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| From | Martyn Sherriff <statams48@gmail.com> |
| To | statalist@hsphsun2.harvard.edu |
| Subject | Re: st: RE: RE: Re: Loglinear quasi-symmetric agreement |
| Date | Thu, 7 Jun 2012 22:21:41 +0100 |
I am now more confused than normal. Basically can I use 'factor
notation' with glm?
Usin the ATS dataset I get:
. xi:glm count i.px i.py i.symm, fam(poi) nolog
i.px _Ipx_1-4 (naturally coded; _Ipx_1 omitted)
i.py _Ipy_1-4 (naturally coded; _Ipy_1 omitted)
i.symm _Isymm_1-10 (naturally coded; _Isymm_1 omitted)
note: _Isymm_5 dropped because of collinearity
note: _Isymm_8 dropped because of collinearity
note: _Isymm_10 dropped because of collinearity
Generalized linear models No. of obs = 16
Optimization : ML Residual df = 3
Scale parameter = 1
Deviance = .978304658 (1/df) Deviance = .3261016
Pearson = .621982784 (1/df) Pearson = .2073276
Variance function: V(u) = u [Poisson]
Link function : g(u) = ln(u) [Log]
AIC = 4.237311
Log likelihood = -20.89849023 BIC = -7.339462
------------------------------------------------------------------------------
| OIM
count | Coef. Std. Err. z P>|z| [95% Conf. Interval]
-------------+----------------------------------------------------------------
_Ipx_2 | -.2360635 .430017 -0.55 0.583 -1.078881 .6067543
_Ipx_3 | -.5038594 .5050534 -1.00 0.318 -1.493746 .486027
_Ipx_4 | 8.229144 1131.109 0.01 0.994 -2208.703 2225.161
_Ipy_2 | -.9090505 .430017 -2.11 0.035 -1.751868 -.0662327
_Ipy_3 | .9963219 .5050534 1.97 0.049 .0064355 1.986208
_Ipy_4 | -9.017585 1131.109 -0.01 0.994 -2225.95 2207.915
_Isymm_2 | -1.321299 .4521483 -2.92 0.003 -2.207493 -.4351046
_Isymm_3 | -3.595678 .783512 -4.59 0.000 -5.131334 -2.060023
_Isymm_4 | -27.11437 2775.396 -0.01 0.992 -5466.791 5412.562
_Isymm_6 | -1.186505 .4441345 -2.67 0.008 -2.056992 -.3160173
_Isymm_7 | -10.41121 1131.109 -0.01 0.993 -2227.344 2206.522
_Isymm_9 | -9.48327 1131.109 -0.01 0.993 -2226.415 2207.449
_cons | 3.091024 .2132027 14.50 0.000 2.673155 3.508894
------------------------------------------------------------------------------
Which seems to me to be reasonable.
But if I omit the xi (which I thought I could do in Stata 12) I get:
. glm count i.px i.py i.symm, fam(poi) nolog
note: 7.symm omitted because of collinearity
note: 9.symm omitted because of collinearity
note: 10.symm omitted because of collinearity
Generalized linear models No. of obs = 16
Optimization : ML Residual df = 3
Scale parameter = 1
Deviance = .978304658 (1/df) Deviance = .3261016
Pearson = .621982784 (1/df) Pearson = .2073276
Variance function: V(u) = u [Poisson]
Link function : g(u) = ln(u) [Log]
AIC = 4.237311
Log likelihood = -20.89849023 BIC = -7.339462
------------------------------------------------------------------------------
| OIM
count | Coef. Std. Err. z P>|z| [95% Conf. Interval]
-------------+----------------------------------------------------------------
px |
2 | -10.64727 1131.109 -0.01 0.992 -2227.58 2206.286
3 | -9.987129 1131.109 -0.01 0.993 -2226.92 2206.945
4 | 8.229144 1131.109 0.01 0.994 -2208.703 2225.161
|
py |
2 | -11.32026 1131.109 -0.01 0.992 -2228.253 2205.613
3 | -8.486948 1131.109 -0.01 0.994 -2225.419 2208.445
4 | -9.017585 1131.109 -0.01 0.994 -2225.95 2207.915
|
symm |
2 | 9.089909 1131.109 0.01 0.994 -2207.843 2226.023
3 | 5.887591 1131.109 0.01 0.996 -2211.045 2222.82
4 | -27.11437 2775.396 -0.01 0.992 -5466.791 5412.562
5 | 20.82242 2262.218 0.01 0.993 -4413.043 4454.688
6 | 18.70797 2262.217 0.01 0.993 -4415.157 4452.573
7 | 0 (omitted)
8 | 18.96654 2262.217 0.01 0.993 -4414.898 4452.831
9 | 0 (omitted)
10 | 0 (omitted)
|
_cons | 3.091024 .2132027 14.50 0.000 2.673155 3.508894
------------------------------------------------------------------------------
I have probably missed it, but I cannot find anything in the
documentation related to factor that says that I cannot use it with
glm, or does it produce a different parametrisation, or am I missing
something that is obvious?
