You should look up the Hauck-Donne phenomenon, which shows that
with binomial GLMs, the standard error can grow faster than
the effect size. Complete separation results, for example,
when one predictor (or a combination of several predictors)
perfectly predicts the response. Something like this seems
to be happening for variables 4 and 5. You could try the
brglm function from the package of the same name, which
uses bias correction. Compare (after coercing your Data to
a data frame):
summary(glm(Y ~ ., binomial, Data))
Call:
glm(formula = Y ~ ., family = binomial, data = Data)
Deviance Residuals:
Min 1Q Median 3Q Max
-2.00979 0.00000 0.00006 0.27987 1.82302
Coefficients:
Estimate Std. Error z value Pr(>|z|)
(Intercept) 10.99326 20.77336 0.529 0.5967
`X 1` 0.01943 0.01040 1.868 0.0617 .
`X 2` 10.61013 5.65409 1.877 0.0606 .
`X 3` -0.66763 0.47668 -1.401 0.1613
`X 4` 70.98785 36.41181 1.950 0.0512 .
`X 5` 17.33126 2872.17069 0.006 0.9952
summary(brglm(Y ~ ., binomial, Data))
Call:
brglm(formula = Y ~ ., family = binomial, data = Data)
Coefficients:
Estimate Std. Error z value Pr(>|z|)
(Intercept) 12.017791 14.337183 0.838 0.4019
`X 1` 0.014898 0.008263 1.803 0.0714 .
`X 2` 8.307941 4.010792 2.071 0.0383 *
`X 3` -0.576309 0.352097 -1.637 0.1017
`X 4` 35.627644 16.638766 2.141 0.0323 *
`X 5` 2.134544 2.570756 0.830 0.4064
Good luck.
Ken
Quoting Christofer Bogaso <bogaso.christo...@gmail.com>:
Thanks Ken for your reply. No doubt your english is quite tough!! I
understand something is not normal with the 5th explanatory variable
(se:2872.17069!) However could not understand what you mean by "You
seem to be getting complete separation on X5 "?
Can you please be more elaborate?
Thanks,
On Thu, Apr 12, 2012 at 4:06 PM, ken knoblauch
<ken.knobla...@inserm.fr> wrote:
Christofer Bogaso <bogaso.christofer <at> gmail.com> writes:
Dear all, I am fitting a LOGIT model on this Data...........
---- << snip >>---
glm(Data[,1] ~ Data[,-1], binomial(link = logit))
Call: glm(formula = Data[, 1] ~ Data[, -1], family =
binomial(link = logit))
Coefficients:
(Intercept) Data[, -1]X 1 Data[, -1]X 2 Data[, -1]X 3 Data[,
-1]X 4 Data[, -1]X 5
10.99326 0.01943 10.61013 -0.66763
70.98785 17.33126
Degrees of Freedom: 43 Total (i.e. Null); 38 Residual
Null Deviance: 44.58
Residual Deviance: 17.46 AIC: 29.46
Warning message:
glm.fit: fitted probabilities numerically 0 or 1 occurred
However I am getting a warning mesage as "fitted probabilities
numerically 0 or 1 occurred". Here my question is,
have I made any
mistakes with my above implementation? I
s it just because, I have too
less number of '0' in my response Variable?
Look at the output of summary, especially the standard errors.
You seem to be getting complete
separation on X5 and X4 doesn,'t look so hot either.
Ken
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Ken Knoblauch
Inserm U846
Stem-cell and Brain Research Institute
Department of Integrative Neurosciences
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