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From | "Scholes, Shaun" <s.scholes@ucl.ac.uk> |
To | "statalist@hsphsun2.harvard.edu" <statalist@hsphsun2.harvard.edu> |
Subject | st: RE: Re: Loglinear quasi-symmetric agreement |
Date | Thu, 7 Jun 2012 17:06:52 +0000 |
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/