rmultinomVineCopulaREMADA {CopulaREMADA} | R Documentation |
Simulation from multinomial quadrivariate (truncated) D-vine copula mixed models for diagnostic test accurracy studies accounting for non-evaluable outcomes
Description
Simulation from multinomial quadrivariate (truncated) D-vine copula mixed models for diagnostic test accurracy studies accounting for non-evaluable outcomes
Usage
rmultinomVineCopulaREMADA.norm(N,p,si,taus,qcond1,
pcond1,tau2par1,qcond2,
pcond2,tau2par2)
rmultinomVineCopulaREMADA.beta(N,p,g,taus,qcond1,
pcond1,tau2par1,qcond2,
pcond2,tau2par2)
Arguments
N |
sample size |
p |
Vector |
si |
Vector |
g |
Vector |
taus |
Kendall's tau values |
qcond1 |
function for the inverse conditional copula cdf at the (1,2) and (3,4) bivariate margin |
pcond1 |
function for the conditional copula cdf at the (1,2) and (3,4) bivariate margin |
tau2par1 |
function for maping Kendall's tau at the (1,2) and (3,4) bivariate margin to copula parameter |
qcond2 |
function for the inverse conditional copula cdf at the (2,3) bivariate margin |
pcond2 |
function for the conditional copula cdf at the (2,3) bivariate margin |
tau2par2 |
function for maping Kendall's tau at the (2,3) bivariate margin to copula parameter |
Value
Simulated data with 6 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
- NEP
the number of non-evaluable positives
- NEN
the number of non-evaluable negatives
References
Nikoloulopoulos, A.K. (2020) A multinomial quadrivariate D-vine copula mixed model for diagnostic studies meta-analysis in the presence of non-evaluable subjects. Statistical Methods in Medical Research, 29 (10), 2988–3005. doi:10.1177/0962280220913898.
See Also
Examples
N=30
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)
qcond1=qcondcln180
pcond1=pcondcln180
tau2par1=tau2par.cln180
qcond2=qcondcln90
pcond2=pcondcln90
tau2par2=tau2par.cln90
out=rmultinomVineCopulaREMADA.beta(N,p,g,taus,qcond1,pcond1,tau2par1,qcond2,pcond2,tau2par2)
TP=out[,1]
NEP=out[,2]
FN=out[,3]
TN=out[,4]
NEN=out[,5]
FP=out[,6]