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RE: st: Problem with variables in gllamm


From   "Alice Dalton (MED)" <A.Dalton@uea.ac.uk>
To   "statalist@hsphsun2.harvard.edu" <statalist@hsphsun2.harvard.edu>
Subject   RE: st: Problem with variables in gllamm
Date   Mon, 7 Oct 2013 15:59:52 +0000

Yes, the data are in long/stacked format...


>-----Original Message-----
>From: owner-statalist@hsphsun2.harvard.edu [mailto:owner-
>statalist@hsphsun2.harvard.edu] On Behalf Of Nirup M Menon
>Sent: Monday, October 07, 2013 4:19 PM
>To: statalist@hsphsun2.harvard.edu
>Subject: RE: st: Problem with variables in gllamm
>
>In case of repeated measures within an ID, I have had to generate an
>additional ID for the repeating level ("route" in your case). Then I had to stack
>the variables using the reshape command:
>
>gen routeID=_n
>reshape long y, i(idno routeID) j(item)
>
>Did you do the above? ("j-item" is only needed if you have multiple variables,
>but looks like you have one DV, and no IV/controls).
>
>
>
>
>-----Original Message-----
>From: owner-statalist@hsphsun2.harvard.edu [mailto:owner-
>statalist@hsphsun2.harvard.edu] On Behalf Of Alice Dalton (MED)
>Sent: Monday, October 07, 2013 10:27 AM
>To: statalist@hsphsun2.harvard.edu
>Subject: RE: st: Problem with variables in gllamm
>
>I have tested the dv by coding.0021 = 0 and all other non-missing values = 1
>and comparing this with the previous model .0021 = 0 and all other non-
>missing values as the original proportions (thanks Richard). The model outputs,
>however,  are different. If the gllamm model is not running correctly, is there
>another option which allows multilevel modelling where the dv is a proportion?
>Thanks again, Alice
>
>.0021 = 0 and all other non-missing values = 1
>
>. gllamm         Overlap50BuffPropZeroOne, i(Id) family(binomial) link(logit)
>
>Iteration 0:   log likelihood = -11.645217  (not concave)
>Iteration 1:   log likelihood = -11.184372
>Iteration 2:   log likelihood = -10.546455
>Iteration 3:   log likelihood = -10.367375
>Iteration 4:   log likelihood = -10.249232  (not concave)
>Iteration 5:   log likelihood = -9.9592308  (not concave)
>Iteration 6:   log likelihood = -9.9586721  (not concave)
>Iteration 7:   log likelihood = -9.9586414  (not concave)
>Iteration 8:   log likelihood = -9.9586414
>
>number of level 1 units = 276
>number of level 2 units = 51
>
>Condition Number = 16383.537
>
>gllamm model
>
>log likelihood = -9.9586414
>
>------------------------------------------------------------------------------
>Overlap50B~e |      Coef.   Std. Err.      z    P>|z|     [95% Conf. Interval]
>-------------+----------------------------------------------------------
>-------------+------
>       _cons |   792.8736   4252.152     0.19   0.852    -7541.192    9126.939
>------------------------------------------------------------------------------
>
>
>Variances and covariances of random effects
>------------------------------------------------------------------------------
>
>
>***level 2 (Id)
>
>    var(1): 79744.543 (856931)
>------------------------------------------------------------------------------
>
>
>.0021 = 0 and all other non-missing values as original proportions
>
>. gllamm        Overlap50BuffPropWithZeros, i(Id) family(binomial) link(logit)
>
>Iteration 0:   log likelihood = -735.21677  (not concave)
>Iteration 1:   log likelihood = -262.89672  (not concave)
>Iteration 2:   log likelihood =  -214.7793  (not concave)
>Iteration 3:   log likelihood = -189.90975
>Iteration 4:   log likelihood = -181.77366
>Iteration 5:   log likelihood = -180.63042
>Iteration 6:   log likelihood = -180.59617
>Iteration 7:   log likelihood = -180.59616
>
>number of level 1 units = 276
>number of level 2 units = 51
>
>Condition Number = 1.2108434
>
>gllamm model
>
>log likelihood = -180.59616
>
>------------------------------------------------------------------------------
>Overlap50~os |      Coef.   Std. Err.      z    P>|z|     [95% Conf. Interval]
>-------------+----------------------------------------------------------
>-------------+------
>       _cons |    -.48261   .1711324    -2.82   0.005    -.8180232   -.1471967
>------------------------------------------------------------------------------
>
>
>Variances and covariances of random effects
>------------------------------------------------------------------------------
>
>
>***level 2 (Id)
>
>    var(1): .52773855 (.29437029)
>------------------------------------------------------------------------------
>
>
>>-----Original Message-----
>>From: owner-statalist@hsphsun2.harvard.edu [mailto:owner-
>>statalist@hsphsun2.harvard.edu] On Behalf Of Richard Williams
>>Sent: Monday, October 07, 2013 3:33 PM
>>To: statalist@hsphsun2.harvard.edu; statalist@hsphsun2.harvard.edu
>>Subject: RE: st: Problem with variables in gllamm
>>
>>At 08:20 AM 10/7/2013, Alice Dalton (MED) wrote:
>>>Dear Statalist,
>>>
>>>Apologies for omitting information/Stata output from my previous post
>>>(I'm new to Statalist and fairly new to Stata). I provide this below.
>>>Thanks in advance for your help, Alice
>>>
>>>- The dependent variable is continuous (a proportion of range 0.0021
>>>to
>>>0.9976) (it measures proportion of overlap between actual and
>>>predicted commute routes).
