Hi, I am trying to run a simple logistic regression using lrm() to calculate a
odds ratio. I found a confusing output when I use summary() on the fit object
which gave some OR that is totally different from simply taking
exp(coefficient), see below:
> dat<-read.table("dat.txt",sep='\t',header=T,row.names=NULL)
> d<-datadist(dat)
> options(datadist='d')
> library(rms)
> (fit<-lrm(response~x,data=dat,x=T,y=T))
Logistic Regression Model
lrm(formula = response ~ x, data = dat, x = T, y = T)
Model Likelihood Discrimination Rank Discrim.
Ratio Test Indexes Indexes
Obs 150 LR chi2 17.11 R2 0.191 C 0.763
0 128 d.f. 1 g 1.209 Dxy 0.526
1 22 Pr(> chi2) <0.0001 gr 3.350 gamma 0.528
max |deriv| 1e-11 gp 0.129 tau-a 0.132
Brier 0.111
Coef S.E. Wald Z Pr(>|Z|)
Intercept -5.0059 0.9813 -5.10 <0.0001
x 0.5647 0.1525 3.70 0.0002
As you can see, the odds ratio for x is exp(0.5647)=1.75892.
But if I run the following using summary():
> summary(fit)
Effects Response : response
Factor Low High Diff. Effect S.E. Lower 0.95 Upper 0.95
x 3.9003 6.2314 2.3311 1.32 0.36 0.62 2.01
Odds Ratio 3.9003 6.2314 2.3311 3.73 NA 1.86 7.49
What are these output? none of the numbers is the odds ratio (1.75892) that I
calculated by using exp().
Can any explain?
Thanks
John
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