print.summary.big_pls_cox_fast

Print method for summary.big_pls_cox_fast objects

Provides Partial least squares Regression and various regular, sparse
or kernel, techniques for fitting Cox models for big data. Provides a Partial
Least Squares (PLS) algorithm adapted to Cox proportional hazards models that
works with 'bigmemory' matrices without loading the entire dataset in memory.
Also implements a gradient-descent based solver for Cox proportional hazards
models that works directly on 'bigmemory' matrices.
Bertrand and Maumy (2023) <https://hal.science/hal-05352069>, and
<https://hal.science/hal-05352061> highlighted
fitting and cross-validating PLS-based Cox models to censored big data.

Frederic Bertrand

bigPLScox

Partial Least Squares for Cox Models with Big Matrices

Myriam Maumy-Bertrand

print.summary.big_pls_cox_fast function

<dl><dt>x</dt>
<dd>An object of class <code>summary.big_pls_cox_fast</code>.</dd>
<dt>...</dt>
<dd>Further arguments passed to or from other methods.</dd></dl>

Arguments

Print method for summary.big_pls_cox_fast objects — print.summary.big_pls_cox_fast

<dl>

<dt>x</dt>
<dd>An object of class <code>summary.big_pls_cox_fast</code>.</dd>


<dt>...</dt>
<dd>Further arguments passed to or from other methods.</dd>

</dl>

print.summary.big_pls_cox_fast: Print method for summary.big_pls_cox_fast objects

Description

Usage

Arguments