R: Predict values using new time series values via a...

wpstCLASS {wavethresh} | R Documentation

## Predict values using new time series values via a non-decimated wavelet packet discrimination object.

### Description

Given a timeseries (`timeseries`) and another time series of categorical values (`groups`) the `makewpstDO` produces a model that permits discrimination of the `groups` series using a discriminant analysis based on a restricted set of non-decimated wavelet packet coefficients of `timeseries`. The current function enables new `timeseries` data, to be used in conjunction with the model to generate new, predicted, values of the `groups` time series.

### Usage

    wpstCLASS(newTS, wpstDO)

### Arguments

`newTS` | A new segment of time series values, of the same time series that was used as the dependent variable used to construct the wpstDO object
`wpstDO` | An object that uses values of a dependent time series to build a discriminatory model of a groups time series. Output from the `makewpstDO` function

### Details

This function performs the same nondecimated wavelet packet (NDWPT) transform of the `newTS` data that was used to analyse the original `timeseries` and the details of this transform are stored within the `wpstDO` object. Then, using information that was recorded in `wpstDO` the packets with the same level/index are extracted from the new NDWPT and formed into a matrix. Then the linear discriminant variables, again stored in `wpstDO` are used to form predictors of the original `groups` time series, ie new values of `groups` that correspond to the new values of `timeseries`.

### Value

The prediction using the usual R `predict.lda` function. The predicted values are stored in the `class` component of that list.

### Author(s)

G P Nason

### See Also

`makewpstDO`

### Examples

    #
    # See example at the end of help page for makewpstDO
    #

[Package wavethresh version 4.6.8 Index]