mix_re

Mixing Function for Dual Random Effect Sigmas

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 types of censoring
with optional random effects, 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.

Frank Harrell Jr

Regression Modeling Strategies

Frank E Harrell Jr

mix_re function

<dl><dt>mre</dt>
<dd>mixing proportion for dual sigmas for random effects with <code>orm()</code>. These are typically a function of time in a Markov model, scaled to be between 0 and 1 inclusive. When <code>mre</code> is zero the multiplier of normal(0,1) random effects is the first sigma, and when <code>mre</code> is 1.0 the multiplier is the second sigma. Otherwise it is a weighted combination of sigmas. The first sigma is constrained to be positive.</dd></dl>

Arguments

Author

mix_re — mix_re

<dl>

<dt>mre</dt>
<dd>mixing proportion for dual sigmas for random effects with <code>orm()</code>. These are typically a function of time in a Markov model, scaled to be between 0 and 1 inclusive. When <code>mre</code> is zero the multiplier of normal(0,1) random effects is the first sigma, and when <code>mre</code> is 1.0 the multiplier is the second sigma. Otherwise it is a weighted combination of sigmas. The first sigma is constrained to be positive.</dd>

</dl>

mix_re: mix_re

Description

Usage

Value

Arguments

Author

Details

Examples