SROC {CopulaREMADA} | R Documentation |
Summary receiver operating characteristic curves for copula mixed effect models for bivariate meta-analysis of diagnostic test accuracy studies
Description
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
Usage
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)))
Arguments
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 |
Value
Summary receiver operating characteristic curves
References
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.
See Also
Examples
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)