereturn matrixes, like e(Cns), e(rules) and so on?
>>
>> Joseph Coveney
>>
>> . sysuse auto
>> (1978 Automobile Data)
>>
>> . generate byte k = 1
>>
>> . generate int weight1 = weight - 1
>>
>> . logistic foreign i.k c.(weight weight1), nolog
>> note: 1.k omitted because of collinearity
>> note: weight1 omitted because of collinearity
>>
>> Logistic regression Number of obs =
> 74
>> LR chi2(1) =
> 31.96
>> Prob > chi2 =
> 0.0000
>> Log likelihood = -29.054002 Pseudo R2 =
> 0.3548
>>
>>
> ----------------------------------------------------------------------------
> --
>> foreign | Odds Ratio Std. Err. z P>|z| [95% Conf.
> Interval]
>>
> -------------+--------------------------------------------------------------
> --
>> 1.k | (omitted)
>> weight | .997416 .0006078 -4.25 0.000 .9962254
> .9986079
>> weight1 | (omitted)
>>
> ----------------------------------------------------------------------------
> --
>>
>> . foreach matrix in b Cns rules {
>> 2. matrix list e(`matrix')
>> 3. }
>>
>> e(b)[1,4]
>> foreign: foreign: foreign: foreign:
>> 1o. o.
>> k weight weight1 _cons
>> y1 0 -.00258739 0 6.2825993
>>
>> e(Cns)[2,5]
>> foreign: foreign: foreign: foreign: _Cns:
>> 1o. o.
>> k weight weight1 _cons _r
>> r1 1 0 0 0 0
>> r2 0 0 1 0 0
>>
>> e(rules)[2,4]
>> c1 c2 c3 c4
>> 1.k 4 0 0 0
>> weight1 4 0 0 0
>>
>>
>> *
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>>
>
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