Description

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

Objects from the Class

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

Slots

call:

the (matched) function call.

prior:

prior probabilities used, default to group proportions

counts:

number of observations in each class

center:

the group means

cov:

the common covariance matrix

ldf:

a matrix containing the linear discriminant functions

ldfconst:

a vector containing the constants of each linear discriminant function

method:

a character string giving the estimation method used

X:

the training data set (same as the input parameter x of the constructor function)

grp:

grouping variable: a factor specifying the class for each observation.

covobj:

object of class "Cov" containing the estimate of the common covariance matrix of the centered data. It is not NULL only in case of method "B".

control:

object of class "CovControl" specifying which estimate and with what estimation options to use for the group means and common covariance (or NULL for classical linear discriminant analysis)

Methods

predict

signature(object = "Lda"): calculates prediction using the results in object. 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.

show

signature(object = "Lda"): prints the results

summary

signature(object = "Lda"): prints summary information

Author(s)

Valentin Todorov valentin.todorov@chello.at

References

Todorov V & Filzmoser P (2009), An Object Oriented Framework for Robust Multivariate Analysis. Journal of Statistical Software, 32(3), 1–47. URL http://www.jstatsoft.org/v32/i03/.

See Also

LdaClassic, LdaClassic-class, LdaRobust-class

Examples