[R] logistic regression with a sample missing subjects with a value of an independent variable

[Prof Brian Ripley]

On Sat, 21 Oct 2006, Gabriele Stocco wrote:

> Dear R-help,
> I am trying to make logistic regression analysis using the R function
> "glm", with the parameter family set to binomial, in order to use a
> logistic regression model.
> I have 70 samples. The dependent variables has two levels (0 and 1) and
> one of the independent variables has too two levels (0 and 1).
> The variables associate in the way shown in the table:
>
>            Dependent   0       1
> Independent 0          55      10
>
>            1           0       5
>
> This gives a strong association evaluated by the fisher test (p-value =
> 0.0002481), but with the logistic regression it gives a p-value of 0.99
> with very high values of estimate and standard error (respectively and
> -19.27 and 1769.26).

Please see the comment at the bottom of this message, as your claims are 
not supported by any code.

> Is there any way (other function, different setting of a parameter) to
> perform logistic regression analysis with these data with R?

fit <- glm(matrix(c(55,0,10,5), 2, 2) ~ factor(c(0,1)), binomial())
fit0 <- glm(matrix(c(55,0,10,5), 2, 2) ~ 1,  binomial())
anova(fit0, fit, test="Chisq")

   Resid. Df Resid. Dev Df Deviance P(>|Chi|)
1         1     16.929
2         0  2.208e-10  1   16.929 3.880e-05

is a reasonable way to do this.  Beware the Hauck-Donner phenomenon (see 
e.g. MASS, the book) for t-tests of coefficients, although I do not get 
the values you quote.  Since the expected values are low, you should not 
take the p value too seriously.

> Thank you.
>
> Gabriele Stocco
> University of Trieste
>
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-- 
Brian D. Ripley,                  ripley at stats.ox.ac.uk
Professor of Applied Statistics,  http://www.stats.ox.ac.uk/~ripley/
University of Oxford,             Tel:  +44 1865 272861 (self)
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