Description Usage Arguments Details Value References See Also Examples

Classify multivariate observations in conjunction with `lda`

, and also
project data onto the linear discriminants.

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`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 |

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

`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 `"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.

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 |

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.

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