lmBselect: Variable selection and fitting Linear Models using bayesian...

In jgodet/utilitR: Lots of useful functions

Variable selection and fitting Linear Models using bayesian inference.

Description

Variable selection and fitting Linear Models using bayesian inference.

Usage

lmBselect(
  formula,
  data,
  graphOutput = TRUE,
  nIter = 10000,
  thin = 1,
  effect = "random"
)

Arguments

formula	an object of class "formula" (or one that can be coerced to that class): a symbolic description of the model to be fitted.
data	an optional data frame containing the variables in the model. If not found in data, the variables are taken from environment(formula), typically the environment from which blm is called.
graphOutput	regression parameters graphical output (MCMC Trace and posterior density)
nIter	number of iterations
thin	thinning interval for monitors
effect	"fixed", "random" or "randomPrior" effect for variable selection

Details

Models for lm are specified symbolically. A typical model has the form response ~ terms where response is the (numeric) response vector and terms is a series of terms which specifies a linear predictor for response. The effect "randomPrior" adds a beta prior for the model inclusion probability. This induces a distribution for the number of included variables which has longer tails than the binomial distribution, allowing the model to learn about the degree of sparsity.

Value

regression parameters

Author(s)

JuG

References

1. O’Hara, R. and Sillanpaa, M. (2009) A review of Bayesian variable selection methods: what, how and which. Bayesian Analysis, 4(1):85-118.

2. Kuo, L. and Mallick, B. (1998) Variable selection for regression models. Sankhya B, 60(1):65-81.

Examples

#generate model data.
n <- 500
p <- 20
X <- matrix(rnorm(n*p),ncol=p)
beta <- 2^(0:(1-p))
print(beta)
alpha <- 3
tau <- 2
eps <- rnorm(n,0,1/sqrt(tau))
y <- alpha+as.vector(X%*%beta + eps)
daten <- cbind(y,as.data.frame(X))

mod <- lm(y~.,data=daten)
require(MASS)
stepAIC(mod,direction = "both")
lmBselect(y~.,data=daten,nIter=10000,graphOutput=FALSE,effect="randomPrior")

jgodet/utilitR documentation built on May 16, 2024, 12:01 p.m.