MOdist: Markov Operator distance for clustering diffusion processes.

In sde: Simulation and Inference for Stochastic Differential Equations

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.
M	number of splines bases used to approximate the Markov Operator.
rangeval	a vector containing lower and upper limit. Default is the range of x.

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

X	a dist object

Author(s)

Stefano Maria Iacus

References

De Gregorio, A. Iacus, S.M. (2008) Clustering of discretely observed diffusion processes, Computational Statistics and Data Analysis, 54(12), 598-606, doi: 10.1016/j.csda.2009.10.005.

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)

sde documentation built on Aug. 9, 2022, 9:05 a.m.