Description Usage Arguments Value Note Author(s) See Also Examples

Plot the scaling coordinates of the Learned Pattern Similarity.

1 |

`object` |
an object of class |

`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. |

The output of `cmdscale`

on scaled Learned Pattern
similarity is returned invisibly.

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)`

Mustafa Gokce Baydogan

1 2 3 4 5 6 7 8 9 10 11 12 13 | ```
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')
``` |

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