The class <code>Lda</code> serves as a base class for deriving 
 all other classes representing the results of classical 
 and robust Linear Discriminant Analisys methods

Lda-class

Robust Location and Scatter Estimation and Robust
Multivariate Analysis with High Breakdown Point:
principal component analysis (Filzmoser and Todorov (2013), <doi:10.1016/j.ins.2012.10.017>),
linear and quadratic discriminant analysis (Todorov and Pires (2007)),
multivariate tests (Todorov and Filzmoser (2010) <doi:10.1016/j.csda.2009.08.015>),
outlier detection (Todorov et al. (2010) <doi:10.1007/s11634-010-0075-2>).
See also Todorov and Filzmoser (2009) <urn:isbn:978-3838108148>,
Todorov and Filzmoser (2010) <doi:10.18637/jss.v032.i03> and
Boudt et al. (2019) <doi:10.1007/s11222-019-09869-x>.

Valentin Todorov

rrcov

Scalable Robust Estimators with High Breakdown Point

Lda-class function

Class "Lda" - virtual base class for all classic and robust LDA classes — Lda-class

A virtual Class: No objects may be created from it.

Objects from the Class

<dl>
 
 <dt><code>call</code>:</dt>
<dd>the (matched) function call.</dd>

 <dt><code>prior</code>:</dt>
<dd>prior probabilities used, default to group proportions</dd>

 <dt><code>counts</code>:</dt>
<dd>number of observations in each class</dd>

 <dt><code>center</code>:</dt>
<dd>the group means</dd>

 <dt><code>cov</code>:</dt>
<dd>the common covariance matrix</dd>

 <dt><code>ldf</code>:</dt>
<dd>a matrix containing the linear discriminant functions</dd>

 <dt><code>ldfconst</code>:</dt>
<dd>a vector containing the constants of each linear discriminant function</dd>

 <dt><code>method</code>:</dt>
<dd>a character string giving the estimation method used</dd>

 <dt><code>X</code>:</dt>
<dd>the training data set (same as the input parameter x of the constructor function)</dd>

 <dt><code>grp</code>:</dt>
<dd>grouping variable: a factor specifying the class for each observation.</dd>
 
 <dt><code>covobj</code>:</dt>
<dd>object of class <code>"Cov"</code> containing the estimate 
 of the common covariance matrix of the centered data. It is not NULL 
 only in case of method "B".</dd>
 
 <dt><code>control</code>:</dt>
<dd>object of class <code>"CovControl"</code> specifying which estimate 
 and with what estimation options to use for the group means and common covariance 
 (or <code>NULL</code> for classical linear discriminant analysis)</dd>
 
 
</dl>

Slots

<dl>
 <dt>predict</dt>
<dd><code>signature(object = "Lda")</code>: calculates prediction using the results in 
 <code>object</code>. An optional data frame or matrix in which to look for variables with which 
 to predict. If omitted, the training data set is used. If the original fit used a formula or 
 a data frame or a matrix with column names, newdata must contain columns with the 
 same names. Otherwise it must contain the same number of columns, 
 to be used in the same order.</dd>

 <dt>show</dt>
<dd><code>signature(object = "Lda")</code>: prints the results</dd>

 <dt>summary</dt>
<dd><code>signature(object = "Lda")</code>: prints summary information</dd>

 
 
</dl>

Methods

Valentin Todorov <a href='mailto:valentin.todorov@chello.at'>valentin.todorov@chello.at</a>

Lda-class: Class "Lda" - virtual base class for all classic and robust LDA classes

Description

Arguments

References

See Also

Examples