# predict.qda

##### Classify from Quadratic Discriminant Analysis

Classify multivariate observations in conjunction with `qda`

- Keywords
- multivariate

##### Usage

```
# S3 method for 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

The MAP classification (a factor)

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

##### Examples

```
# NOT RUN {
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
# }
```

*Documentation reproduced from package MASS, version 7.3-51.1, License: GPL-2 | GPL-3*