Mean.blrm

object

if <code>TRUE</code>, use the integer codes \(1,2,\ldots,k\) for the \(k\)-level response in computing the predicted mean response.

codes

defaults to posterior mean; may also specify <code>"median"</code>. Must be consistent with the summary used when creating <code>lp</code>.

posterior.summary

Function Generator for Mean Y for <code>blrm</code>

Regression modeling, testing, estimation, validation,
graphics, prediction, and typesetting by storing enhanced model design
attributes in the fit.  'rms' is a collection of functions that
assist with and streamline modeling.  It also contains functions for
binary and ordinal logistic regression models, ordinal models for
continuous Y with a variety of distribution families, and the Buckley-James
multiple regression model for right-censored responses, and implements
penalized maximum likelihood estimation for logistic and ordinary
linear models.  'rms' works with almost any regression model, but it
was especially written to work with binary or ordinal regression
models, Cox regression, accelerated failure time models,
ordinary linear models,	the Buckley-James model, generalized least
squares for serially or spatially correlated observations, generalized
linear models, and quantile regression.  Bayesian methods are implemented for binary and ordinal logistic regression with or without random effects.

Mean.blrm: Mean.blrm

Description

Usage

Arguments

Value

Details