pooledROC.emp {AROC} | R Documentation |
Estimates the pooled ROC curve using the empirical estimator proposed by Hsieh and Turnbull (1996).
pooledROC.emp(y0, y1, p = seq(0, 1, l = 101), B = 500, method = c("ncoutcome", "coutcome"))
y0 |
Diagnostic test outcomes in the healthy group. |
y1 |
Diagnostic test outcomes in the diseased group. |
p |
Set of false positive fractions (FPF) at which to estimate the covariate-adjusted ROC curve. |
B |
An integer value specifying the number of bootstrap resamples for the construction of the confidence intervals. By default 500. |
method |
A character string specifying if bootstrap resampling (for the confidence intervals) should be done with or without regard to the disease status (“coutcome” or “noutcome”). In both cases, a naive bootstrap is used. By default, the resampling is done conditionally on the disease status. |
As a result, the function provides a list with the following components:
call |
the matched call. |
p |
Set of false positive fractions (FPF) at which the pooled ROC curve has been estimated |
ROC |
Estimated pooled ROC curve, and corresponding 95% confidence intervals (if required) |
AUC |
Estimated pooled AUC, and corresponding 95% confidence intervals (if required). |
Hsieh, F., and Turnbull, B.W. (1996). Nonparametric and semiparametric estimation of the receiver operating characteristic curve, The Annals of Statistics, 24, 25-40.
AROC.bnp
, AROC.bsp
, AROC.sp
, AROC.kernel
, pooledROC.BB
or pooledROC.emp
.
library(AROC) data(psa) # Select the last measurement newpsa <- psa[!duplicated(psa$id, fromLast = TRUE),] # Log-transform the biomarker newpsa$l_marker1 <- log(newpsa$marker1) m0_emp <- pooledROC.emp(newpsa$l_marker1[newpsa$status == 0], newpsa$l_marker1[newpsa$status == 1], p = seq(0,1,l=101), B = 500) summary(m0_emp) plot(m0_emp)