survival_data

Converts the input survival data with one row or element corresponding to a 
single observation or subject into a long-form dataset where one observation or subject
contributes <code>j</code> rows, where <code>j</code> is the number of time intervals that 
a subject survived at least part-way through.

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

survival_data function

<dl><dt>y</dt>
<dd>response, which must be a <code>Surv</code> object (see
<code><a href="/link/Surv?package=glmmPen&version=1.5.4.8" data-mini-rdoc="glmmPen::Surv">Surv</a></code> from the <code>survival</code> package)</dd>
<dt>X</dt>
<dd>matrix of fixed effects covariates</dd>
<dt>Z</dt>
<dd>matrix of random effects covariates</dd>
<dt>group</dt>
<dd>vector specifying the group assignment for each subject</dd>
<dt>offset_fit</dt>
<dd>vector specifying the offset. 
This can be used to specify an a priori known component to be included in the 
linear predictor during fitting. Default set to <code>NULL</code> (no offset). If the data 
argument is not <code>NULL</code>, this should be a numeric vector of length equal to the 
number of cases (the length of the response vector).</dd>
<dt>survival_options</dt>
<dd>a structure of class "survivalControl" created 
from function <code>survivalControl</code> that specifies several parameters needed to 
properly fit the input survival data using a piecewise constant hazard mixed model. See the 
documentation for <code>survivalControl</code> for more details on defaults.</dd></dl>

Arguments

Convert Input Survival Data Into Long-Form Data Needed for Fitting a Piecewise
Exponential Model — survival_data

<dl>

<dt>y</dt>
<dd>response, which must be a <code>Surv</code> object (see
<code><a href='https://rdrr.io/pkg/survival/man/Surv.html'>Surv</a></code> from the <code>survival</code> package)</dd>


<dt>X</dt>
<dd>matrix of fixed effects covariates</dd>


<dt>Z</dt>
<dd>matrix of random effects covariates</dd>


<dt>group</dt>
<dd>vector specifying the group assignment for each subject</dd>


<dt>offset_fit</dt>
<dd>vector specifying the offset. 
This can be used to specify an a priori known component to be included in the 
linear predictor during fitting. Default set to <code>NULL</code> (no offset). If the data 
argument is not <code>NULL</code>, this should be a numeric vector of length equal to the 
number of cases (the length of the response vector).</dd>


<dt>survival_options</dt>
<dd>a structure of class "survivalControl" created 
from function <code>survivalControl</code> that specifies several parameters needed to 
properly fit the input survival data using a piecewise constant hazard mixed model. See the 
documentation for <code>survivalControl</code> for more details on defaults.</dd>

</dl>

survival_data: Convert Input Survival Data Into Long-Form Data Needed for Fitting a Piecewise Exponential Model

Description

Usage

Arguments