predict.big_sp

Predict method for class <code>big_sp</code>.

Easy-to-use, efficient, flexible and scalable statistical tools.
Package bigstatsr provides and uses Filebacked Big Matrices via memory-mapping.
It provides for instance matrix operations, Principal Component Analysis,
sparse linear supervised models, utility functions and more
<doi:10.1093/bioinformatics/bty185>.

Florian Privé

bigstatsr

Statistical Tools for Filebacked Big Matrices

Michael Blum

Hugues Aschard

predict.big_sp function

<dl><dt>object</dt>
<dd>Object of class <code>big_sp</code>.</dd>
<dt>X</dt>
<dd>An object of class FBM.</dd>
<dt>ind.row</dt>
<dd>An optional vector of the row indices that are used.
If not specified, all rows are used. Don't use negative indices.</dd>
<dt>ind.col</dt>
<dd>An optional vector of the column indices that are used.
If not specified, all columns are used. Don't use negative indices.</dd>
<dt>covar.row</dt>
<dd>Matrix of covariables to be added in each model to correct
for confounders (e.g. the scores of PCA), corresponding to <code>ind.row</code>.
Default is <code>NULL</code> and corresponds to only adding an intercept to each model.
You can use <code>covar_from_df()</code> to convert from a data frame.</dd>
<dt>ncores</dt>
<dd>Number of cores used. Default doesn't use parallelism.
You may use nb_cores.</dd>
<dt>...</dt>
<dd>Not used.</dd></dl>

Arguments

Predict method — predict.big_sp

<dl>

<dt>object</dt>
<dd>Object of class <code>big_sp</code>.</dd>


<dt>X</dt>
<dd>An object of class FBM.</dd>


<dt>ind.row</dt>
<dd>An optional vector of the row indices that are used.
If not specified, all rows are used. Don't use negative indices.</dd>


<dt>ind.col</dt>
<dd>An optional vector of the column indices that are used.
If not specified, all columns are used. Don't use negative indices.</dd>


<dt>covar.row</dt>
<dd>Matrix of covariables to be added in each model to correct
for confounders (e.g. the scores of PCA), corresponding to <code>ind.row</code>.
Default is <code>NULL</code> and corresponds to only adding an intercept to each model.
You can use <code>covar_from_df()</code> to convert from a data frame.</dd>


<dt>ncores</dt>
<dd>Number of cores used. Default doesn't use parallelism.
You may use nb_cores.</dd>


<dt>...</dt>
<dd>Not used.</dd>

</dl>

predict.big_sp: Predict method

Description

Usage

Value

Arguments

See Also