Class constructor for `chaoticInvariant`

.

- 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).

prints a qualitiative summary of the results.

`infoDim`

, `corrDim`

, `lyapunov`

.

1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 | ```
## 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")
``` |

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