ind_fun_NLfP {BayesS5}

the log-marginal likelhood function based on the invers moment functional priors and inverse gamma prior (0.01,0.01)

Description

a log-marginal likelhood value of a model, based on the peMoM prior on the regression coefficients and inverse gamma prior (0.01,0.01) on the variance.

Usage

ind_fun_NLfP(ind2, y, phi, n, p, K, IP.phi, C.prior1, tuning)

Arguments

ind2

the index of covariates in a model

y

the response variable

phi

the B-spline basis

n

the sample size

p

the total number of covariates

K

the degree of freedom for the B-spline basis

IP.phi

the projection matrix on the null space; Q matrix in Shin and Bhattacharya (2020+)

C.prior1

the logarithm of the normalizing constant of the nonlocal funcitonal prior

tuning

a value of the tuning parameter

References

Shin, M. and Bhattacharya, A.(2020) Nonlocal Functional Priors for Nonparametric Hypothesis Testing and High-dimensional Model Selection.