Qda-class: Class "Qda" - virtual base class for all classic and robust...

In armstrtw/rrcov: Scalable Robust Estimators with High Breakdown Point

Description

The class Qda serves as a base class for deriving all other classes representing the results of classical and robust Quadratic 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 group covariance matrices

covinv:

the inverse of the group covariance matrices

covdet:

the determinants of the group covariance matrices

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.

control:

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

Methods

predict

signature(object = "Qda"): 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 scores are 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 = "Qda"): prints the results

summary

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

Author(s)

Valentin Todorov [e-mail]

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

QdaClassic, QdaClassic-class, QdaRobust-class

Examples

showClass("Qda")

armstrtw/rrcov documentation built on May 10, 2019, 1:43 p.m.