[R] Likelihood ratio test in porl (MASS)

[Faradj Koliev]

Dear Achim Zeileis, dear John Fox,

Thank you for your time! Both worked well. 

lrtest(Restrict, Full)

  #Df  LogLik Df  Chisq Pr(>Chisq)    
1  27 -882.00                         
2  28 -866.39  1 31.212  2.313e-08 ***


anova(Restrict, Full)

  Resid. df Resid. Dev   Test    Df LR stat.      Pr(Chi)
1      2121   1763.999                                   
2      2120   1732.787 1 vs 2     1 31.21204 2.313266e-08



And both seems to reject the null hypothesis.  Thanks again! 

Best, 
Faradj








> 27 jul 2016 kl. 13:35 skrev Fox, John <jfox at mcmaster.ca>:
> 
> Dear Faradj Koliev,
> 
> There is an anova() method for "polr" objects that computes LR chisquare tests for nested models, so a short answer to your question is anova(Full, Restricted).
> 
> The question, however, seems to reflect some misunderstandings. First aov() fits linear analysis-of-variance models, which assume normally distributed errors. These are different from the ordinal regression models, such as the proportional-odds model, fit by polr(). For the former, F-tests *are* LR tests; for the latter, F-tests aren't appropriate.
> 
> I hope this helps,
> John
> 
> -----------------------------
> John Fox, Professor
> McMaster University
> Hamilton, Ontario
> Canada L8S 4M4
> Web: socserv.mcmaster.ca/jfox
> 
> 
> 
> 
>> -----Original Message-----
>> From: R-help [mailto:r-help-bounces at r-project.org] On Behalf Of Faradj Koliev
>> Sent: July 27, 2016 4:50 AM
>> To: r-help at r-project.org
>> Subject: [R] Likelihood ratio test in porl (MASS)
>> 
>> Dear all,
>> 
>> A quick question: Let’s say I have a full and a restricted model that looks
>> something like this:
>> 
>> Full<- polr(Y ~ X1+X2+X3+X4, data=data, Hess = TRUE,  method="logistic”) #
>> ordered logistic regression
>> 
>> Restricted<- polr(Y ~ X1+X2+X3, data=data, Hess = TRUE,  method="logistic”) #
>> ordered logistic regression
>> 
>> I wanted to conduct the F-test (using aov command) in order to determine
>> whether the information from the X4 variable statistically improves our
>> understanding of Y.
>> However, I’ve been told that the likelihood ratio test is a better alternative. So,
>> I would like to conduct the LR test. In rms package this is easy -- lrest(Full,
>> Restricted) — I’m just curious how to perform the same using polr. Thanks!
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>> 
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