Hi
Try the following reference:
Comparison of Three Methods for Estimating the
Standard Error of the Area under the Curve in ROC
Analysis of Quantitative Data by Hajian-Tilaki and Hanley, Academic
Radiology, Vol 9, No 11, November 2002.
Below is a simple implementation that will return both the AUC and its
standard error (DeLong et al method).
Hope this helps...
Pedro
#Input: yreal [-1,1]
auc <- function(yreal,forecasts){
sizeT <-nrow(yreal)
pos <- 0
for(i in 1:sizeT){
if(yreal[i]>0) {pos <- pos + 1}
}
neg <- sizeT-pos
yrealpos <- vector(length=pos)
yrealneg <- vector(length=neg)
forepos <- vector(length=pos)
foreneg <- vector(length=neg)
controlpos <- 1
controlneg <- 1
for(i in 1:sizeT){
if(yreal[i]>0) {
yrealpos[controlpos] <- yreal[i]
forepos[controlpos] <- forecasts[i]
controlpos <- controlpos + 1
} else {
yrealneg[controlneg] <- yreal[i]
foreneg[controlneg] <- forecasts[i]
controlneg <- controlneg + 1
}
}
oper <- 0
for( i in 1:pos){
for(j in 1:neg){
if(forepos[i] > foreneg[j]) {oper <- oper + 1}
if(forepos[i]==foreneg[j]) {oper <- oper + 0.50
}
}
}
area <- oper/(pos*neg)
vpj <- vector(length=pos)
vqk <- vector(length=neg)
oper <- 0
for(i in 1:pos){
for(j in 1:neg){
if(forepos[i] > foreneg[j]) {oper <-
oper + 1
} else {if(forepos[i]==foreneg[j]) {oper
<- oper + 0.50 }}
}
division <- oper/neg
resta <- (division-area)^2
vpj[i] <- resta
oper <- 0
}
oper <- 0
resta <- 0
for(j in 1:neg){
for(i in 1:pos){
if(forepos[i] > foreneg[j]) {oper <-
oper + 1
} else {if(forepos[i]==foreneg[j]) {oper
<- oper + 0.50 }}
}
division <- oper/pos
resta <- (division-area)^2
vqk[j] <- resta
oper <- 0
}
vpj <- vpj/(pos*(pos-1))
vqk <- vqk/(neg*(neg-1))
var <- sum(vpj)+sum(vqk)
s <- sqrt(var)
return(list(AUC=area, std=s))
}
-----Original Message-----
From: [EMAIL PROTECTED] [mailto:[EMAIL PROTECTED]
On Behalf Of Frank E Harrell Jr
Sent: Monday, September 08, 2008 8:22 AM
To: gallon li
Cc: r-help
Subject: Re: [R] ROC curve from logistic regression
gallon li wrote:
> I know how to compute the ROC curve and the empirical AUC from the
logistic
> regression after fitting the model.
>
> But here is my question, how can I compute the standard error for the
AUC
> estimator resulting form logistic regression? The variance should be
more
> complicated than AUC based on known test results. Does anybody know a
> reference on this problem?
>
The rcorr.cens function in the Hmisc package will compute the std. error
of Somers' Dxy rank correlation. Dxy = 2*(C-.5) where C is the ROC
area. This standard error does not include a variance component for the
uncertainty in the model (e.g., it does not penalize for the estimation
of the regression coefficients if you are estimating the coefficients
and assessing ROC area on the same sample).
The lrm function in the Design package fits binary and ordinal logistic
regression models and reports C, Dxy, and other measures.
I haven't seen an example where drawing the ROC curve provides useful
information that leads to correct actions.
Frank
--
Frank E Harrell Jr Professor and Chair School of Medicine
Department of Biostatistics Vanderbilt
University
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______________________________________________
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PLEASE do read the posting guide http://www.R-project.org/posting-guide.html
and provide commented, minimal, self-contained, reproducible code.
