On Fri, Aug 1, 2008 at 10:56 AM, Doran, Harold <[EMAIL PROTECTED]> wrote: > First off, Marc Schwartz posted this link earlier today, read it. > > http://cran.r-project.org/doc/FAQ/R-FAQ.html#Why-are-p_002dvalues-not-di > splayed-when-using-lmer_0028_0029_003f > > Second, your email is not really descriptive enough. I have no idea what > OR is, so I have no reaction.
Perhaps OR is "odds ratio". In a generalized linear model or a generalized linear mixed model for binary responses and using the logit link, the exponentials of the coefficients are scale factors for the odds ratio. > Third, you're comparing estimates from different methods of estimation. > lmer will give standard errors that account for the correlation of > individuals within similar units whereas the SPSS procedure will not. > The lmer standard errors better capture the true sampling variance of > the parameters and SPSS doesn't. > > > >> -----Original Message----- >> From: Draga, R. [mailto:[EMAIL PROTECTED] >> Sent: Friday, August 01, 2008 11:45 AM >> To: Doran, Harold >> Subject: RE: [R] Major difference in the outcome between SPSS >> and R statisticalprograms >> >> Thanks for the reaction >> >> I know, I would not expect the outcomes to be the same. >> But, I have never before encountered such a difference in >> outcomes between SPSS and R; mostly the OR's and p-values >> differed a little bit. >> >> Strange is that R shows a OR of 10,176 and 95% CI of >> 6,295-14,056. Then the p-value must be <0.05 doesn't it? >> For age the OR's differ dramatically between SPSS and R, >> 0.985 and 0.003. >> >> I just can not explain it. >> >> Ronald >> >> -----Oorspronkelijk bericht----- >> Van: Doran, Harold [mailto:[EMAIL PROTECTED] >> Verzonden: vrijdag 1 augustus 2008 17:36 >> Aan: Draga, R.; r-help@r-project.org >> Onderwerp: RE: [R] Major difference in the outcome between >> SPSS and R statisticalprograms >> >> >> The biggest problem is that SPSS cannot fit a generalized linear mixed >> model but lmer does. So, why would you expect the GLM in SPSS and the >> GLMM in lmer to match anyhow? >> >> > -----Original Message----- >> > From: [EMAIL PROTECTED] >> > [mailto:[EMAIL PROTECTED] On Behalf Of Draga, R. >> > Sent: Friday, August 01, 2008 10:19 AM >> > To: r-help@r-project.org >> > Subject: [R] Major difference in the outcome between SPSS and >> > R statisticalprograms >> > >> > Dear collegues, >> > >> > I have used R statistical program, package 'lmer', several >> > times already. >> > I never encountered major differences in the outcome between >> > SPSS and R. >> > ...untill my last analyses. >> > >> > Would some know were the huge differences come from. >> > >> > Thanks in advance, Ronald >> > >> > In SPSS the Pearson correlation between variable 1 and >> > variable 2 is 31% p<0.001. >> > >> > >> > >> > In SPSS binary logistic regression gives us an OR=4.9 (95% CI >> > 2.7-9.0), p<0.001, n=338. >> > >> > OR lower upper >> > >> > gender 1,120 0,565 2,221 >> > >> > age 0,985 0,956 1,015 >> > >> > variable 2 4,937 2,698 9,032 >> > >> > >> > >> > In R multilevel logistic regression using statistical >> package 'lmer' >> > gives us an OR=10.2 (95% CI 6.3-14), p=0.24, n=338, groups: >> group 1, >> > 98; group 2 84. >> > >> > OR lower upper >> > >> > gender 2,295 -2,840 7,430 >> > >> > age 0,003 -70,047 70,054 >> > >> > variable 2 10,176 6,295 14,056 >> > >> > >> > >> > The crosstabs gives us: >> > >> > variable A >> > >> > Var B 0 1 >> > >> > 0 156 108 >> > >> > 1 17 57 >> > >> > >> > >> > Would somebody know how it is possible that in SPSS we get >> > p<0.001 and in R we get p=0.24? >> > >> > >> > [[alternative HTML version deleted]] >> > >> > ______________________________________________ >> > 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. >> > >> > > _______________________________________________ > [EMAIL PROTECTED] mailing list > https://stat.ethz.ch/mailman/listinfo/r-sig-mixed-models > ______________________________________________ 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.