The DLDA classifier belongs to the family of Naive Bayes classifiers, where
the distributions of each class are assumed to be multivariate normal and to
share a common covariance matrix.
The DLDA classifier is a modification to the well-known LDA classifier, where
the off-diagonal elements of the pooled sample covariance matrix are assumed
to be zero -- the features are assumed to be uncorrelated. Under multivariate
normality, the assumption uncorrelated features is equivalent to the
assumption of independent features. The feature-independence assumption is a
notable attribute of the Naive Bayes classifier family. The benefit of these
classifiers is that they are fast and have much fewer parameters to estimate,
especially when the number of features is quite large.
The matrix of training observations are given in x. The rows of x
contain the sample observations, and the columns contain the features for each
training observation.
The vector of class labels given in y are coerced to a factor.
The length of y should match the number of rows in x.
An error is thrown if a given class has less than 2 observations because the
variance for each feature within a class cannot be estimated with less than 2
observations.
The vector, prior, contains the a priori class membership for
each class. If prior is NULL (default), the class membership
probabilities are estimated as the sample proportion of observations belonging
to each class. Otherwise, prior should be a vector with the same length
as the number of classes in y. The prior probabilities should be
nonnegative and sum to one.