Class "LdaPP" - Robust method for Linear Discriminant Analysis by Projection-pursuit
LdaPP represents an algorithm for robust linear discriminant
analysis by projection-pursuit approach. The objects of class
LdaPP contain the results
of the robust linear discriminant analysis by projection-pursuit approach.
Objects from the Class
Objects can be created by calls of the form
new("LdaPP", ...) but the
usual way of creating
LdaPP objects is a call to the function
LdaPP which serves as a constructor.
The (matched) function call.
Prior probabilities used, default to group proportions
number of observations in each class
the group means
the common covariance matrix
a matrix containing the raw linear discriminant functions - see Details in
a vector containing the raw constants of each raw linear discriminant function - see Details in
a matrix containing the linear discriminant functions
a vector containing the constants of each linear discriminant function
a character string giving the estimation method used
the training data set (same as the input parameter x of the constructor function)
grouping variable: a factor specifying the class for each observation.
"Lda", by class "LdaRobust", distance 2.
signature(object = "LdaPP"): 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. If the argument
raw=TRUEis set the raw (obtained by the first approximation algorithm) linear discriminant function and constant will be used.
Pires, A. M. and A. Branco, J. (2010) Projection-pursuit approach to robust linear discriminant analysis Journal Multivariate Analysis, Academic Press, Inc., 101, 2464–2485.
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/.
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