predict.qda               package:MASS               R Documentation

Classify from Quadratic Discriminant Analysis

Description:

     Classify multivariate observations in conjunction with 'qda'

Usage:

     ## S3 method for class 'qda':
     predict(object, newdata, prior = object$prior,
             method = c("plug-in", "predictive", "debiased", "looCV"), ...)

Arguments:

  object: object of class '"qda"' 

 newdata: data frame of cases to be classified or, if object has a
          formula, a data frame with columns of the same names as the
          variables used. A vector will be interpreted as a row vector.
           If newdata is missing, an attempt will be made to retrieve
          the data used to fit the 'qda' object. 

   prior: The prior probabilities of the classes, by default the
          proportions in the training set or what was set in the call
          to 'qda'. 

  method: This determines how the parameter estimation is handled. With
          '"plug-in"' (the default) the usual unbiased parameter
          estimates are used and assumed to be correct. With
          '"debiased"' an unbiased estimator of the log posterior
          probabilities is used, and with '"predictive"' the parameter
          estimates are integrated out using a vague prior.  With
          '"looCV"' the leave-one-out cross-validation fits to the
          original dataset are computed and returned. 

     ...: arguments based from or to other methods 

Details:

     This function is a method for the generic function 'predict()' for
     class '"qda"'. It can be invoked by calling 'predict(x)' for an
     object 'x' of the appropriate class, or directly by calling
     'predict.qda(x)' regardless of the class of the object.

     Missing values in 'newdata' are handled by returning 'NA' if the
     quadratic discriminants cannot be evaluated. If 'newdata' is
     omitted and the 'na.action' of the fit omitted cases, these will
     be omitted on the prediction.

Value:

     a list with components

   class: The MAP classification (a factor) 

posterior: posterior probabilities for the classes 

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:

     'qda', 'lda', 'predict.lda'

Examples:

     data(iris3)
     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)))
     zq <- qda(train, cl)
     predict(zq, test)$class

