Dear all:
"predict.glm" provides an example to perform logistic regression when the
response variable is a tow-columned matrix. I find some paradox about the
degree of freedom .
> summary(budworm.lg)
Call:
glm(formula = SF ~ sex * ldose, family = binomial)
Deviance Residuals:
Min 1Q Median 3Q Max
-1.39849 -0.32094 -0.07592 0.38220 1.10375
Coefficients:
Estimate Std. Error z value Pr(>|z|)
(Intercept) -2.9935 0.5527 -5.416 6.09e-08 ***
sexM 0.1750 0.7783 0.225 0.822
ldose 0.9060 0.1671 5.422 5.89e-08 ***
sexM:ldose 0.3529 0.2700 1.307 0.191
---
Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1
(Dispersion parameter for binomial family taken to be 1)
Null deviance: 124.8756 on 11 degrees of freedom
Residual deviance: 4.9937 on 8 degrees of freedom
AIC: 43.104
Number of Fisher Scoring iterations: 4
This is the data set used in regression:
numdead numalive sex ldose
1 1 19 M 0
2 4 16 M 1
3 9 11 M 2
4 13 7 M 3
5 18 2 M 4
6 20 0 M 5
7 0 20 F 0
8 2 18 F 1
9 6 14 F 2
10 10 10 F 3
11 12 8 F 4
12 16 4 F 5
The degree of freedom is 8. Each row in the example is thought to be
one observation. If I extend it to be a three column data.frame, the first
denoting the whether the individual is alive , the secode denoting the sex,
and the third "ldose",there will be 12*20=240 observations.
Since my data set is one of the second type , I wish to know whether
the form of data set affects the result of regression ,such as the degree of
freedom.
Dose anybody have any idea about this? Thank all who read this message.
Regards,
Bin Yue
-----
Best regards,
Bin Yue
*************
student for a Master program in South Botanical Garden , CAS
--
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