predict.qda {MASS} | R Documentation |

## Classify from Quadratic Discriminant Analysis

### Description

Classify multivariate observations in conjunction with `qda`

### Usage

```
## S3 method for class 'qda'
predict(object, newdata, prior = object$prior,
method = c("plug-in", "predictive", "debiased", "looCV"), ...)
```

### Arguments

`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 |

`method` |
This determines how the parameter estimation is handled. With |

`...` |
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

`class` |
The MAP classification (a factor) |

`posterior` |
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

```
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
```

*MASS*version 7.3-60.2 Index]