logistic regression by glm

Thank you Ligges :)
one more question:
my response value "diagnostic" have 4 levels (0, 1, 2 and 3), so I use it like this:
"as.factor(diagnostic) ~ as.factor(7161521) +as.factor(2281517)"
Is it all right?

? 2011-11-20 23:45:23?"Uwe Ligges"<ligges at statistik.tu-dortmund.de>  ???

On 20.11.2011 12:46, tujchl wrote:

HI

I use glm in R to do logistic regression. and treat both response and
predictor as factor
In my first try:

*******************************************************************************
Call:
glm(formula = as.factor(diagnostic) ~ as.factor(7161521) +
as.factor(2281517), family = binomial())

Deviance Residuals:
Min 1Q Median 3Q Max
-1.5370 -1.0431 -0.9416 1.3065 1.4331

Coefficients:
Estimate Std. Error z value Pr(>|z|)
(Intercept) -0.58363 0.27948 -2.088 0.0368 *
as.factor(7161521)2 1.39811 0.66618 2.099 0.0358 *
as.factor(7161521)3 0.28192 0.83255 0.339 0.7349
as.factor(2281517)2 -1.11284 0.63692 -1.747 0.0806 .
as.factor(2281517)3 -0.02286 0.80708 -0.028 0.9774
---
Signif. codes: 0 ?***? 0.001 ?**? 0.01 ?*? 0.05 ?.? 0.1 ? ? 1

(Dispersion parameter for binomial family taken to be 1)

Null deviance: 678.55 on 498 degrees of freedom
Residual deviance: 671.20 on 494 degrees of freedom
AIC: 681.2

Number of Fisher Scoring iterations: 4
*******************************************************************************

And I remodel it and *want no intercept*:
*******************************************************************************
Call:
glm(formula = as.factor(diagnostic) ~ as.factor(2281517) +
as.factor(7161521) - 1, family = binomial())

Deviance Residuals:
Min 1Q Median 3Q Max
-1.5370 -1.0431 -0.9416 1.3065 1.4331

Coefficients:
Estimate Std. Error z value Pr(>|z|)
as.factor(2281517)1 -0.5836 0.2795 -2.088 0.0368 *
as.factor(2281517)2 -1.6965 0.6751 -2.513 0.0120 *
as.factor(2281517)3 -0.6065 0.8325 -0.728 0.4663
as.factor(7161521)2 1.3981 0.6662 2.099 0.0358 *
as.factor(7161521)3 0.2819 0.8325 0.339 0.7349
---
Signif. codes: 0 ?***? 0.001 ?**? 0.01 ?*? 0.05 ?.? 0.1 ? ? 1

(Dispersion parameter for binomial family taken to be 1)

Null deviance: 691.76 on 499 degrees of freedom
Residual deviance: 671.20 on 494 degrees of freedom
AIC: 681.2

Number of Fisher Scoring iterations: 4
*******************************************************************************

*As show above in my second model it return no intercept but look this:
Model1:
(Intercept) -0.58363 0.27948 -2.088 0.0368 *
Model2:
as.factor(2281517)1 -0.5836 0.2795 -2.088 0.0368 **

They are exactly the same. Could you please tell me what happen?

Actually it does not make sense to estimate the model without an
intercept unless you assume that it is exactly zero for the first levels
of your factors. Think about the contrasts you are interested in. Looks
like not the default?

Uwe Ligges

Thank you in advance


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