Generate posterior simulations for a given fitted linear or general linear
model, assuming the standard "noninformative" priors on the unknowns.
Usage
posterior(obj, ...)
Arguments
obj
an object
…
further arguments
Value
A (named) list of random vectors. For example, the lm method
returns a list with components sigma (the residual s.d.) and
beta, the regression coefficients.
References
Kerman, J. and Gelman, A. (2007). Manipulating and Summarizing
Posterior Simulations Using Random Variable Objects. Statistics and
Computing 17:3, 235-244.
# NOT RUN {# }# NOT RUN { x <- 1:20 y <- rnorm(length(x), mean=x, sd=10)
print(summary(fit <- lm(y ~ x)))
bayes.estimates <- posterior(fit)
# }# NOT RUN {# }