My Stata update is
Update status
Last check for updates: 07 Jun 2012
New update available: none (as of 07 Jun 2012)
Current update level: 23 May 2012 (what's new)
Thanks,
Martyn
On 7 June 2012 20:52, Martyn Sherriff <statams48@gmail.com> wrote:
> Shaun, thank you for the link. I will follow it up and see whatI can find.
> Cheers,
> Martyn
>
> On 7 June 2012 18:19, Scholes, Shaun <s.scholes@ucl.ac.uk> wrote:
>> Actually, this appears to give different results:
>>
>> version 9
>> use http://www.ats.ucla.edu/stat/stata/examples/icda/carcinoma, clear
>> xi: glm count i.px i.py i.symm, fam(poi) nolog
>>
>> hope this helps
>> best wishes
>> Shaun
>>
>>
>>
>>
>>
>>
>>
>> -----Original Message-----
>> From: owner-statalist@hsphsun2.harvard.edu [mailto:owner-statalist@hsphsun2.harvard.edu] On Behalf Of Scholes, Shaun
>> Sent: 07 June 2012 18:07
>> To: statalist@hsphsun2.harvard.edu
>> Subject: st: RE: Re: Loglinear quasi-symmetric agreement
>>
>> Martyn, I can't help you with your question but it may be worth taking a close look at:
>>
>> http://www.ats.ucla.edu/stat/stata/examples/icda/icdast9.htm
>>
>> Best wishes
>> Shaun
>>
>>
>>
>> -----Original Message-----
>> From: owner-statalist@hsphsun2.harvard.edu [mailto:owner-statalist@hsphsun2.harvard.edu] On Behalf Of Martyn Sherriff
>> Sent: 07 June 2012 16:24
>> To: statalist@hsphsun2.harvard.edu
>> Subject: st: Re: Loglinear quasi-symmetric agreement
>>
>> I am trying to use loglinear models to assess agreement using the quasi-symmetry model and have used the data from Agresti (An Introduction to Categorical Analysis, p 245) to check my method.
>>
>> +-------------------------+
>> | px py count qasym |
>> |-------------------------|
>> 1. | 1 1 22 1 |
>> 2. | 1 2 2 2 |
>> 3. | 1 3 2 3 |
>> 4. | 1 4 0 4 |
>> 5. | 2 1 5 2 |
>> |-------------------------|
>> 6. | 2 2 7 5 |
>> 7. | 2 3 14 6 |
>> 8. | 2 4 0 7 |
>> 9. | 3 1 0 3 |
>> 10. | 3 2 2 6 |
>> |-------------------------|
>> 11. | 3 3 36 8 |
>> 12. | 3 4 0 9 |
>> 13. | 4 1 0 4 |
>> 14. | 4 2 1 7 |
>> 15. | 4 3 17 9 |
>> |-------------------------|
>> 16. | 4 4 10 10 |
>> +-------------------------+
>>
>> The simple symmetry model is satisfactory:
>> glm count i.px i.py, fam(poi) nolog
>>
>> Generalized linear models No. of obs = 16
>> Optimization : ML Residual df = 9
>> Scale parameter = 1
>> Deviance = 117.9568605 (1/df) Deviance = 13.10632
>> Pearson = 120.2634516 (1/df) Pearson = 13.36261
>>
>> Variance function: V(u) = u [Poisson]
>> Link function : g(u) = ln(u) [Log]
>>
>> AIC = 10.79847
>> Log likelihood = -79.38776817 BIC = 93.00356
>>
>> ------------------------------------------------------------------------------
>> | OIM
>> count | Coef. Std. Err. z P>|z| [95% Conf. Interval]
>> -------------+----------------------------------------------------------
>> -------------+------
>> px |
>> 2 | -4.07e-08 .2773501 -0.00 1.000 -.5435962 .5435962
>> 3 | .3794896 .2545139 1.49 0.136 -.1193485 .8783277
