priorcontrol.bnp: Prior information for the 'AROC.bnp' and 'cROC.bnp'

In ROCnReg: ROC Curve Inference with and without Covariates

Prior information for the AROC.bnp and cROC.bnp

Description

This function is used to set various parameters controlling the prior information to be used in the AROC.bnp and cROC.bnp functions.

Usage

priorcontrol.bnp(m0 = NA, S0 = NA, nu = NA, Psi = NA, a = 2, b = NA, 
	alpha = 1, L = 10)

Arguments

m0	A numeric vector. Hyperparameter; mean vector of the (multivariate) normal prior distribution for the mean of the normal component of the centring distribution. NA signals autoinitialization, with defaults: a vector, of length Q, of zeros, if the data are standardised and the least squares estimates of the regression coefficients if the data are not standardised.
S0	A numeric matrix. Hyperparameter; covariance matrix of the (multivariate) normal prior distribution for the mean of the normal component of the centring distribution. NA signals autoinitialization, with defaults: 10I_{Q\times Q} if the data are standardised and \mathbf{\hat{\Sigma}} if the data are not standardised, where \mathbf{\hat{\Sigma}} is the estimated covariance matrix of the regression coefficients obtained by fitting a linear model to the data.
nu	A numeric value. Hyperparameter; degrees of freedom of the Wishart prior distribution for the precision matrix of the the normal component of the centring distribution.NA signals autoinitialization, with default: Q+2 where Q is the number of columns of the design matrix.
Psi	A numeric matrix. Hyperparameter; scale matrix of the Wishart distribution for the precision matrix of the the normal component of the centring distribution. NA signals autoinitialization, with defaults: I_{Q\times Q} if the data are standardised and to 30\mathbf{\hat{\Sigma}} if the data are not standardised, where \mathbf{\hat{\Sigma}} is the estimated covariance matrix of the regression coefficients obtained by fitting a linear model to the data.
a	A numeric value. Hyperparameter; shape parameter of the gamma prior distribution for the precisions (inverse variances) of each component. The default is 2.
b	A numeric value. Hyperparameter; shape parameter of the gamma prior distribution for the precisions (inverse variances) of each component. NA signals autoinitialization, with defaults: 0.5 if the data are standardised and \frac{\hat{\sigma}^2}{2} if the data are not standardised
alpha	A numeric value. Precision parameter of the Dirichlet Process. The default is 1.
L	A numeric value. Upper bound on the number of mixture components. Setting L = 1 corresponds to a normal model. The default is 10.

Value

A list with components for each of the possible arguments.

See Also

AROC.bnp and cROC.bnp

ROCnReg documentation built on March 31, 2023, 5:42 p.m.