adaptControl

Control of Metropolis-within-Gibbs Adaptive Random Walk Sampling Procedure
Controls the adaptive random walk Metropolis-within-Gibbs sampling procedure.

Fits high dimensional penalized generalized linear
mixed models using
the Monte Carlo Expectation Conditional Minimization (MCECM) algorithm.
The purpose of the package is to perform variable selection on both the fixed and
random effects simultaneously for generalized linear mixed models.
The package supports fitting of Binomial, Gaussian, and Poisson data with canonical links, and
supports penalization using the MCP, SCAD, or LASSO penalties. The MCECM algorithm
is described in Rashid et al. (2020) <doi:10.1080/01621459.2019.1671197>.
The techniques used in the minimization portion of the procedure (the M-step) are
derived from the procedures of the 'ncvreg' package (Breheny and Huang (2011)
<doi:10.1214/10-AOAS388>) and 'grpreg' package (Breheny and Huang (2015)
<doi:10.1007/s11222-013-9424-2>), with
appropriate modifications to account for the estimation and penalization of
the random effects. The 'ncvreg' and 'grpreg' packages also describe the MCP, SCAD,
and LASSO penalties.

Hillary Heiling

glmmPen

High Dimensional Penalized Generalized Linear Mixed Models
(pGLMM)

Naim Rashid

Quefeng Li

Joseph Ibrahim

adaptControl function

<dl><dt>batch_length</dt>
<dd>positive integer specifying the number of posterior samples to collect
before the proposal variance is adjusted based on the acceptance rate of the last 
<code>batch_length</code> accepted posterior samples. Default is set to 100. Batch length restricted
to be no less than 50.</dd>
<dt>offset</dt>
<dd>non-negative integer specifying an offset value for the increment of the proposal
variance adjustment. Optionally used to ensure the required diminishing adaptation condition. 
Default set to 0.
<code>
increment = min(0.01, 1 / sqrt(batch*batch_length + offset))
</code></dd></dl>

Arguments

Control of Metropolis-within-Gibbs Adaptive Random Walk Sampling Procedure

Controls the adaptive random walk Metropolis-within-Gibbs sampling procedure. — adaptControl

<dl>

<dt>batch_length</dt>
<dd>positive integer specifying the number of posterior samples to collect
before the proposal variance is adjusted based on the acceptance rate of the last 
<code>batch_length</code> accepted posterior samples. Default is set to 100. Batch length restricted
to be no less than 50.</dd>


<dt>offset</dt>
<dd>non-negative integer specifying an offset value for the increment of the proposal
variance adjustment. Optionally used to ensure the required diminishing adaptation condition. 
Default set to 0.
<code>
increment = min(0.01, 1 / sqrt(batch*batch_length + offset))
</code></dd>

</dl>

Control of Metropolis-within-Gibbs Adaptive Random Walk Sampling Procedure

Controls the adaptive random walk Metropolis-within-Gibbs sampling procedure.

Control of Metropolis-within-Gibbs Adaptive Random Walk Sampling ProcedureControls the adaptive random walk Metropolis-within-Gibbs sampling procedure.

adaptControl: Control of Metropolis-within-Gibbs Adaptive Random Walk Sampling Procedure
Controls the adaptive random walk Metropolis-within-Gibbs sampling procedure.

Description

Usage

Value

Arguments