Lda serves as a base class for deriving
all other classes representing the results of classical
and robust Linear Discriminant Analisys methods
A virtual Class: No objects may be created from it.
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 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.
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.
signature(object = "Lda"): prints the results
signature(object = "Lda"): prints summary information
Valentin Todorov [email protected]
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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