R question - combine values

The district a is the baseline and we observe the difference between
District a & b is not significant, we can choose to combine these 2 values.
How to write code to combine these 2 value ?
m1=glm(Claims~District+Group+Age+log(Holders),fami ly=poisson,data=mydata)
summary(m1)
Call:
glm(formula = Claims ~ District + Group + Age + log(Holders),
family = poisson, data = mydata)

Deviance Residuals:
Min 1Q Median 3Q Max
-2.553115 -0.471819 0.002411 0.455274 1.800739

Coefficients:
Estimate Std. Error z value Pr(>|z|)
(Intercept) -2.777752 0.689162 -4.031 5.56e-05 ***
Districtb 0.119942 0.079861 1.502 0.133125
Districtc 0.228371 0.144503 1.580 0.114019
Districtd 0.571661 0.248792 2.298 0.021576 *
Group>2l 0.794721 0.180354 4.406 1.05e-05 ***
Group1-1.5l -0.003496 0.127947 -0.027 0.978202
Group1.5-2l 0.379190 0.055856 6.789 1.13e-11 ***
Age>35 -1.074971 0.389480 -2.760 0.005780 **
Age25-29 -0.332131 0.129512 -2.564 0.010333 *
Age30-35 -0.539815 0.160138 -3.371 0.000749 ***
log(Holders) 1.201696 0.144135 8.337 < 2e-16 ***
---
Signif. codes: 0 ?***? 0.001 ?**? 0.01 ?*? 0.05 ?.? 0.1 ? ? 1

(Dispersion parameter for poisson family taken to be 1)

Null deviance: 4236.68 on 63 degrees of freedom
Residual deviance: 49.45 on 53 degrees of freedom
AIC: 388.77
You don't.

And even if you do get someone to tell you how, you may still not  
legitimately lower your degrees of freedom. Friends don't let friends  
use stepwise approaches to regression analysis.
David Winsemius

On Feb 25, 2009, at 10:33 PM, choonhong ang wrote:

> The district a is the baseline and we observe the difference between
> District a & b is not significant, we can choose to combine these 2  
> values.
> How to write code to combine these 2 value ?
>
>> m1=glm(Claims~District+Group+Age+log(Holders),fami  
>> ly=poisson,data=mydata)
>> summary(m1)
>
> Call:
> glm(formula = Claims ~ District + Group + Age + log(Holders),
> family = poisson, data = mydata)
>
> Deviance Residuals:
> Min 1Q Median 3Q Max
> -2.553115 -0.471819 0.002411 0.455274 1.800739
>
> Coefficients:
> Estimate Std. Error z value Pr(>|z|)
> (Intercept) -2.777752 0.689162 -4.031 5.56e-05 ***
> Districtb 0.119942 0.079861 1.502 0.133125
> Districtc 0.228371 0.144503 1.580 0.114019
> Districtd 0.571661 0.248792 2.298 0.021576 *
> Group>2l 0.794721 0.180354 4.406 1.05e-05 ***
> Group1-1.5l -0.003496 0.127947 -0.027 0.978202
> Group1.5-2l 0.379190 0.055856 6.789 1.13e-11 ***
> Age>35 -1.074971 0.389480 -2.760 0.005780 **
> Age25-29 -0.332131 0.129512 -2.564 0.010333 *
> Age30-35 -0.539815 0.160138 -3.371 0.000749 ***
> log(Holders) 1.201696 0.144135 8.337 < 2e-16 ***
> ---
> Signif. codes: 0 ?***? 0.001 ?**? 0.01 ?*? 0.05 ?.? 0.1 ? ? 1
>
> (Dispersion parameter for poisson family taken to be 1)
>
> Null deviance: 4236.68 on 63 degrees of freedom
> Residual deviance: 49.45 on 53 degrees of freedom
> AIC: 388.77
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