# predict.lda

##### Classify Multivariate Observations by Linear Discrimination

Classify multivariate observations in conjunction with `lda`

, and also
project data onto the linear discriminants.

- Keywords
- multivariate

##### Usage

```
# S3 method for lda
predict(object, newdata, prior = object$prior, dimen,
method = c("plug-in", "predictive", "debiased"), …)
```

##### Arguments

- object
object of class

`"lda"`

- 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`lda`

object.- prior
The prior probabilities of the classes, by default the proportions in the training set or what was set in the call to

`lda`

.- dimen
the dimension of the space to be used. If this is less than

`min(p, ng-1)`

, only the first`dimen`

discriminant components are used (except for`method="predictive"`

), and only those dimensions are returned in`x`

.- 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.- …
arguments based from or to other methods

##### Details

This function is a method for the generic function `predict()`

for
class `"lda"`

. It can be invoked by calling `predict(x)`

for
an object `x`

of the appropriate class, or directly by calling
`predict.lda(x)`

regardless of the class of the object.

Missing values in `newdata`

are handled by returning `NA`

if the
linear discriminants cannot be evaluated. If `newdata`

is omitted and
the `na.action`

of the fit omitted cases, these will be omitted on the
prediction.

This version centres the linear discriminants so that the
weighted mean (weighted by `prior`

) of the group centroids is at
the origin.

##### Value

a list with components

The MAP classification (a factor)

posterior probabilities for the classes

the scores of test cases on up to `dimen`

discriminant variables

##### 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)))
z <- lda(train, cl)
predict(z, test)$class
# }
```

*Documentation reproduced from package MASS, version 7.3-51.3, License: GPL-2 | GPL-3*