CopulaDTA (version 1.0.1)

fit.cdtamodel: Fit copula based bivariate beta-binomial distribution to diagnostic data.

Description

Fit copula based bivariate beta-binomial distribution to diagnostic data.

Usage

fit.cdtamodel(
  cdtamodel,
  data,
  SID,
  cores = 3,
  chains = 3,
  iter = 6000,
  warmup = 1000,
  thin = 10,
  ...
)

Value

An object of cdtafit class.

Arguments

cdtamodel

An object of cdtamodel class from cdtamodel.

data

A data-frame with no missing values containing TP, TN, FP, FN, 'SID' and co-variables(if necessary).

SID

A string indicating the name of the column with the study identifier.

cores

A positive numeric values specifying the number of cores to use to execute parallel sampling. When the hardware has more at least 4 cores, the default is 3 cores and otherwise 1 core.

chains

A positive numeric value specifying the number of chains, default is 3.

iter

A positive numeric value specifying the number of iterations per chain. The default is 6000.

warmup

A positive numeric value (<iter) specifying the number of iterations to be discarded(burn-in/warm-up). The default is 1000.

thin

A positive numeric value specifying the interval in which the samples are stored. The default is 10.

...

Other optional parameters as specified in stan.

Author

Victoria N Nyaga <[e-mail]>

References

Nyaga VN, Arbyn M, Aerts M (2017). CopulaDTA: An R Package for Copula-Based Beta-Binomial Models for Diagnostic Test Accuracy Studies in a Bayesian Framework. Journal of Statistical Software, 82(1), 1-27. doi:10.18637/jss.v082.c01

Agresti A (2002). Categorical Data Analysis. John Wiley & Sons, Inc.

Clayton DG (1978). A model for Association in Bivariate Life Tables and its Application in Epidemiological Studies of Familial Tendency in Chronic Disease Incidence. Biometrika,65(1), 141-151.

Frank MJ (1979). On The Simultaneous Associativity of F(x, y) and x + y - F(x, y). Aequationes Mathematicae, pp. 194-226.

Farlie DGJ (1960). The Performance of Some Correlation Coefficients for a General Bivariate Distribution. Biometrika, 47, 307-323.

Gumbel EJ (1960). Bivariate Exponential Distributions. Journal of the American Statistical Association, 55, 698-707.

Meyer C (2013). The Bivariate Normal Copula. Communications in Statistics - Theory and Methods, 42(13), 2402-2422.

Morgenstern D (1956). Einfache Beispiele Zweidimensionaler Verteilungen. Mitteilungsblatt furMathematische Statistik, 8, 23 - 235.

Sklar A (1959). Fonctions de Repartition a n Dimensions et Leurs Marges. Publications de l'Institut de Statistique de L'Universite de Paris, 8, 229-231.

Examples

data(telomerase)
model1 <-  cdtamodel(copula = 'fgm')

model2 <- cdtamodel(copula = 'fgm',
               modelargs=list(param=2,
                              prior.lse='normal',
                              par.lse1=0,
                              par.lse2=5,
                              prior.lsp='normal',
                              par.lsp1=0,
                              par.lsp2=5))

model3 <-  cdtamodel(copula = 'fgm',
               modelargs = list(formula.se = StudyID ~ Test - 1))
if (FALSE) {
fit1 <- fit(model1,
                SID='ID',
                data=telomerase,
                iter=2000,
                warmup=1000,
                thin=1,
                seed=3)


fit2 <- fit(model2,
                SID='StudyID',
                data=ascus,
                iter=2000,
                warmup=1000,
                thin=1,
                seed=3)
}