chaoticInvariant: Class for chaotic invariant objects

Description S3 METHODS See Also Examples

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

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## 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.