bayes.lm: Bayesian linear models with uniform priors

In asbio: A Collection of Statistical Tools for Biologists

Bayesian linear models with uniform priors

Description

Gelman et al. (2002) describe general methods for Bayesian implementation of simple linear models (e.g. simple and multiple regression and fixed effect one way ANOVA) with standard non-informative priors uniform on \alpha, \sigma^2. The function is not yet suited for multifactor or multivariance (random effect) ANOVAs.

Usage

bayes.lm(Y, X, model = "anova", length = 1000, cred = 0.95)

Arguments

Y	An n x 1 column vector (a matrix with one column) containing the response variable.
X	The n x p design matrix
model	One of "anova" or "reg". Parameter output labels are changed depending on choice.
length	Number of draws for posterior.
cred	Level for credible interval.

Value

Provides the median and central credible intervals for model parameters.

Author(s)

Ken Aho

References

Gelman, A., Carlin, J. B., Stern, H. S., and D. B. Rubin (2003) Bayesian Data Analysis, 2nd edition. Chapman and Hall/CRC.

See Also

mcmc.norm.hier

Examples

## Not run: 
data(Fbird)
X <- with(Fbird, cbind(rep(1, 18), vol))
Y <- Fbird$freq
bayes.lm(Y, X, model = "reg")

## End(Not run)

asbio documentation built on May 29, 2024, 5:57 a.m.