rFactorCopulaREMADA {CopulaREMADA} | R Documentation |
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