rl_importance: Fitting restricted likelihood model using importance sampling...
In jrlewi/brlm: Bayesian Restricted Likelihood Methods

Description Usage Arguments Value Author(s)

Full model:

b~N(μ_0, Σ_0), σ^2~IG(α, β), y~N(Xb, σ^2)

For the restricted likelihood, conditioning is done on a pair of location and scale statistics T(y) = (b(y), s(y)). statistic is a function of X, y and outputing the p+1-dimensional vector T(y) with the p location statistics (for b) and 1 scale statistic. Instrumental (importance) distributions are normal. For beta, a multivariate normal centererd at the estimate of beta with covariance cov_b. For sigma^2, a truncated normal with mean (before truncation) of the estimate of σ^2 with sd scale.

rl_importance(
  y,
  X,
  statistic,
  mu0,
  Sigma0,
  alpha,
  beta,
  cov_b,
  scale,
  smooth = 1,
  N,
  Nins
)

`y`	vector of data
`X`	a matrix or data frame containing the explanatory variables. The matrix should include a vector of 1's if intercept is desired.
`statistic`	character name of a function computing the location and scale statistic. See details for specification of this function.
`alpha`	prior shape and scale for sigma^2
`beta`	prior shape and scale for sigma^2
`cov_b`	positive definite pxp matrix defining the covariance for the normal instrumental distribution on `b`. Importance sample weights should be examined to evaluate the appropriatness of this choice.
`scale`	numeric, sd for the truncated normal instrumental distribution for σ^2. Importance sample weights should be examined to evaluate the appropriatness of this choice.
`smooth`	scalar or vector of length 2 that will scale the initial bandwidth computed by `hpi`.
`N`	number of samples of the statistics to use for the kernel density estimation
`Nins`	number of samples from the instrumental distribution

list with elements impSamps, w, fit. These are the Nins by length(beta)+1 matrix of importance samples, the corresponding weights, and the observed statistic.

John R. Lewis lewis.865@osu.edu

jrlewi/brlm documentation built on March 17, 2021, 1:10 a.m.