>> 4 | .0741079 .2723524 0.27 0.786 -.4596929 .6079088
>> |
>> py |
>> 2 | -.8109302 .3469443 -2.34 0.019 -1.490929 -.1309318
>> 3 | .9382696 .2270017 4.13 0.000 .4933544 1.383185
>> 4 | -.9932518 .3701851 -2.68 0.007 -1.718801 -.2677022
>> |
>> _cons | 1.783249 .2588899 6.89 0.000 1.275834 2.290664
>> ------------------------------------------------------------------------------
>>
>> However when I attempt the quasi-symmetric model I get very large and equal standard errors which do not make sense to me:
>>
>> . glm count i.px i.py i.qasym, fam(poi) nolog
>> note: 7.qasym omitted because of collinearity
>> note: 9.qasym omitted because of collinearity
>> note: 10.qasym omitted because of collinearity
>>
>> Generalized linear models No. of obs = 16
>> Optimization : ML Residual df
>> = 3
>> Scale
>> parameter = 1
>> Deviance = .978304658 (1/df) Deviance = .3261016
>> Pearson = .621982784 (1/df) Pearson = .2073276
>>
>> Variance function: V(u) = u [Poisson]
>> Link function : g(u) = ln(u) [Log]
>>
>> AIC
>> = 4.237311
>> Log likelihood = -20.89849023 BIC = -7.339462
>>
>> ------------------------------------------------------------------------------
>> | OIM
>> count | Coef. Std. Err. z P>|z| [95% Conf. Interval]
>> -------------+----------------------------------------------------------
>> -------------+------
>> px |
>> 2 | -10.64727 1131.109 -0.01 0.992 -2227.58 2206.286
>> 3 | -9.987129 1131.109 -0.01 0.993 -2226.92 2206.945
>> 4 | 8.229144 1131.109 0.01 0.994 -2208.703 2225.161
>> |
>> py |
>> 2 | -11.32026 1131.109 -0.01 0.992 -2228.253 2205.613
>> 3 | -8.486948 1131.109 -0.01 0.994 -2225.419 2208.445
>> 4 | -9.017585 1131.109 -0.01 0.994 -2225.95 2207.915
>> |
>> qasym |
>> 2 | 9.089909 1131.109 0.01 0.994 -2207.843 2226.023
>> 3 | 5.887591 1131.109 0.01 0.996 -2211.045 2222.82
>> 4 | -27.11437 2775.396 -0.01 0.992 -5466.791 5412.562
>> 5 | 20.82242 2262.218 0.01 0.993 -4413.043 4454.688
>> 6 | 18.70797 2262.217 0.01 0.993 -4415.157 4452.573
>> 7 | 0 (omitted)
>> 8 | 18.96654 2262.217 0.01 0.993 -4414.898 4452.831
>> 9 | 0 (omitted)
>> 10 | 0 (omitted)
>> |
>> _cons | 3.091024 .2132027 14.50 0.000 2.673155 3.508894
>> ------------------------------------------------------------------------------
>>
>> I would be grateful for any advice on what I am doing wrong. I am using Stata 12.
>>
>> Thank you,
>> Martyn
>> *
>> * For searches and help try:
>> * http://www.stata.com/help.cgi?search
>> * http://www.stata.com/support/statalist/faq
>> * http://www.ats.ucla.edu/stat/stata/
>>
>>
>>
>> *
>> * For searches and help try:
>> * http://www.stata.com/help.cgi?search
>> * http://www.stata.com/support/statalist/faq
>> * http://www.ats.ucla.edu/stat/stata/
>>
>>
>>
>> *
>> * For searches and help try:
>> * http://www.stata.com/help.cgi?search
>> * http://www.stata.com/support/statalist/faq
>> * http://www.ats.ucla.edu/stat/stata/
*
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