On Sat, 5 Jan 2008, xinyi lin wrote: > Hi, > > I want to do a global likelihood ratio test for the proportional odds > logistic regression model and am unsure how to go about it. I am using > the polr() function in library(MASS). > > 1. Is the p-value from the likelihood ratio test obtained by > anova(fit1,fit2), where fit1 is the polr model with only the intercept > and fit2 is the full polr model (refer to example below)? So in the > case of the example below, the p-value would be 1.
There is no improvement in fit, as the near-zero coefficients show. You are not calling polr correctly on this example: 'why is this so?' given that it *is* the example on the help page and all you had to do was to read the help (or the book for which this is support software). > 2. For the model in which there is only one independent variable, I > would expect the Wald test and the likelihood ratio test to give > similar p-values. However the p-values obtained from anova(fit1,fit3) > (refer to example below) are very different (0.0002622986 vs. 1). Why > is this so? Because you compared a t-value to a p-value, not at all the same thing. > > >> library(MASS) >> fit1 <- polr(housing$Sat~1) >> fit2<- polr(housing$Sat~housing$Infl) >> fit3<- polr(housing$Sat~housing$Cont) >> summary(fit1) > > Re-fitting to get Hessian > > Call: > polr(formula = housing$Sat ~ 1) > > No coefficients > > Intercepts: > Value Std. Error t value > Low|Medium -0.6931 0.2500 -2.7726 > Medium|High 0.6931 0.2500 2.7726 > > Residual Deviance: 158.2002 > AIC: 162.2002 >> summary(fit2) > > Re-fitting to get Hessian > > Call: > polr(formula = housing$Sat ~ housing$Infl) > > Coefficients: > Value Std. Error t value > housing$InflMedium 6.347464e-06 0.5303301 1.196889e-05 > housing$InflHigh 6.347464e-06 0.5303301 1.196889e-05 > > Intercepts: > Value Std. Error t value > Low|Medium -0.6931 0.3953 -1.7535 > Medium|High 0.6932 0.3953 1.7536 > > Residual Deviance: 158.2002 > AIC: 166.2002 >> summary(fit3) > > Re-fitting to get Hessian > > Call: > polr(formula = housing$Sat ~ housing$Cont) > > Coefficients: > Value Std. Error t value > housing$ContHigh 0.0001135777 0.4330091 0.0002622986 > > Intercepts: > Value Std. Error t value > Low|Medium -0.6931 0.3307 -2.0956 > Medium|High 0.6932 0.3307 2.0960 > > Residual Deviance: 158.2002 > AIC: 164.2002 >> anova(fit1,fit2) > Likelihood ratio tests of ordinal regression models > > Response: housing$Sat > Model Resid. df Resid. Dev Test Df LR stat. Pr(Chi) > 1 1 70 158.2002 > 2 housing$Infl 68 158.2002 1 vs 2 2 -6.375558e-10 1 >> anova(fit1,fit3) > Likelihood ratio tests of ordinal regression models > > Response: housing$Sat > Model Resid. df Resid. Dev Test Df LR stat. Pr(Chi) > 1 1 70 158.2002 > 2 housing$Cont 69 158.2002 1 vs 2 1 -1.224427e-07 1 > > > Thank you, > Xinyi > > ______________________________________________ > R-help@r-project.org mailing list > https://stat.ethz.ch/mailman/listinfo/r-help > PLEASE do read the posting guide http://www.R-project.org/posting-guide.html > and provide commented, minimal, self-contained, reproducible code. > -- Brian D. Ripley, [EMAIL PROTECTED] Professor of Applied Statistics, http://www.stats.ox.ac.uk/~ripley/ University of Oxford, Tel: +44 1865 272861 (self) 1 South Parks Road, +44 1865 272866 (PA) Oxford OX1 3TG, UK Fax: +44 1865 272595 ______________________________________________ R-help@r-project.org mailing list https://stat.ethz.ch/mailman/listinfo/r-help PLEASE do read the posting guide http://www.R-project.org/posting-guide.html and provide commented, minimal, self-contained, reproducible code.
