rFactorCopulaREMADA: Simulation from 1-factor copula mixed models for joint...

In CopulaREMADA: Copula Mixed Models for Multivariate Meta-Analysis of Diagnostic Test Accuracy Studies

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

CopulaREMADA documentation built on Aug. 7, 2022, 5:15 p.m.