[R] logistic regression lrm() output

2011-05-18 Thread array chip 
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 DiscriminationRank Discrim.
 Ratio TestIndexes  Indexes   

Obs   150LR chi2  17.11R2   0.191C   0.763
 0128d.f. 1g1.209Dxy 0.526
 1 22Pr( chi2) 0.0001gr   3.350gamma   0.528
max |deriv| 1e-11  gp   0.129tau-a   0.132
   Brier0.111 

  CoefS.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  LowHigh   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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Re: [R] logistic regression lrm() output

2011-05-18 Thread Frank Harrell 
Why is a one unit change in x an interesting range for the purpose of
estimating an odds ratio?

The default in summary() is the inter-quartile-range odds ratio as clearly
stated in the rms documentation.
Frank

array chip wrote:
 
 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 DiscriminationRank Discrim.
  Ratio TestIndexes  Indexes   
 
 Obs   150LR chi2  17.11R2   0.191C   0.763
  0128d.f. 1g1.209Dxy 0.526
  1 22Pr( chi2) 0.0001gr   3.350gamma   0.528
 max |deriv| 1e-11  gp   0.129tau-a   0.132
Brier0.111 
 
   CoefS.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  LowHigh   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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 http://www.R-project.org/posting-guide.html
 and provide commented, minimal, self-contained, reproducible code.
 


-
Frank Harrell
Department of Biostatistics, Vanderbilt University
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