trCCH: Truncated Compound Confluent Hypergeometric function

In BAS: Bayesian Variable Selection and Model Averaging using Bayesian Adaptive Sampling

Truncated Compound Confluent Hypergeometric function

Description

Compute the Truncated Confluent Hypergeometric function from Li and Clyde (2018) which is the normalizing constant in the tcch density of Gordy (1998) with integral representation:

Usage

trCCH(a, b, r, s, v, k, log = FALSE)

Arguments

a	a > 0
b	b > 0
r	r >= 0
s	arbitrary
v	0 < v
k	arbitrary
log	logical indicating whether to return values on the log scale; useful for Bayes Factor calculations

Details

tr.cch(a,b,r,s,v,k) = Int_0^1/v u^(a-1) (1 - vu)^(b -1) (k + (1 - k)vu)^(-r) exp(-s u) du

This uses a more stable method for calculating the normalizing constant using R's 'integrate' function rather than the version in Gordy 1998. For calculating Bayes factors that use the 'trCCH' function we recommend using the 'log=TRUE' option to compute log Bayes factors.

Author(s)

Merlise Clyde ([e-mail])

References

Gordy 1998 Li & Clyde 2018

See Also

Other special functions: hypergeometric1F1(), hypergeometric2F1(), phi1()

Examples

# special cases
# trCCH(a, b, r, s=0, v = 1, k) is the same as
# 2F1(a, r, a + b, 1 - 1/k)*beta(a, b)/k^r

k = 10; a = 1.5; b = 2; r = 2;  
trCCH(a, b, r, s=0, v = 1, k=k) *k^r/beta(a,b)
hypergeometric2F1(a, r, a + b, 1 - 1/k, log = FALSE)

# trCCH(a,b,0,s,1,1) is the same as 
# beta(a, b) 1F1(a, a + b, -s, log=FALSE)
s = 3; r = 0; v = 1; k = 1
beta(a, b)*hypergeometric1F1(a, a+b, -s, log = FALSE)
trCCH(a, b, r, s, v, k)

# Equivalence with the Phi1 function 
a = 1.5; b = 3; k = 1.25; s = 400;  r = 2;  v = 1; 

phi1(a, r,  a + b, -s, 1 - 1/k,  log=FALSE)*(k^-r)*gamma(a)*gamma(b)/gamma(a+b)
trCCH(a,b,r,s,v,k)

BAS documentation built on Sept. 30, 2024, 9:19 a.m.