Hi all Assume I have a data set xx; Group: 1=group1 ?, 2=group2 IQ: ?1= High, 0 =low fit <- glm(IQ ~group, data = xx, family = binomial()) summary(fit) Results ?????? ????????????Estimate Std. Error z value Pr(>|z|) (Intercept) -2.55456??? 0.210 -12.273? < 5e-16 *** group????????? 0.36180 ?????0.076?? 3.952 ????5.24e-05 *** the odd ratio = exp(0.36180 )= 1.435912 My question is that the log-odd ?estimate 0.3618 ?is it for group1 or group2? What does the odd ratio 1.43359 is interpreted? Thanks in advance
Hi val, Val a ?crit :> Hi all > > Assume I have a data set xx; > > Group: 1=group1 , 2=group2 > > IQ: 1= High, 0 =low > > fit <- glm(IQ ~group, data = xx, family = binomial()) > > summary(fit) > > Results > > Estimate Std. Error z value Pr(>|z|) > > (Intercept) -2.55456 0.210 -12.273 < 5e-16 *** > > group 0.36180 0.076 3.952 5.24e-05 *** > > the odd ratio = exp(0.36180 )= 1.435912 > > My question is that the log-odd estimate 0.3618 is it for group1 or group2? >normally 1vs2, glm takes 2 as reference, in the group1 the IQ increase by 0.3618compared to group 2> What does the odd ratio 1.43359 is interpreted? >in the group1 the IQ score increase by 1.43359 compared to group 2> Thanks in advance >Regards ML> ______________________________________________ > R-help at 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. > >-- Mohamed Lajnef INSERM Unit? 955. 40 rue de Mesly. 94000 Cr?teil. Courriel : Mohamed.lajnef at inserm.fr tel.: 01 49 81 31 31 (poste 18470) Sec : 01 49 81 32 90 fax : 01 49 81 30 99 Portable:06 15 60 01 62
Thanks for your response.
Do you mean that both the log-odds and odd ratio have the same meaning?
My question is that the log-odd estimate 0.3618 is it for group1 or group2?
normally 1vs2, glm takes 2 as reference, in the group1 the IQ increase
by 0.3618compared to group 2
What does the odd ratio 1.43359 is interpreted?
in the group1 the IQ score increase by 1.43359 compared to group 2
On Mon, Jan 25, 2010 at 10:05 AM, Mohamed Lajnef
<Mohamed.lajnef at inserm.fr> wrote:> Hi val,
>
>
> Val a ?crit :
>>
>> Hi all
>>
>> Assume I have a data set xx;
>>
>> Group: 1=group1 ?, 2=group2
>>
>> IQ: ?1= High, 0 =low
>>
>> fit <- glm(IQ ~group, data = xx, family = binomial())
>>
>> summary(fit)
>>
>> Results
>>
>> ? ? ? ? ? ? ? ? ? Estimate Std. Error z value Pr(>|z|)
>>
>> (Intercept) -2.55456 ? ?0.210 -12.273 ?< 5e-16 ***
>>
>> ?group ? ? ? ? ?0.36180 ? ? ?0.076 ? 3.952 ? ? 5.24e-05 ***
>>
>> the odd ratio = exp(0.36180 )= 1.435912
>>
>> My question is that the log-odd ?estimate 0.3618 ?is it for group1 or
>> group2?
>>
>
> normally 1vs2, glm takes 2 as reference, in the group1 the IQ increase by
> 0.3618compared to group 2
>
>> What does the odd ratio 1.43359 is interpreted?
>>
>
> in the group1 the IQ score ?increase by 1.43359 ?compared to group 2
>
>> Thanks in advance
>>
>
> Regards
> ML
>>
>> ______________________________________________
>> R-help at 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.
>>
>>
>
>
> --
> Mohamed Lajnef
> INSERM Unit? 955. 40 rue de Mesly. 94000 Cr?teil.
> Courriel : Mohamed.lajnef at inserm.fr tel.: 01 49 81 31 31 (poste 18470)
> Sec : 01 49 81 32 90
> fax : 01 49 81 30 99 Portable:06 15 60 01 62
>
>
Val wrote:> Hi all > > Assume I have a data set xx; > > Group: 1=group1 , 2=group2 > > IQ: 1= High, 0 =low > > fit <- glm(IQ ~group, data = xx, family = binomial()) > > summary(fit) > > Results > > Estimate Std. Error z value Pr(>|z|) > > (Intercept) -2.55456 0.210 -12.273 < 5e-16 *** > > group 0.36180 0.076 3.952 5.24e-05 *** > > the odd ratio = exp(0.36180 )= 1.435912 > > My question is that the log-odd estimate 0.3618 is it for group1 or group2? > > What does the odd ratio 1.43359 is interpreted?Val, Before using R's model fitting functions, it helps to understand your model. See any introductory text on logistic regression. Despite what you claim, it appears that your data 'set' may be a data.frame with variables 'IQ' and 'group', something like this: set.seed(34) xx <- data.frame(IQ = sample(0:1, 10, TRUE), group = gl(2, 5)) xx The summary you show was not produced by R, at least not as you show it. Here's the result for the above data: fit <- glm(IQ ~ group, data=xx, family=binomial()) summary(fit) ## snipped R output Coefficients: Estimate Std. Error z value Pr(>|z|) (Intercept) -0.4055 0.9129 -0.444 0.657 group2 0.8109 1.2910 0.628 0.530 ## end R output Note the '2' in 'group2'. R is smart. It let's you know which level of factor 'group' should get the added 0.8109 in its log-odds estimate. From your reported output it would be impossible to tell that. You might have set 'group' to have level '2' as the reference level, in which case R would show a 'group1' row. For more on logistic regression, you could consult Wikipedia, but here's a brief explanation of your simple case: Consider two models, one for each group: log(Pr(IQ=1)/Pr(IQ=0)) = const_1 (group 1) log(Pr(IQ=1)/Pr(IQ=0)) = const_2 (group 2) Combine these into a single model, using an indicator variable to signal the group: log(Pr(IQ=1)/Pr(IQ=0)) = beta_0 + beta_1 * Indic(group 2) where Indic(group 2) = 1 for group 2 and 0 otherwise and beta_0 = const_1, beta_0 + beta_1 = const_2. This should help you answer your questions yourself. - Peter Ehlers> > Thanks in advance > > ______________________________________________ > R-help at 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. > >-- Peter Ehlers University of Calgary