sdPrior (version 1.0-0)

mdbeta: Marginal Density of \(\beta\)

Description

This function computes the marginal density of \(\beta\) and for \(\beta\) on an equidistant grid specified by the user. Currently only implemented for \(dim(\beta)=1,2\).

Usage

mdbeta(D = 1, rangebeta, ngridbeta, a = 5, b = 25, r = 0.00025,
  a0 = 0.5, b0 = 0.5, plot = FALSE, log = FALSE)

Arguments

D

dimension of \(\beta\).

rangebeta

a vector containing the start and ending point of \(\beta\) to be computed for.

ngridbeta

the number of grid values.

a

shape parameter of inverse gamma prior of \(\psi^2\).

b

scale parameter of inverse gamma prior of \(\psi^2\).

r

the scaling parameter \(r(\delta=1)\) in the variance \(r(\delta)\psi^2\) of prior of \(\tau^2\).

a0

shape parameter of beta prior of \(\omega\).

b0

scale parameter of beta prior of \(\omega\).

plot

logical value (default is FALSE). If TRUE, a plot is also returned as the function pl().

log

logical value (default is FALSE). If TRUE, \(log(p(\beta))\) is also returned in logval. as well as, if necessary, a plot function logpl().

Value

the marginal density, the sequence of \(\beta\) and depending on specified plot, log arguments also the log-density and plot functions.

References

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

Examples

# NOT RUN {
set.seed(123)
#1-dimensional example
D = 1
ngridbeta = 1000
rangebeta = c(0.000001,1)
a0 = b0 = 0.5
a = 5
b = 50
r = 0.005
mdf <- mdbeta(D=1,rangebeta,ngridbeta,a=a,b=b,r=r,a0=a0,b0=b0) 

#2-dimensional example
D = 2
ngridbeta = 100
rangebeta = c(0.000001,8)
a0 = b0 = 0.5
a = 5
b = 50
r = 0.005
mdf <- mdbeta(D=2,rangebeta,ngridbeta,a=a,b=b,r=r,a0=a0,b0=b0,plot=TRUE,log=TRUE) 
mdf$logpl()

# }