rFactorCopulaREMADA {CopulaREMADA}

Simulation from 1-factor copula mixed models for joint meta-analysis of T diagnostic tests

Description

Simulation from 1-factor copula mixed models for joint meta-analysis of T diagnostic tests

Usage

rFactorCopulaREMADA.norm(N,p,si,taus,qcond1,tau2par1,qcond2,tau2par2)
rFactorCopulaREMADA.beta(N,p,g,taus,qcond1,tau2par1,qcond2,tau2par2)      

Arguments

N

number of studies

p

vector of sensitivities and specificities

si

vector of variabilities; normal margins

g

vector of variabilities; beta margins

taus

Kendall's tau values

qcond1

function for the inverse conditional copula cdfs that link the factor with the latent sensitivities

tau2par1

function for maping Kendall's tau to copula parameter at the copulas that link the factor with the latent sensitivities

qcond2

function for the inverse conditional copula cdfs that link the factor with the latent specificities

tau2par2

function for maping Kendall's tau to copula parameter at the copulas that link the factor with the latent specificities

Value

A list with the simulated data in matrices with T columns and N rows.

TP

the number of true positives

FN

the number of false negatives

FP

the number of false positives

TN

the number of true negatives

References

Nikoloulopoulos, A.K. (2022) An one-factor copula mixed model for joint meta-analysis of multiple diagnostic tests. Journal of the Royal Statistical Society: Series A (Statistics in Society), 185 (3), 1398–1423. doi:10.1111/rssa.12838.

Examples

N=50

qcond1=qcondcln
tau2par1=tau2par.cln
qcond2=qcondcln270
tau2par2=tau2par.cln270

p=c(0.8,0.7,0.8,0.7,0.8,0.7)
mu=log(p/(1-p))
si=rep(1,6)
taus=c(0.6,0.7,0.5,-0.3,-0.4,-0.2)

out=rFactorCopulaREMADA.norm(N,p,si,taus,qcond1,tau2par1,qcond2,tau2par2)
  
TP=out$TP
FN=out$FN
TN=out$TN
FP=out$FP