bayesglm.fit: Fitting Generalized Linear Models and Bayesian marginal...

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

Fitting Generalized Linear Models and Bayesian marginal likelihood evaluation

Description

A version of glm.fit rewritten in C; also returns marginal likelihoods for Bayesian model comparison

Usage

bayesglm.fit(
  x,
  y,
  weights = rep(1, nobs),
  start = NULL,
  etastart = NULL,
  mustart = NULL,
  offset = rep(0, nobs),
  family = binomial(),
  coefprior = bic.prior(nobs),
  control = glm.control(),
  intercept = TRUE
)

Arguments

x	design matrix
y	response
weights	optional vector of weights to be used in the fitting process. Should be NULL or a numeric vector.
start	starting value for coefficients in the linear predictor
etastart	starting values for the linear predictor
mustart	starting values for the vectors of means
offset	a priori known component to be included in the linear predictor
family	a description of the error distribution and link function for exponential family; currently only binomial(), poisson(), and Gamma() with canonical links are implemented.
coefprior	function specifying prior distribution on coefficients with optional hyperparameters leading to marginal likelihood calculations; options include bic.prior(), aic.prior(), and ic.prior()
control	a list of parameters that control convergence in the fitting process. See the documentation for glm.control()
intercept	should an intercept be included in the null model?

Details

C version of glm-fit. For different prior choices returns, marginal likelihood of model using a Laplace approximation.

Value

coefficients	MLEs
se	Standard errors of coefficients based on the sqrt of the diagonal of the inverse information matrix
mu	fitted mean
rank	numeric rank of the fitted linear model
deviance	minus twice the log likelihood evaluated at the MLEs
g	value of g in g-priors
shrinkage	shrinkage factor for coefficients in linear predictor
RegSS	quadratic form beta'I(beta)beta used in shrinkage
logmarglik	the log marginal or integrated log likelihood (up to a constant)

Author(s)

Merlise Clyde translated the glm.fit from R base into C using the .Call interface

References

glm

See Also

bic.prior

Examples

data(Pima.tr, package="MASS")
Y <- as.numeric(Pima.tr$type) - 1
X <- cbind(1, as.matrix(Pima.tr[,1:7]))
out <- bayesglm.fit(X, Y, family=binomial(),coefprior=bic.prior(n=length(Y)))
out$coef
out$se
# using built in function
glm(type ~ ., family=binomial(), data=Pima.tr)

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