[R] Major difference in the outcome between SPSS and R statisticalprograms
Doran, Harold
HDoran at air.org
Fri Aug 1 17:35:42 CEST 2008
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: r-help-bounces at r-project.org
> [mailto:r-help-bounces at r-project.org] On Behalf Of Draga, R.
> Sent: Friday, August 01, 2008 10:19 AM
> To: r-help at 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]]
>
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