Classify observations in conjunction with `mda`

.

```
# S3 method for mda
predict(object, newdata, type, prior, dimension, g, …)
```

object

a fitted mda object.

newdata

new data at which to make predictions. If missing, the training data is used.

type

kind of predictions: `type = "class"`

(default)
produces a fitted factor, `type = "variates"`

produces a matrix
of discriminant variables (note that the maximal dimension is
determined by the number of subclasses), `type = "posterior"`

produces a matrix of posterior probabilities (based on a gaussian
assumption), `type = "hierarchical"`

produces the predicted
class in sequence for models of dimensions specified by
`dimension`

argument.

prior

the prior probability vector for each class; the default is the training sample proportions.

dimension

the dimension of the space to be used, no larger
than the dimension component of `object`

, and in general less
than the number of subclasses. `dimension`

can be a vector for
use with `type = "hierarchical"`

.

g

???

…

further arguments to be passed to or from methods.

An appropriate object depending on `type`

. `object`

has a
component `fit`

which is regression fit produced by the
`method`

argument to `mda`

. There should be a
`predict`

method for this object which is invoked. This method
should itself take as input `object`

and optionally `newdata`

.

# NOT RUN { data(glass) samp <- sample(1:nrow(glass), 100) glass.train <- glass[samp,] glass.test <- glass[-samp,] glass.mda <- mda(Type ~ ., data = glass.train) predict(glass.mda, glass.test, type = "post") # abbreviations are allowed confusion(glass.mda, glass.test) # }