infocrit: Expected Number of Parameters, DIC, AIC and BIC for bnlr fit.

In bnormnlr: Bayesian Estimation for Normal Heteroscedastic Nonlinear Regression Models

Description

Function to calculate Expected Number of Parameters, DIC, AIC and BIC for bnlr output.

Usage

infocrit(model, burn)

Arguments

model	A list derived from bnlr function
burn	A vector indicating which samples must be discarded from the mcmc simulation

Value

a vector with:

pd	Expected Number of Parameters
DIC	Deviance Information Criterion
AIC	Akaike Information Criterion
BIC	Bayesian Information Criterion

Author(s)

Nicolas Molano-Gonzalez, Marta Corrales Bossio, Maria Fernanda Zarate, Edilberto Cepeda-Cuervo.

References

Carlin, B. P. & Louis, T. A. (2009), Bayesian Methods for Data Analysis, 3rd edn, CRC Press, New York.

Gamerman, D. & Lopes, H. F. (2006), Markov Chain Monte Carlo: Stochastic Simulation for Bayesian Inference, 2nd edn, CRC Press, New York.

Examples

#######################################
###Simulation of heteroscedastic model
#######################################
utils::data(muscle, package = "MASS")
###mean and variance functions
fmu<-function(param,cov){ param[1] + param[2]*exp(-cov/exp(param[3]))}
fsgma<-function(param,cov){drop(exp(cov%*%param))}

###simulate heteroscedastic data
muscle$Length<-fmu(c(28.9632978, -34.2274097,  -0.4972977),muscle$Conc)+
rnorm(60,0,sqrt(exp(log(2)+.8*muscle$Conc)))

##Note: use more MCMC chains (i.e NC=10000) for more accurate results.
m2b<-bnlr(y=muscle$Length,f1=fmu,f2=fsgma,x=muscle$Conc,
z=matrix(rep(1,length(muscle$Length)),ncol=1),bta0=c(20,-30,-1),gma0=2,Nc=650)
m1b<-bnlr(y=muscle$Length,f1=fmu,f2=fsgma,x=muscle$Conc,z=cbind(1,muscle$Conc),
bta0=c(20,-30,0),gma0=c(.5,.5),Nc=650)

chainsum(m1b$chains,burn=1:65)
chainsum(m2b$chains,burn=1:65)
infocrit(m1b,1:65)
infocrit(m2b,1:65)

bnormnlr documentation built on May 2, 2019, 6:49 a.m.