plotMDS: Multi-dimensional Scaling Plot of Learned Pattern Similarity

In LPStimeSeries: Learned Pattern Similarity and Representation for Time Series

Description

Plot the scaling coordinates of the Learned Pattern Similarity.

Usage

plotMDS(object, newdata, classinfo, k=2, palette=NULL, pch=20, ...) 

Arguments

object	an object of class learnPattern, as that created by the function learnPattern.
newdata	a data frame or matrix containing the data for similarity computation.
classinfo	labels for the time series for color-coding.
k	number of dimensions for the scaling coordinates.
palette	colors to use to distinguish the classes; length must be the equal to the number of levels.
pch	plotting symbols to use.
...	other graphical parameters.

Value

The output of cmdscale on scaled Learned Pattern similarity is returned invisibly.

Note

If k > 2, pairs is used to produce the scatterplot matrix of the coordinates.

The entries of the similarity matrix is divided by the maximum possible similarity which is 2*sum(object$nobs)

Author(s)

Mustafa Gokce Baydogan

See Also

learnPattern

Examples

set.seed(1)
data(GunPoint)
## Learn patterns on GunPoint training series with default parameters
ensemble=learnPattern(GunPoint$trainseries)
plotMDS(ensemble, GunPoint$trainseries,GunPoint$trainclass)

## Using different symbols for the classes:
plotMDS(ensemble, GunPoint$trainseries,GunPoint$trainclass, 
         palette=rep(1, 2), pch=as.numeric(GunPoint$trainclass))
         
## Learn patterns on GunPoint training series with random splits
ensemble=learnPattern(GunPoint$trainseries,random.split=1)
plotMDS(ensemble, GunPoint$trainseries,GunPoint$trainclass,main='Random Splits')

LPStimeSeries documentation built on May 2, 2019, 8:25 a.m.