# 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 class '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 ret - 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 probabili - ...
- 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
class The MAP classification (a factor) posterior posterior probabilities for the classes x 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

```
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 7.3-19, License: GPL-2 | GPL-3*