default.lambda

Internal function that performs cross validation to determine reasonable default values for L1 penalty
in a conditional logistic regression

Implements L1 and L2 penalized conditional logistic regression with penalty
factors allowing for integration of multiple data sources. Implements
stability selection for variable selection.

Vera Djordjilovi'c

penalizedclr

Integrative Penalized Conditional Logistic Regression

Erica Ponzi

default.lambda function

<dl><dt>X</dt>
<dd>A matrix of covariates, with the number of rows equaling the number
of observations.</dd>
<dt>Y</dt>
<dd>A binary response variable.</dd>
<dt>stratum</dt>
<dd>A numeric vector with stratum membership of each observation.</dd>
<dt>nfolds</dt>
<dd>The number of folds used in cross-validation. Default is 10.</dd>
<dt>alpha</dt>
<dd>The elastic net mixing parameter, a number between 0 and 1.
alpha=0 would give pure ridge; alpha=1 gives lasso. Pure ridge penalty is never obtained in this implementation since alpha must be positive.</dd></dl>

Arguments

Default values for L1 penalty in conditional logistic regression — default.lambda

<dl>

<dt>X</dt>
<dd>A matrix of covariates, with the number of rows equaling the number
of observations.</dd>


<dt>Y</dt>
<dd>A binary response variable.</dd>


<dt>stratum</dt>
<dd>A numeric vector with stratum membership of each observation.</dd>


<dt>nfolds</dt>
<dd>The number of folds used in cross-validation. Default is 10.</dd>


<dt>alpha</dt>
<dd>The elastic net mixing parameter, a number between 0 and 1.
alpha=0 would give pure ridge; alpha=1 gives lasso. Pure ridge penalty is never obtained in this implementation since alpha must be positive.</dd>

</dl>

default.lambda: Default values for L1 penalty in conditional logistic regression

Description

Usage

Value

Arguments