hyperpar_mod {sdPrior}

Find Scale Parameter for modular regression

Description

Find Scale Parameter for modular regression

Usage

hyperpar_mod(Z, K1, K2, A, c = 0.1, alpha = 0.1, omegaseq, omegaprob,
  R = 10000, myseed = 123, thetaseq = NULL, type = "IG",
  lowrank = FALSE, k = 5, mc = FALSE, ncores = 1, truncate = 1)

Arguments

Z

rows from the tensor product design matrix

K1

precision matrix1

K2

precision matrix2

A

constraint matrix

c

threshold from eq. (8) in Klein & Kneib (2016)

alpha

probability parameter from eq. (8) in Klein & Kneib (2016)

omegaseq

sequence of weights for the anisotropy

omegaprob

prior probabilities for the weights

R

number of simulations

myseed

seed in case of simulation. default is 123.

thetaseq

possible sequence of thetas. default is NULL.

type

type of hyperprior for tau/tau^2; options: IG => IG(1,theta) for tau^2, SD => WE(0.5,theta) for tau^2, HN => HN(0,theta) for tau, U => U(0,theta) for tau, HC => HC(0,theta) for tau

lowrank

default is FALSE. If TRUE a low rank approximation is used for Z with k columns.

k

only used if lowrank=TRUE. specifies target rank of low rank approximation. Default is 5.

mc

default is FALSE. only works im thetaseq is supplied. can parallel across thetaseq.

ncores

default is 1. number of cores is mc=TRUE

truncate

default is 1. If < 1 the lowrank approximation is based on on cumsum(values)/sum(values).

Value

the optimal value for theta

Author(s)

Nadja Klein

References

Kneib, T., Klein, N., Lang, S. and Umlauf, N. (2017) Modular Regression - A Lego System for Building Structured Additive Distributional Regression Models with Tensor Product Interactions Working Paper.