loss.biglasso

internal

Extend lasso and elastic-net model fitting for ultra
high-dimensional, multi-gigabyte data sets that cannot be loaded into
memory. Designed to be more memory- and computation-efficient than existing
lasso-fitting packages like 'glmnet' and 'ncvreg', thus allowing
the user to analyze big data analysis even on an ordinary laptop.

Patrick Breheny

biglasso

Extending Lasso Model Fitting to Big Data

Yaohui Zeng

Chuyi Wang

loss.biglasso function

<dl><dt>y</dt>
<dd>The observed response vector.</dd>
<dt>yhat</dt>
<dd>The predicted response vector.</dd>
<dt>family</dt>
<dd>Either "gaussian" or "binomial", depending on the response.</dd>
<dt>eval.metric</dt>
<dd>The evaluation metric for the cross-validated error and
for choosing optimal <code>lambda</code>. "default" for linear regression is MSE
(mean squared error), for logistic regression is misclassification error.
"MAPE", for linear regression only, is the Mean Absolute Percentage Error.
"auc", for logistic regression, is the area under the receiver operating
characteristic curve (ROC).</dd>
<dt>grouped</dt>
<dd>Whether to calculate loss for the entire CV fold
(<code>TRUE</code>), or for predictions individually. Must be <code>TRUE</code> when
<code>eval.metric</code> is 'auc'.</dd></dl>

Arguments

Yaohui Zeng and Patrick Breheny
Maintainer: Yaohui Zeng &lt;yaohui.zeng@gmail.com&gt;

Author

Internal biglasso functions — loss.biglasso

<dl>

<dt>y</dt>
<dd>The observed response vector.</dd>


<dt>yhat</dt>
<dd>The predicted response vector.</dd>


<dt>family</dt>
<dd>Either "gaussian" or "binomial", depending on the response.</dd>


<dt>eval.metric</dt>
<dd>The evaluation metric for the cross-validated error and
for choosing optimal <code>lambda</code>. "default" for linear regression is MSE
(mean squared error), for logistic regression is misclassification error.
"MAPE", for linear regression only, is the Mean Absolute Percentage Error.
"auc", for logistic regression, is the area under the receiver operating
characteristic curve (ROC).</dd>


<dt>grouped</dt>
<dd>Whether to calculate loss for the entire CV fold
(<code>TRUE</code>), or for predictions individually. Must be <code>TRUE</code> when
<code>eval.metric</code> is 'auc'.</dd>

</dl>

loss.biglasso: Internal biglasso functions

Description

Usage

Arguments

Author

Details