summary.big_pls_cox_gd

Summary for big_pls_cox_gd 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

summary.big_pls_cox_gd function

<dl><dt>object</dt>
<dd>A <code>big_pls_cox_gd</code> object.</dd>
<dt>digits</dt>
<dd>Number of digits to print.</dd>
<dt>...</dt>
<dd>Unused, for S3 compatibility.</dd></dl>

Arguments

Summary for big_pls_cox_gd objects — summary.big_pls_cox_gd

<dl>

<dt>object</dt>
<dd>A <code>big_pls_cox_gd</code> object.</dd>


<dt>digits</dt>
<dd>Number of digits to print.</dd>


<dt>...</dt>
<dd>Unused, for S3 compatibility.</dd>

</dl>

summary.big_pls_cox_gd: Summary for big_pls_cox_gd objects

Description

Usage

Arguments