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From |
<[email protected]> |

To |
<[email protected]> |

Subject |
Re: AW: st: gllamm, multilevel conditional logit |

Date |
Wed, 27 Nov 2013 19:48:27 +0000 |

```
This is the data-structure concerning your question. For observation with id == 1 there are 3 lines which equals the number of alternatives.
---------------------------
id alt choice
---------------------------
1 1 0
1 2 0
1 3 1
2 1 0
2 2 1
2 3 0
...
This is in line with the gllamm-help file: "the 'response' labels the original lines of data". If I do specify the hierarchical structure with "i(region) instead of "i(id region)", I would get a 2-level-model. The N on level 1 would equal all observations (lines) and the N on level 2 are the number of regions. The "id"-variable therefore has to be used to take the clustering of the data into account.
With the definition in my example, I estimate a 3-level-model: N on level 1 equals all observations (lines), N on level 2 equals the number of clusters (students) and the N on level 3 is the number of regions
>> i(id region) noconstant ///
>> nrf(2 2) eq(gam1 gam2 gam1 gam2) ///
>> expanded(id choice o)
As I have mentioned, I do get the same results when using a single-level model with gllamm and asclogit, respectively. Is this answering your question? What is your comment on my previous questions?
>> Questions:
>> 1. I am not sure whether I have to include the alternative-specific constant variables (alt2, alt3) in the model, when I also use them in the equation-specification? If I do not, some of the fixed-effects estimates change their direction and are no longer in line with theory and expectations.
>> 2. I am not sure whether I really need a 3-level model, since the data on the chosen track is clustered within students. Because in gllamm the cluster-option is only available on the highest hierarchy-level, I so far cannot see a solution to estimate a model with clustered-student information on level 1 and region-information on level 2.
>>
...
You are using ID as an real ID and also as the the actual response in your expanded syntax?
>> i(id region) noconstant ///
>> nrf(2 2) eq(gam1 gam2 gam1 gam2) ///
>> expanded(id choice o)
Maybe this is causing trouble.
> On 26.11.2013, at 23:03, <[email protected]> wrote:
>
> Thanks for your comment.
>
> My expand-syntax is "expanded(id choice o)". "id" is used as identifier for what is called "response" in the help file. My variable "choice" equals 1 if the alternative is selected. In the single-level model I do get identical results with asclogit and gllamm with the mentioned expand-specification. Why do you think the expand-specification is wrong?
>
> David
>
>
>
> -----Ursprüngliche Nachricht-----
> Von: [email protected] [mailto:[email protected]] Im Auftrag von Jonas Krueger
> Gesendet: Dienstag, 26. November 2013 17:19
> An: [email protected]
> Betreff: Re: st: gllamm, multilevel conditional logit
>
> Hi. From a quick Look i guess your expand Syntax is wrong. Check the help file.
>
>> On 25.11.2013, at 16:12, <[email protected]> wrote:
>>
>> Dear Users,
>> I'm using -gllamm expand() link(mlogit)- with Stata13 and getting a result I know is wrong, so I know I'm misunderstanding something with the equation-specification.
>>
>> I have data on educational track decision, including alternative-specific and case-constant variables on individual and regional level, and would like to use gllamm to estimate a 3-level discrete choice model with fixed-effects and random effects on individual and region-level. So far I have just found stata-syntax on British election data from Skrondal & Rabe-Hesketh (2004, http://www.gllamm.org/books/readme.html#13.4, Model M23(c)), which comes close to what I would like to do.
>>
>> My data is in long form and consists for each student 3 rows for the 3 educational alternatives/modes (track1, track2, track3), from which students had to choose one, as well as a dummy-variable choice, which indicates the chosen track by the student. I use 2 alternative-specific variables (as1, as2) as well as case constant variables for egp-class and math-grades on individual-level and 2 variables on the region-level (reg1, reg2).
>>
>> I first fitted single-level models without the variables on the region-level using asclogit and gllamm with the syntax below - the results are identical and in line with theory:
>>
>> asclogit choice as1 as2 , casevars(math egp2 egp34 egp567) ///
>> case(id) alternative(alt) basealternative(1) vce(cluster id) nolog
>>
>> gllamm alt as1 as2 ///
>> alt2Xmath alt2Xegp2 alt2Xegp34 alt2Xegp567 alt2 ///
>> alt3Xmath alt3Xegp2 alt3Xegp34 alt3Xegp567 alt3 ///
>> i(id) link(mlogit) expanded(id choice o) noconstant cluster(id) robust init
>>
>> The second step would now be to estimate a 3-level conditional logit-model, including fixed-effects for alternative and case-constant variables and a random part on the student and region-level:
>>
>> eq gam1: alt2
>> eq gam2: alt3
>>
>> gllamm alt as1 as2 ///
>> alt2Xreg1 alt2Xreg2 alt2Xmath alt2Xegp2 alt2Xegp34 alt2Xegp567 alt2 ///
>> alt3Xreg1 alt3Xreg2 alt3Xmath alt3Xegp2 alt3Xegp34 alt3Xegp567 alt3 ///
>> i(id region) noconstant ///
>> nrf(2 2) eq(gam1 gam2 gam1 gam2) ///
>> expanded(id choice o) f(binom) link(mlogit) adapt trace
>>
>> Questions:
>> 1. I am not sure whether I have to include the alternative-specific constant variables (alt2, alt3) in the model, when I also use them in the equation-specification? If I do not, some of the fixed-effects estimates change their direction and are no longer in line with theory and expectations.
>> 2. I am not sure whether I really need a 3-level model, since the data on the chosen track is clustered within students. Because in gllamm the cluster-option is only available on the highest hierarchy-level, I so far cannot see a solution to estimate a model with clustered-student information on level 1 and a region-information on level 2.
>>
>> I would be very thankful for comments on my gllamm-syntax and questions.
>> Kind regards
>> David
>>
>>
>>
>> *
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>
> *
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>
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*
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```

**Follow-Ups**:**Re: AW: st: gllamm, multilevel conditional logit***From:*Jonas Krüger <[email protected]>

**References**:**st: gllamm, multilevel conditional logit***From:*<[email protected]>

**Re: st: gllamm, multilevel conditional logit***From:*Jonas Krueger <[email protected]>

**AW: st: gllamm, multilevel conditional logit***From:*<[email protected]>

**Re: AW: st: gllamm, multilevel conditional logit***From:*Jonas Krueger <[email protected]>

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