RidgeBinaryLogisticFit

This function fits a logistic regression between a dependent variable y and 
 some independent variables x, and solves the separation problem in this type
 of regression using ridge regression and penalization.

Several multivariate techniques from a biplot perspective. It is the translation (with many improvements) into R of the previous package developed in 'Matlab'. The package contains some of the main developments of my team during the last 30 years together with some more standard techniques. Package includes: Classical Biplots, HJ-Biplot, Canonical Biplots, MANOVA Biplots, Correspondence Analysis, Canonical Correspondence Analysis, Canonical STATIS-ACT, Logistic Biplots for binary and ordinal data, Multidimensional Unfolding, External Biplots for Principal Coordinates Analysis or Multidimensional Scaling, among many others. References can be found in the help of each procedure.

Jose Vicente-Villardon

MultBiplotR

Multivariate Analysis Using Biplots in R

RidgeBinaryLogisticFit function

<dl> <dt>y</dt>
<dd>A vector with the values of the dependent variable</dd> <dt>xd</dt>
<dd>A matrix with the independent variables</dd> <dt>freq</dt>
<dd>Frequencies of each pattern</dd> <dt>tolerance</dt>
<dd>Tolerance for the iterations.</dd> <dt>maxiter</dt>
<dd>Maximum number of iterations for convergenc~</dd> <dt>penalization</dt>
<dd>Penalization used in the diagonal matrix to avoid singularities.</dd></dl>

Arguments

Author

Fits a binary logistic regression with ridge penalization — RidgeBinaryLogisticFit

<dl>

 <dt>y</dt>
<dd>A vector with the values of the dependent variable</dd>

 <dt>xd</dt>
<dd>A matrix with the independent variables</dd>

 <dt>freq</dt>
<dd>Frequencies of each pattern</dd>

 <dt>tolerance</dt>
<dd>Tolerance for the iterations.</dd>

 <dt>maxiter</dt>
<dd>Maximum number of iterations for convergenc~</dd>

 <dt>penalization</dt>
<dd>Penalization used in the diagonal matrix to avoid singularities.</dd>

</dl>

RidgeBinaryLogisticFit: Fits a binary logistic regression with ridge penalization

Description

Usage

Value

Arguments

Author

Details

See Also

Examples