chaoticInvariant: Class for chaotic invariant objects

In fractal: A Fractal Time Series Modeling and Analysis Package

Description

Class constructor for chaoticInvariant.

S3 METHODS

eda.plot

plots an extended data analysis plot, which graphically summarizes the process of obtaining a correlation dimension estimate. A time history, phase plane embeddding, correlation summation curves, and the slopes of correlation summation curves as a function of scale are plotted.

plot

plots the correlation summation curves on a log-log scale. The following options may be used to adjust the plot components:

type

Character string denoting the type of data to be plotted. The "stat" option plots the correlation summation curves while the "dstat" option plots a 3-point estimate of the derivatives of the correlation summation curves. The "slope" option plots the estimated slope of the correlation summation curves as a function of embedding dimension. Default: "stat".

fit

Logical flag. If TRUE, a regression line is overlaid for each curve. Default: TRUE.

grid

Logical flag. If TRUE, a grid is overlaid on the plot. Default: TRUE.

legend

Logical flag. If TRUE, a legend of the estimated slopes as a function of embedding dimension is displayed. Default: TRUE.

...

Additional plot arguments (set internally by the par function).

print

prints a qualitiative summary of the results.

See Also

infoDim, corrDim, lyapunov.

Examples

## create a faux object of class chaoticInvariant 
faux.data <- list(matrix(rnorm(1024), ncol=2), matrix(1:512))
chaoticInvariant(faux.data,
    dimension   = 1:2,
    n.embed     = 10,
    n.reference = 50,
    n.neighbor  = 35,
    tlag        = 10,
    olag        = 15,
    resolution  = 2,
    series.name = "my series",
    series      = 1:10,
    ylab        = "log2(C2)",
    xlab        = "log2(scale)",
    metric      = Inf,
    invariant   = "correlation dimension")

Example output

Loading required package: splus2R
Loading required package: ifultools
Correlation dimension for my series
-----------------------------------
Embedding points       : 10 
Embedding dimension(s) : 1 2 
Time lag               : 10 
Oribital lag           : 15 
Distance metric        : L-Inf 
Invariant estimate(s)  : 0.001 -0.002 

fractal documentation built on May 1, 2019, 8:04 p.m.