dqda: Diagonal Quadratic Discriminant Analysis (DQDA)
In ramey/sparsediscrim: Sparse and Regularized Discriminant Analysis

Description Usage Arguments Details Value References Examples

Given a set of training data, this function builds the Diagonal Quadratic Discriminant Analysis (DQDA) classifier, which is often attributed to Dudoit et al. (2002). The DQDA classifier belongs to the family of Naive Bayes classifiers, where the distributions of each class are assumed to be multivariate normal. Note that the DLDA classifier is a special case of the DQDA classifier.

The DQDA classifier is a modification to QDA, where the off-diagonal elements of the pooled sample covariance matrix are set to zero.

dqda(x, ...)

## Default S3 method:
dqda(x, y, prior = NULL, ...)

## S3 method for class 'formula'
dqda(formula, data, prior = NULL, ...)

## S3 method for class 'dqda'
predict(object, newdata, ...)

`x`	matrix containing the training data. The rows are the sample observations, and the columns are the features.
`...`	additional arguments
`y`	vector of class labels for each training observation
`prior`	vector with prior probabilities for each class. If NULL (default), then equal probabilities are used. See details.
`formula`	A formula of the form `groups ~ x1 + x2 + ...` That is, the response is the grouping factor and the right hand side specifies the (non-factor) discriminators.
`data`	data frame from which variables specified in `formula` are preferentially to be taken.
`object`	trained DQDA object
`newdata`	matrix of observations to predict. Each row corresponds to a new observation.

The DQDA classifier is a modification to the well-known QDA classifier, where the off-diagonal elements of each class 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.

dqda object that contains the trained DQDA classifier

list predicted class memberships of each row in newdata

Dudoit, S., Fridlyand, J., & Speed, T. P. (2002). "Comparison of Discrimination Methods for the Classification of Tumors Using Gene Expression Data," Journal of the American Statistical Association, 97, 457, 77-87.

n <- nrow(iris)
train <- sample(seq_len(n), n / 2)
dqda_out <- dqda(Species ~ ., data = iris[train, ])
predicted <- predict(dqda_out, iris[-train, -5])$class

dqda_out2 <- dqda(x = iris[train, -5], y = iris[train, 5])
predicted2 <- predict(dqda_out2, iris[-train, -5])$class
all.equal(predicted, predicted2)