> On 06 Feb 2015, at 18:25 , Mohamed Farah <m.fa...@sc.qa> wrote:
>
> Peter,
>
> Appreciate the comment. Here is a summary table. Both variables (Profit and
> dividend with 0=yes & 1=no) are binary as pointed out. I have out of 368
> companies of which 342 were profitable and 26 unprofitable. Of the 342, 79
> paid no dividends and 263 paid dividends. Of the 26, 25 paid no dividends and
> 1 paid dividends. The result is 104 dividend payers and 264 payers.
>
>
> DIV
> Profit
> 0 1 Grand Total
> 0 25 1 26
> 1 79 263 342
> Grand Total 104 264 368
This doesn't quite fit with the 376 df for the null deviance in your glm().
However, the OR for that table is 25*263/(79*1) = 83.23, which isn't far off.
However, something is strange. Your glm() output had odds for one group at
0.03508772, but
> 1/29
[1] 0.03448276
> 1/28
[1] 0.03571429
However
> 2/57
[1] 0.03508772
and the odds for the other group should be
> .03508772*87.69230769
[1] 3.076923
which is pretty much exactly 40/13
> 40/13
[1] 3.076923
Now, to fit the 376 null df, I'd expect 377 obs total. That fits if the other
group is actually 240:78, so that the entire table is
57 2
78 240
And, lo and behold:
> x <- rep(0:1, c(59,318))
> y <- rep(c(0,1,0,1),c(57,2,78,240))
> summary(glm(y~x, binomial))
Call:
glm(formula = y ~ x, family = binomial)
Deviance Residuals:
Min 1Q Median 3Q Max
-1.6765 -0.2626 0.7502 0.7502 2.6017
Coefficients:
Estimate Std. Error z value Pr(>|z|)
(Intercept) -3.3499 0.7194 -4.656 3.22e-06 ***
x 4.4738 0.7311 6.119 9.41e-10 ***
---
Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1
(Dispersion parameter for binomial family taken to be 1)
Null deviance: 491.84 on 376 degrees of freedom
Residual deviance: 371.78 on 375 degrees of freedom
AIC: 375.78
-pd
>
>
>
>
> ________________________________________
> From: peter dalgaard [pda...@gmail.com]
> Sent: Friday, February 06, 2015 7:35 PM
> To: Michael Dewey
> Cc: Mohamed Farah; r-help@r-project.org
> Subject: Re: [R] Interpreting a Logit regression result
>
> On 06 Feb 2015, at 16:58 , Michael Dewey <i...@aghmed.fsnet.co.uk> wrote:
>
> > Dear Mohamed
> >
> > Your dataset did not make it through, the list strips most attachments.
> >
> > In my area of application I would be suspicious that such an odds ratio was
> > the result of a data error or my misunderstanding of the underlying
> > science. You are probably in the best position to judge both of these in
> > your area.
> >
>
> If both variables are binary, a table would be informative:
>
> with(divs, table(Div, PRFD))
>
> The output is roughly consistent with odds 1:30 if PRFD==0 and 3:1 if
> PRFD==1. That sounds extreme, but not entirely implausible, depending on
> field of application.
>
>
> > Michael
> >
> >
> > On 06/02/2015 07:42, Mohamed Farah wrote:
> >> I have run a logit regression with two categorical variables (with 0 and
> >> 1) as the values. i.e. payment (1) / non-payment(0) on profit (profitable
> >> =1, non-profitable=0) on 375 entities. Here is the result from R:
> >>
> >>
> >>
> >>> divgress <-glm(Div~PRFD, family=binomial(link="logit"), data=divs)
> >>> summary(divgress)
> >>
> >> Call:
> >> glm(formula = Div ~ PRFD, family = binomial(link = "logit"),
> >> data = divs)
> >>
> >> Deviance Residuals:
> >> Min 1Q Median 3Q Max
> >> -1.6765 -0.2626 0.7502 0.7502 2.6017
> >>
> >> Coefficients:
> >> Estimate Std. Error z value Pr(>|z|)
> >> (Intercept) -3.3499 0.7194 -4.656 3.22e-06 ***
> >> PRFD 4.4738 0.7311 6.119 9.41e-10 ***
> >> ---
> >> Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1
> >>
> >> (Dispersion parameter for binomial family taken to be 1)
> >>
> >> Null deviance: 491.84 on 376 degrees of freedom
> >> Residual deviance: 371.78 on 375 degrees of freedom
> >> AIC: 375.78
> >>
> >> Number of Fisher Scoring iterations: 6
> >>
> >>
> >>
> >> My question is that the coefficient of the independent variable (log-odds)
> >> at 4.4738 is difficult to interpret. I have obtained the exponent of the
> >> coefficient below and as the result of 87.69.. shown below shows, the
> >> number is high which makes suspicious that there is something not working
> >> right.
> >>
> >>
> >>
> >>> exp(coef(divgress))
> >> (Intercept) PRFD
> >> 0.03508772 87.69230769
> >>>
> >>
> >>
> >>
> >> The dataset is attached. I appreciate your help.
> >>
> >>
> >>
> >>
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> > --
> > Michael
> > http://www.dewey.myzen.co.uk
> >
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> --
> Peter Dalgaard, Professor,
> Center for Statistics, Copenhagen Business School
> Solbjerg Plads 3, 2000 Frederiksberg, Denmark
> Phone: (+45)38153501
> Email: pd....@cbs.dk Priv: pda...@gmail.com
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--
Peter Dalgaard, Professor,
Center for Statistics, Copenhagen Business School
Solbjerg Plads 3, 2000 Frederiksberg, Denmark
Phone: (+45)38153501
Email: pd....@cbs.dk Priv: pda...@gmail.com
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