On Fri, 6 Jun 2014 11:16:11 AM Nwinters wrote:
> I have a variable coded in Stata as follows:
> **
> *gen sat_pm25cat_=.
> replace sat_pm25cat_= 1 if (sat_pm25>=4 & sat_pm25<=7.1 &
sat_pm25!=.)
> replace sat_pm25cat_= 2 if (sat_pm25>=7.1 & sat_pm25<=10)
> replace sat_pm25cat_= 3 if (sat_pm25>=10.1 &
sat_pm25<=11.3)
> replace sat_pm25cat_= 4 if (sat_pm25>=11.4 &
sat_pm25<=12.1)
> replace sat_pm25cat_= 5 if (sat_pm25>=12.2 &
sat_pm25<=17.1)
>
> gen satpm25catR= "A" if sat_pm25cat_==1
> replace satpm25catR= "B" if sat_pm25cat_==2
> replace satpm25catR= "C" if sat_pm25cat_==3
> replace satpm25catR= "D" if sat_pm25cat_==4
> replace satpm25catR= "E" if sat_pm25cat_==5
> ***
>
> my model for R is:
> ##
> *glm.PM25linB <-glm(leuk ~ satpm25catR + sex + ageR,
data=leuk,
> family=binomial, epsilon=1e-15, maxit=1000)*
> ##
>
> In the summary, satpm25catR is being reported as all levels:
>
>
<http://r.789695.n4.nabble.com/file/n4691823/Screen_Shot_2014-06-06_at_2.png
> >
>
> *What I want is to make "A" the reference level, how do I do this??*
Hi Nwinters,
I get what you want with this example:
leukdf<-
data.frame(leuk=sample(0:1,100,TRUE),sat_pm25=runif(100,0,17.1),
sex=sample(c("M","F"),100,TRUE),ageR=sample(20:75,100,TRUE))
leukdf$satpm25catR<-factor(NA,levels=LETTERS[1:5])
leukdf$satpm25catR<-factor(rep(NA,100),levels=LETTERS[1:5])
leukdf$satpm25catR[leukdf$sat_pm25 < 7.1]<-"A"
leukdf$satpm25catR[leukdf$sat_pm25 >= 7.1 &
leukdf$sat_pm25 < 10.1]<-"B"
leukdf$satpm25catR[leukdf$sat_pm25 >= 10.1 &
leukdf$sat_pm25 < 11.3]<-"C"
leukdf$satpm25catR[leukdf$sat_pm25 >= 11.3 &
leukdf$sat_pm25 < 12.1]<-"D"
leukdf$satpm25catR[leukdf$sat_pm25 >= 12.1 &
leukdf$sat_pm25 < 17.1]<-"E"
summary(glm(leuk ~ satpm25catR + sex + ageR, data=leukdf,
family=binomial, epsilon=1e-15, maxit=1000))
Call:
glm(formula = leuk ~ satpm25catR + sex + ageR, family = binomial,
data = leukdf, epsilon = 1e-15, maxit = 1000)
Deviance Residuals:
Min 1Q Median 3Q Max
-1.4813 -1.1798 0.7631 1.1347 1.5195
Coefficients:
Estimate Std. Error z value Pr(>|z|)
(Intercept) 1.67565 0.87205 1.922 0.0547
satpm25catRB -0.52289 0.58578 -0.893 0.3721
satpm25catRC -0.79998 0.78405 -1.020 0.3076
satpm25catRD -0.36488 0.88162 -0.414 0.6790
satpm25catRE -0.65372 0.51461 -1.270 0.2040
sexM -0.54063 0.42073 -1.285 0.1988
ageR -0.02095 0.01455 -1.440 0.1500
(Dispersion parameter for binomial family taken to be 1)
Null deviance: 138.59 on 99 degrees of freedom
Residual deviance: 133.74 on 93 degrees of freedom
AIC: 147.74
Number of Fisher Scoring iterations: 5
It may be a problem with the way you have calculated the categorical
variable as David noted. However, if you haven't read a paper I had
published a few years ago titled "On the perils of categorizing
responses", you might want to have a look.
Jim
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