predict.kda: Classify Multivariate Observations Based on Kernel Rules
In schiffner/locClass: Collection of Local Classification Methods

Description Usage Arguments Details Value References See Also Examples

Classify multivariate observations in conjunction with kda.

1	predict.kda(object, newdata, ...)

`object`	Object of class `"kda"`.
`newdata`	A `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 `kda` object.
`...`	Further arguments.

This function is a method for the generic function predict() for class "kda". It can be invoked by calling predict(x) for an object x of the appropriate class, or directly by calling predict.kda(x) regardless of the class of the object.

A list with components:

`class`	The predicted class labels (a `factor`).
`posteriors`	Matrix of class posterior probabilities.

Hastie, T., Tibshirani, R. and Friedman, J. (2009), The Elements of Statistical Learning: Data Mining, Inference and Prediction. Second edition, Springer, New York.

kda.

Other observation_specific majority: kda

  fit <- kda(Species ~ Sepal.Length + Sepal.Width, data = iris,
             wf = "gaussian", bw = 0.5)
  pred <- predict(fit)
  mean(pred$class != iris$Species)