Last data update: 2014.03.03
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R: Markov Operator distance for clustering diffusion processes.
Markov Operator distance for clustering diffusion processes.
Description
Markov Operator distance for clustering diffusion processes.
Usage
MOdist(x, M=50, rangeval=range(x, na.rm=TRUE, finite = TRUE))
Arguments
x |
one or multi-dimensional time series.
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M |
number of splines bases used to approximate the Markov Operator.
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rangeval |
a vector containing lower and upper limit. Default is the range of x .
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Details
This function return a lower triangular dist object to be further used
in cluster analysis (see examples below).
If x is a one-dimensional time series, the output is the scalar 0, not a dist object.
If x has less than 2 observations, NA is returned.
If time series x contains missing data, then x is converted to a zoo object and
missing data are imputed by interpolation.
Value
Author(s)
Stefano Maria Iacus
References
De Gregorio, A. Iacus, S.M. (2008) Clustering of discretely observed diffusion processes, http://arxiv.org/abs/0809.3902
Examples
## Not run:
data(quotes)
plot(quotes)
d <- MOdist(quotes)
cl <- hclust( d )
groups <- cutree(cl, k=4)
cmd <- cmdscale(d)
plot( cmd, col=groups)
text( cmd, labels(d) , col=groups)
plot(quotes, col=groups)
plot(quotes, col=groups,ylim=range(quotes))
## End(Not run)
Results
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