predict.rqda: Restricted Quadratic Discriminant Analysis. Multivariate Observations Classification

Description

Classify multivariate observations with quadratic classification rules built with additional information in conjunction with rqda.

Usage

# S3 method for rqda
predict(object, newdata, prior = object$prior,
        gamma = object$gamma, grouping = NULL, ...)

Value

A list with components

call: The (matched) function call.
class: Matriarchx with the classification for each rule (in columns).
prior: The prior probabilities of the classes used.
posterior: Array with the posterior probabilities of the classes for each rule.
error.rate: True error rate estimation (when grouping specified) for each rule, based on newdata.

Arguments

object: An object of class 'rqda'.
newdata: A data frame of cases to be classified, containing the variables used on creating object. A vector will be interpreted as a row vector.
prior: The prior probabilities of class membership. If unspecified, object$prior probabilities are used. If present, the probabilities must be specified in the order of the factor levels.
gamma: A vector of values specifying which rules to take among the ones in object. If unspecified, all rules built with object$gamma will be used. If present, gamma must be contained in object$gamma.
grouping: A numeric vector or factor with numeric levels specifying the class for each observation. If present, true error rate will be estimated from newdata.
...: Arguments based from or to other methods.

Author

David Conde

Details

This function is a method for the generic function predict() for class 'rqda'.

References

Conde, D., Fernandez, M. A., Rueda, C., and Salvador, B. (2012). Classification of samples into two or more ordered populations with application to a cancer trial. Statistics in Medicine, 31, 3773-3786.

Conde, D., Fernandez, M. A., Salvador, B., and Rueda, C. (2015). dawai: An R Package for Discriminant Analysis with Additional Information. Journal of Statistical Software, 66(10), 1-19. URL http://www.jstatsoft.org/v66/i10/.

Examples

Run this code

data(Vehicle2)
levels(Vehicle2$Class)
## "bus" "opel" "saab" "van"

data <- Vehicle2[, 1:4]
grouping = Vehicle2$Class
levels(grouping) <- c(4, 2, 1, 3)
## classes ordered by increasing size
## 
## according to variable definitions, we can consider 
## the following restrictions on the means vectors:
## mu11 >= mu21 >= mu31 >= mu41
## mu12 >= mu22 >= mu32 >= mu42
## mu13 >= mu23 >= mu33 >= mu43
## 
## we can specify these restrictions by restext = "s>1,2,3"

set.seed(7964)
values <- runif(dim(data)[1])
trainsubset <- values < 0.2
testsubset <- values >= 0.2
obj <- rqda(data, grouping, subset = trainsubset,
            gamma = (1:5)/5, restext = "s>1,2,3")
pred <- predict(obj, newdata = data[testsubset,], 
                grouping = grouping[testsubset])
pred$error.rate
## we can see that the test error rate of the restricted
## rules decrease with gamma:
##                      gamma=0.2 gamma=0.4 gamma=0.6 gamma=0.8  gamma=1
## True error rate (%):  40.14815  39.85185  39.85185  39.11111 39.11111

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