estimate

Several chaotic invariants are estimated by using linear regression. This
function provides a common interface for the estimate of all these parameters
(see <code>corrDim</code>, <code>dfa</code> and <code>maxLyapunov</code> 
for examples).

Functions for nonlinear time series analysis. This package permits
the computation of the  most-used nonlinear statistics/algorithms
including generalized correlation dimension, information dimension,
largest Lyapunov exponent, sample entropy and Recurrence
Quantification Analysis (RQA), among others. Basic routines
for surrogate data testing are also included. Part of this work
was based on the  book "Nonlinear time series analysis" by
Holger Kantz and Thomas Schreiber (ISBN: 9780521529020).

Constantino A. Garcia

nonlinearTseries

Nonlinear Time Series Analysis

Gunther Sawitzki

estimate function

<dl><dt>x</dt>
<dd>An object containing all the information needed for the estimate.</dd>
<dt>regression.range</dt>
<dd>Range of values on the x-axis on which the regression
is performed.</dd>
<dt>do.plot</dt>
<dd>Logical value. If TRUE (default value), a plot of the 
regression is shown.</dd>
<dt>...</dt>
<dd>Additional parameters.</dd></dl>

Arguments

Author

Estimate several chaotic invariants using linear regression — estimate

<dl>

<dt>x</dt>
<dd>An object containing all the information needed for the estimate.</dd>


<dt>regression.range</dt>
<dd>Range of values on the x-axis on which the regression
is performed.</dd>


<dt>do.plot</dt>
<dd>Logical value. If TRUE (default value), a plot of the 
regression is shown.</dd>


<dt>...</dt>
<dd>Additional parameters.</dd>

</dl>

estimate: Estimate several chaotic invariants using linear regression

Description

Usage

Value

Arguments

Author

References