Classify from Quadratic Discriminant Analysis
Classify multivariate observations in conjunction with qda
## S3 method for class 'qda'
predict(object, newdata, prior = object$prior,
method = c("plug-in", "predictive", "debiased", "looCV"), ...)object |
object of class |
newdata |
data frame of cases to be classified or, if |
prior |
The prior probabilities of the classes, by default the proportions in the
training set or what was set in the call to |
method |
This determines how the parameter estimation is handled. With |
... |
arguments based from or to other methods |
This function is a method for the generic function
predict() for class "qda".
It can be invoked by calling predict(x) for an
object x of the appropriate class, or directly by
calling predict.qda(x) regardless of the
class of the object.
Missing values in newdata are handled by returning NA if the
quadratic discriminants cannot be evaluated. If newdata is omitted and
the na.action of the fit omitted cases, these will be omitted on the
prediction.
a list with components
class |
The MAP classification (a factor) |
posterior |
posterior probabilities for the classes |
Venables, W. N. and Ripley, B. D. (2002) Modern Applied Statistics with S. Fourth edition. Springer.
Ripley, B. D. (1996) Pattern Recognition and Neural Networks. Cambridge University Press.
tr <- sample(1:50, 25)
train <- rbind(iris3[tr,,1], iris3[tr,,2], iris3[tr,,3])
test <- rbind(iris3[-tr,,1], iris3[-tr,,2], iris3[-tr,,3])
cl <- factor(c(rep("s",25), rep("c",25), rep("v",25)))
zq <- qda(train, cl)
predict(zq, test)$classPlease choose more modern alternatives, such as Google Chrome or Mozilla Firefox.