print.fpen

Print the summary of a non-penalized regression fitted by any model with function <code>frailtyMMpen</code>.
The first column is the tuning parameter sequence, the second column is the degree of freedom and the third column is the BIC.

The penalized and non-penalized Minorize-Maximization (MM) method for frailty models to fit the clustered data, multi-event data and recurrent data. Least absolute shrinkage and selection operator (LASSO), minimax concave penalty (MCP) and smoothly clipped absolute deviation (SCAD) penalized functions are implemented. All the methods are computationally efficient. These general methods are proposed based on the following papers,
Huang, Xu and Zhou (2022) <doi:10.3390/math10040538>,
Huang, Xu and Zhou (2023) <doi:10.1177/09622802221133554>.

Yunpeng Zhou

frailtyMMpen

Efficient Algorithm for High-Dimensional Frailty Model

Xifen Huang

Jinfeng Xu

print.fpen function

<dl><dt>x</dt>
<dd>Object with class "fpen" fitted by function <code>frailtyMMpen</code>.</dd>
<dt>...</dt>
<dd>Ignored</dd></dl>

Arguments

print a penalized regression object — print.fpen

<dl>

<dt>x</dt>
<dd>Object with class "fpen" fitted by function <code>frailtyMMpen</code>.</dd>


<dt>...</dt>
<dd>Ignored</dd>

</dl>

print.fpen: print a penalized regression object

Description

Usage

Value

Arguments

See Also