>>>- I have 51 participants, each with between 1 and 10 observations
>>>(routes) (n=276 in total).
>>>- I would like to run a fractional logit model (as I'm using proportions).
>>>- I ran this as a gml command initially
>>>(glm   Overlap50BuffProp  Age   Health_binaryReversed DistGIS  PoI
>>>Bike Bus CarBike CarWalk Walk, family(binomial) link(logit) robust)
>>>- I'd like to run this in gllamm (so I can model for observations
>>>within participants).
>>>- I will have just a few predictors (indicated with the glm model  as
>>>age, health, predicted route distance, points of interest en route,
>>>travel mode)
>>>- In the Problem 2 example I gave, I replaced the two lowest values
>>>with zero then the model worked
>>
>>It may have ran, but I am not convinced it ran correctly. Based on your
>>error message I am betting gllamm treated all the non-zero cases as
>>equal to 1, the same as logit does. To test that idea, recode your dv
>>so .0021 = 0 and all other non-missing values = 1. See if you get the
>>same results using that as your dv. If so gllamm is not doing what you
>>want, i.e. it is acting more like logit than it is like glm.
>>
>>
>>>PROBLEM 1. Dependent variable will only work if the variable contains a
>zero:
>>>a) Where smallest value = 0.0021, model fails
>>>
>>>. gllamm   Overlap50BuffProp, i(Id) family(binomial) link(logit)
>>>r(2000);
>>>
>>>b) Where smallest value = 0 , model works (two values of 0.0021
>>>changed to 0)
>>>
>>>. gllamm    Overlap50BuffPropNoZeros, i(Id) family(binomial) link(logit)
>>>
>>>Iteration 0:   log likelihood = -735.21677  (not concave)
>>>Iteration 1:   log likelihood = -262.89672  (not concave)
>>>Iteration 2:   log likelihood =  -214.7793  (not concave)
>>>Iteration 3:   log likelihood = -189.90975
>>>Iteration 4:   log likelihood = -181.77366
>>>Iteration 5:   log likelihood = -180.63042
>>>Iteration 6:   log likelihood = -180.59617
>>>Iteration 7:   log likelihood = -180.59616
>>>
>>>number of level 1 units = 276
>>>number of level 2 units = 51
>>>
>>>Condition Number = 1.2108434
>>>
>>>gllamm model
>>>
>>>log likelihood = -180.59616
>>>
>>>------------------------------------------------------------------------------
>>>Overlap50~os |      Coef.   Std. Err.      z    P>|z|     [95% Conf. Interval]
>>>-------------+--------------------------------------------------------
>>>-------------+-
>>>-------------+-------
>>>        _cons
>>> |    -.48261   .1711324    -2.82   0.005    -.8180232   -.1471967
>>>----------------------------------------------------------------------
>>>-
>>>-------
>>>
>>>Variances and covariances of random effects
>>>----------------------------------------------------------------------
>>>-
>>>-------
>>>
>>>***level 2 (Id)
>>>
>>>     var(1): .52773855 (.29437029)
>>>----------------------------------------------------------------------
>>>-
>>>-------
>>>.
>>>
>>>PROBLEM 2
>>>Adding binary explanatory variables (0/ 1) into the working model
>>>(with zero in dependant variable)
>>>
>>>. gllamm    Overlap50BuffPropNoZeros  Health_binaryReversed, i(Id)
>>>family(binomial) link(logit)
>>>variables have been dropped, can't continue r(198);
>>>
>>>
>>>
>>> >-----Original Message-----
>>> >From: owner-statalist@hsphsun2.harvard.edu [mailto:owner-
>>> >statalist@hsphsun2.harvard.edu] On Behalf Of William Buchanan
>>> >Sent: Monday, October 07, 2013 1:31 PM
>>> >To: statalist@hsphsun2.harvard.edu
>>> >Subject: Re: st: Problem with variables in glamm
>>> >
>>> >If your dependent variable is binary (like it is implied by the
>>> info you provide),
>>> >then the only values it should take are 0 & 1.  Beyond that it
>>> isn't exactly clear
>>> >what your specific problem is. You should also include the _exact_
>>> syntax you
>>> >enter and the exact message/output provided by Stata.
>>> >
>>> >Sent from my iPhone
>>> >
>>> >> On Oct 7, 2013, at 6:53, "Alice Dalton (MED)" <A.Dalton@uea.ac.uk>
>>wrote:
>>> >>
>>> >> Dear Statlist,
>>> >>
>>> >> I'm having a problem with the gllamm program (family(binomial)
>>> link(logit)).
>>> >>
>>> >> 1. My dependant variable (a proportion) will only work if the
>>> >> variable
>>> >contains a zero, otherwise I get an r(2000) (no observations) error
>>> >>
>>> >> 2. Adding binary explanatory variables (eg a health variable where
>>> >> 1
>>> >excellent, 0 not excellent) results in the message 'variables have
>>> >been dropped, can't continue' and an r(198) error. The null model
>>> >works; the null model works with continuous variables added in; the
>>> >null model plus one or more binary variables fails.
>>> >>
>>> >> The command I am using is     gllamm  [depvar] [varlist], i(ParticipantId)
>>> >family(binomial) link(logit). I have 276 cases and 129 variables
>>> (not all of which
>>> >are added to the model).
>>> >>
>>> >> If anyone with experience of gllamm has an idea of what is
>>> >> happening here,
>>> >I would be most grateful to hear it.
>>> >>
>>> >> Thank you!
>>> >>
>>> >> Alice Dalton
>>> >>
>>> >> *
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>>
>>-------------------------------------------
>>Richard Williams, Notre Dame Dept of Sociology
>>OFFICE: (574)631-6668, (574)631-6463
>>HOME:   (574)289-5227
>>EMAIL:  Richard.A.Williams.5@ND.Edu
>>WWW:    http://www.nd.edu/~rwilliam
>>
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