rmultinomVineCopulaREMADA {CopulaREMADA}

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 (\pi_1,\pi_2,\pi_3) of sensitivity/specificity/prevalence

si

Vector (\sigma_1,\sigma_2,\sigma_3) of variability; normal margins

g

Vector (\gamma_1,\gamma_2,\gamma_3) of variability; beta margins

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

dvinesim

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]