SROC | R Documentation |
Summary receiver operating characteristic (SROC) curves are demonstrated for the proposed models through quantile regression techniques and different characterizations of the estimated bivariate random effects distribution
SROC.norm(param,dcop,qcondcop,tau2par,TP,FN,FP,TN, points=TRUE,curves=TRUE, NEP=rep(0,length(TP)),NEN=rep(0,length(TP))) SROC.beta(param,dcop,qcondcop,tau2par,TP,FN,FP,TN, points=TRUE,curves=TRUE, NEP=rep(0,length(TP)),NEN=rep(0,length(TP))) SROC(param.beta,param.normal,TP,FN,FP,TN, NEP=rep(0,length(TP)),NEN=rep(0,length(TP)))
param |
A vector with the sensitivities, specifities, variabilities and Kendall's tau value (the latter only for |
param.beta |
A vector with the sensitivity, specifity and variabilities of the countermonotonic CopulaREMADA with beta margins |
param.normal |
A vector with the sensitivity, specifity and variabilities of the countermonotonic CopulaREMADA with normal margins |
dcop |
function for copula density |
qcondcop |
function for the inverse of conditional copula cdf |
tau2par |
function for maping Kendall's tau to copula parameter |
TP |
the number of true positives |
FN |
the number of false negatives |
FP |
the number of false positives |
TN |
the number of true negatives |
points |
logical: print individual studies |
curves |
logical: print quantile regression curves |
NEP |
the number of non-evaluable positives in the presence of non-evaluable subjects |
NEN |
the number of non-evaluable negatives in the presence of non-evaluable subjects |
Summary receiver operating characteristic curves
Nikoloulopoulos, A.K. (2015) A mixed effect model for bivariate meta-analysis of diagnostic test accuracy studies using a copula representation of the random effects distribution. Statistics in Medicine, 34, 3842–3865. doi: 10.1002/sim.6595.
CopulaREMADA
rCopulaREMADA
nq=15 gl=gauss.quad.prob(nq,"uniform") mgrid<- meshgrid(gl$n,gl$n) data(telomerase) attach(telomerase) est.n=countermonotonicCopulaREMADA.norm(TP,FN,FP,TN,gl,mgrid) est.b=countermonotonicCopulaREMADA.beta(TP,FN,FP,TN,gl,mgrid) SROC(est.b$e,est.n$e,TP,FN,FP,TN) detach(telomerase) data(LAG) attach(LAG) c180est.b=CopulaREMADA.beta(TP,FN,FP,TN,gl,mgrid,qcondcln180,tau2par.cln180) SROC.beta(c180est.b$e,dcln180,qcondcln180,tau2par.cln180,TP,FN,FP,TN) detach(LAG) data(MRI) attach(MRI) c270est.n=CopulaREMADA.norm(TP,FN,FP,TN,gl,mgrid,qcondcln270,tau2par.cln270) SROC.norm(c270est.n$e,dcln270,qcondcln270,tau2par.cln270,TP,FN,FP,TN) detach(MRI) data(MK2016) attach(MK2016) p=c(0.898745016,0.766105342,0.059168715,0.109217888) g=c(0.090270947,0.079469009,0.367463579,0.154976269) taus=c(0.82050793,-0.51867629,0.26457961) SROC.beta(c(p[1:2],g[1:2],taus[1]), dcln180,qcondcln180,tau2par.cln180, TP,FN,FP,TN,points=TRUE,curves=TRUE,NEP,NEN) detach(MK2016)
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