qda
From MASS v7.347
by Brian Ripley
Quadratic discriminant analysis.
 Keywords
 multivariate
Usage
qda(x, …)# S3 method for formula
qda(formula, data, …, subset, na.action)
# S3 method for default
qda(x, grouping, prior = proportions,
method, CV = FALSE, nu, …)
# S3 method for data.frame
qda(x, …)
# S3 method for matrix
qda(x, grouping, …, subset, na.action)
Arguments
 formula

A formula of the form
groups ~ x1 + x2 + …
That is, the response is the grouping factor and the right hand side specifies the (nonfactor) discriminators.  data

Data frame from which variables specified in
formula
are preferentially to be taken.  x
 (required if no formula is given as the principal argument.) a matrix or data frame or Matrix containing the explanatory variables.
 grouping
 (required if no formula principal argument is given.) a factor specifying the class for each observation.
 prior
 the prior probabilities of class membership. If unspecified, the class proportions for the training set are used. If specified, the probabilities should be specified in the order of the factor levels.
 subset
 An index vector specifying the cases to be used in the training sample. (NOTE: If given, this argument must be named.)
 na.action

A function to specify the action to be taken if
NA
s are found. The default action is for the procedure to fail. An alternative is na.omit, which leads to rejection of cases with missing values on any required variable. (NOTE: If given, this argument must be named.)  method

"moment"
for standard estimators of the mean and variance,"mle"
for MLEs,"mve"
to usecov.mve
, or"t"
for robust estimates based on a t distribution.  CV
 If true, returns results (classes and posterior probabilities) for leaveoutout crossvalidation. Note that if the prior is estimated, the proportions in the whole dataset are used.
 nu

degrees of freedom for
method = "t"
.  …
 arguments passed to or from other methods.
Details
Uses a QR decomposition which will give an error message if the withingroup variance is singular for any group.
Value
an object of class "qda"
containing the following components:
i
, scaling[,,i]
is an array which transforms observations
so that withingroups covariance matrix is spherical.
CV=TRUE
, when the return value is a list with components:
References
Venables, W. N. and Ripley, B. D. (2002) Modern Applied Statistics with S. Fourth edition. Springer. Ripley, B. D. (1996) Pattern Recognition and Neural Networks. Cambridge University Press.
See Also
Examples
library(MASS)
tr < sample(1:50, 25)
train < rbind(iris3[tr,,1], iris3[tr,,2], iris3[tr,,3])
test < rbind(iris3[tr,,1], iris3[tr,,2], iris3[tr,,3])
cl < factor(c(rep("s",25), rep("c",25), rep("v",25)))
z < qda(train, cl)
predict(z,test)$class
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