2-level empty ordinal model

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vivian1234
Posts: 30
Joined: Tue Apr 12, 2016 10:54 am

2-level empty ordinal model

Post by vivian1234 »

Hi,

I am new to runmlwin and I have a problem in running a 2-level empty model for my ordinal outcome variable.

This is my code:

Code: Select all

runmlwin outcome cons, ///
	level2(hh: (cons))	///
	level1(id:)	///
	discrete(distribution(multinomial) link(ologit) denominator(cons) basecategory(4) pql2) nopause

Code: Select all

runmlwin outcome cons, ///
	level2(hh: (cons))	///
	level1(id:)	///
	discrete(distribution(multinomial) link(ologit) denominator(cons) basecategory(4))  ///
	mcmc(on) initsprevious nopause

However, the result looks strange. rather than giving me the value for var(cons_123), it gives me 6 random effect parameters.

Thank you very much!

Vivian
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ChrisCharlton
Posts: 1390
Joined: Mon Oct 19, 2009 10:34 am

Re: 2-level empty ordinal model

Post by ChrisCharlton »

Is the following more like what you are after?

Code: Select all

use http://www.bristol.ac.uk/cmm/media/runmlwin/alevchem, clear
egen school = group(lea estab)

Code: Select all

runmlwin a_point cons, level2(school: (cons, contrast(1/5))) level1(pupil: ) discrete(distribution(multinomial) link(ologit) denom(cons) base(6) pql2) nopause

MLwiN 2.36 multilevel model                     Number of obs      =      2166
Ordered multinomial logit response model
Estimation algorithm: IGLS, PQL2

-----------------------------------------------------------
                |   No. of       Observations per Group
 Level Variable |   Groups    Minimum    Average    Maximum
----------------+------------------------------------------
         school |      220          1        9.8         94
-----------------------------------------------------------

----------------------------------
    Contrast | Log-odds
-------------+--------------------
           1 | 1 vs. 2 3 4 5 6
           2 | 1 2 vs. 3 4 5 6
           3 | 1 2 3 vs. 4 5 6
           4 | 1 2 3 4 vs. 5 6
           5 | 1 2 3 4 5 vs. 6
----------------------------------

Run time (seconds)   =       4.84
Number of iterations =          8
------------------------------------------------------------------------------
             |      Coef.   Std. Err.      z    P>|z|     [95% Conf. Interval]
-------------+----------------------------------------------------------------
Contrast 1   |
      cons_1 |  -1.372275   .1031697   -13.30   0.000    -1.574484   -1.170066
-------------+----------------------------------------------------------------
Contrast 2   |
      cons_2 |  -.4809893   .0982812    -4.89   0.000    -.6736169   -.2883616
-------------+----------------------------------------------------------------
Contrast 3   |
      cons_3 |   .2940195   .0976683     3.01   0.003     .1025931    .4854459
-------------+----------------------------------------------------------------
Contrast 4   |
      cons_4 |   1.171295   .1001301    11.70   0.000     .9750433    1.367546
-------------+----------------------------------------------------------------
Contrast 5   |
      cons_5 |   2.391542   .1099275    21.76   0.000     2.176088    2.606996
------------------------------------------------------------------------------

------------------------------------------------------------------------------
   Random-effects Parameters |   Estimate   Std. Err.     [95% Conf. Interval]
-----------------------------+------------------------------------------------
Level 2: school              |
             var(cons_12345) |   1.329894   .1784171      .9802029    1.679585
------------------------------------------------------------------------------

Code: Select all

runmlwin a_point cons, level2(school: (cons, contrast(1/5))) level1(pupil: ) discrete(distribution(multinomial) link(ologit) denom(cons) base(6)) mcmc(on) initsprevious nopause


MLwiN 2.36 multilevel model                     Number of obs      =      2166
Ordered multinomial logit response model
Estimation algorithm: MCMC

-----------------------------------------------------------
                |   No. of       Observations per Group
 Level Variable |   Groups    Minimum    Average    Maximum
----------------+------------------------------------------
         school |      220          1        9.8         94
-----------------------------------------------------------

----------------------------------
    Contrast | Log-odds
-------------+--------------------
           1 | 1 vs. 2 3 4 5 6
           2 | 1 2 vs. 3 4 5 6
           3 | 1 2 3 vs. 4 5 6
           4 | 1 2 3 4 vs. 5 6
           5 | 1 2 3 4 5 vs. 6
----------------------------------

Burnin                     =        500
Chain                      =       5000
Thinning                   =          1
Run time (seconds)         =       46.6
Deviance (dbar)            =    7046.19
Deviance (thetabar)        =    6891.03
Effective no. of pars (pd) =     155.16
Bayesian DIC               =    7201.36
------------------------------------------------------------------------------
             |      Mean    Std. Dev.     ESS     P       [95% Cred. Interval]
-------------+----------------------------------------------------------------
Contrast 1   |
      cons_1 |  -1.324595    .103499       40   0.000    -1.506988   -1.094635
-------------+----------------------------------------------------------------
Contrast 2   |
      cons_2 |   -.462988    .098712       36   0.000    -.6341004   -.2398282
-------------+----------------------------------------------------------------
Contrast 3   |
      cons_3 |   .2886934   .1000466       35   0.000     .1082631    .5150148
-------------+----------------------------------------------------------------
Contrast 4   |
      cons_4 |   1.142674   .1030695       38   0.000     .9477457    1.380694
-------------+----------------------------------------------------------------
Contrast 5   |
      cons_5 |   2.339768    .113265       47   0.000      2.12508    2.583499
------------------------------------------------------------------------------

------------------------------------------------------------------------------
   Random-effects Parameters |     Mean   Std. Dev.   ESS     [95% Cred. Int]
-----------------------------+------------------------------------------------
Level 2: school              |
             var(cons_12345) |  1.271291  .1928117    469   .9335589  1.688362
------------------------------------------------------------------------------
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vivian1234
Posts: 30
Joined: Tue Apr 12, 2016 10:54 am

Re: 2-level empty ordinal model

Post by vivian1234 »

So I have to specify contrast(1/3) in the 2nd level random part? Is it because the var(cons.123) is the only random part?

Thank you so much!


Vivian
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ChrisCharlton
Posts: 1390
Joined: Mon Oct 19, 2009 10:34 am

Re: 2-level empty ordinal model

Post by ChrisCharlton »

The contrast option is used where you want a parameter to be common across more than one of the response categories (as in your case), otherwise the default is to have a separate parameter for each of the response categories.
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