# [R] logistic regression by glm

Uwe Ligges ligges at statistik.tu-dortmund.de
Sun Nov 20 16:45:23 CET 2011

```
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

>
>
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