# get_theta_aunif: Find Scale Parameter for Hyperprior for Variances Where the... In sdPrior: Scale-Dependent Hyperpriors in Structured Additive Distributional Regression

## Description

This function implements a optimisation routine that computes the scale parameter θ of the prior τ^2 (corresponding to a differentiably approximated version of the uniform prior for τ) for a given design matrix and prior precision matrix such that approximately P(|f(x_{k}|≤ c,k=1,…,p)≥ 1-α

## Usage

 ```1 2``` ```get_theta_aunif(alpha = 0.01, method = "integrate", Z, c = 3, eps = .Machine\$double.eps, Kinv) ```

## Arguments

 `alpha` denotes the 1-α level. `method` with `integrate` as default. Currently no further method implemented. `Z` the design matrix. `c` denotes the expected range of the function. `eps` denotes the error tolerance of the result, default is `.Machine\$double.eps`. `Kinv` the generalised inverse of K.

## Value

an object of class `list` with values from `uniroot`.

## References

Nadja Klein and Thomas Kneib (2015). Scale-Dependent Priors for Variance Parameters in Structured Additive Distributional Regression. Working Paper.

Andrew Gelman (2006). Prior Distributions for Variance Parameters in Hierarchical Models. Bayesian Analysis, 1(3), 515–533.

## Examples

 ``` 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) theta <- get_theta_aunif(alpha = 0.01, method = "integrate", Z = Z, c = 3, eps = .Machine\$double.eps, Kinv = Kinv)\$root ```

sdPrior documentation built on July 7, 2018, 9:02 a.m.