| predict.qda {MASS} | R Documentation |
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 "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 |
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.
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