Hi: John Fox's recode function in his car package provides a convenient
way for doing what
you need. I don't know what your factor is specifically but below is
mostly taken out
of the help for "recode" and shows how to take a factor and recode it to
make it a new
factor. you can apply that for your particular problem.
x <- gl(3,3,length=9)
print(x)
print(str(x))
temp <- recode(x,"1:2 = 'A'; 3 = 'B'")
print(temp)
print(str(temp))
On Wed, 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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and provide commented, minimal, self-contained, reproducible code.
