hyperpar: Find Scale Parameters for Inverse Gamma Hyperprior of...

In sdPrior: Scale-Dependent Hyperpriors in Structured Additive Distributional Regression

Description

This function implements a optimisation routine that computes the scale parameter b and selection parameter r. . Here, we assume an inverse gamma prior IG(a,b) for ψ^2 and τ^2\sim N(0,r(δ)ψ^2) and given shape paramter a, such that approximately P(f(x)≤ c|spike,\forall x\in D)≥ 1-α1 and P(\exists x\in D s.t. f(x)≥ c|slab)≥ 1-α2.

Usage

hyperpar(Z, Kinv, a = 5, c = 0.1, alpha1 = 0.1, alpha2 = 0.1,
  R = 10000, myseed = 123)

Arguments

Z	the row of the design matrix (or the complete matrix of several observations) evaluated at.
Kinv	the generalised inverse of K.
a	is the shape parameter of the inverse gamma distribution, default is 5.
c	denotes the expected range of eqnf .
alpha1	denotes the 1-α1 level for b.
alpha2	denotes the 1-α2 level for r.
R	denotes the number of replicates drawn during simulation.
myseed	denotes the required seed for the simulation based method.

Value

an object of class list with root values r, b from uniroot.

Author(s)

Nadja Klein

References

Nadja Klein, Thomas Kneib, Stefan Lang and Helga Wagner (2016). Spike and Slab Priors for Effect Selection in Distributional Regression. Working Paper.

Examples

set.seed(123)
library(MASS)
# prior precision matrix (second order differences) 
# of a spline of degree l=3 and with m=22 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 (same as if we used mixed model representation!)
Kinv <- ginv(K)
# covariate x
x <- runif(1)
Z <- matrix(DesignM(x)$Z_B,nrow=1)
fgrid <- seq(-3,3,length=1000)
mdf <- hyperpar(Z,Kinv,a=5,c=0.1,alpha1=0.05,alpha2=0.05,R=10000,myseed=123)

sdPrior documentation built on May 2, 2019, 8:57 a.m.