Description Usage Arguments Value Author(s) References Examples
This function computes the marginal density of z_p'β for generalised beta prior hyperprior for τ^2 (half-Chauchy for τ)
1 | mdf_gbp(f, theta, Z, Kinv)
|
f |
point the marginal density to be evaluated at. |
theta |
denotes the scale parameter of the generalised beta prior hyperprior for τ^2 (half-Chauchy for τ). |
Z |
the row of the design matrix evaluated. |
Kinv |
the generalised inverse of K. |
the marginal density evaluated at point x.
Nadja Klein
Nadja Klein and Thomas Kneib (2015). Scale-Dependent Priors for Variance Parameters in Structured Additive Distributional Regression. Working Paper.
1 2 3 4 5 6 7 8 9 10 11 12 13 | set.seed(123)
library(MASS)
# prior precision matrix (second order differences)
# of a spline of degree l=3 and with m=20 inner knots
# yielding dim(K)=m+l-1=22
K <- t(diff(diag(22), differences=2))%*%diff(diag(22), differences=2)
# generalised inverse of K
Kinv <- ginv(K)
# covariate x
x <- runif(1)
Z <- matrix(DesignM(x)$Z_B,nrow=1)
fgrid <- seq(-3,3,length=1000)
mdf <- mdf_gbp(fgrid,theta=0.0028,Z=Z,Kinv=Kinv)
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