plot_phase_1d

Creates a phase diagram plotting dZ vs Z, useful for visualizing autonomous
dynamics and identifying fixed points.

A comprehensive toolkit for discovering differential and difference
equations from empirical time series data using symbolic regression. The package
implements a complete workflow from data preprocessing (including Total Variation
Regularized differentiation for noisy economic data), visual exploration of
dynamical structure, and symbolic equation discovery via genetic algorithms.
It leverages a high-performance 'Julia' backend ('SymbolicRegression.jl') to provide
industrial-grade robustness, physics-informed constraints, and rigorous
out-of-sample validation. Designed for economists, physicists, and researchers
studying dynamical systems from observational data.

José Mauricio Gómez Julián

EmpiricalDynamics

Empirical Discovery of Differential Equations from Time Series
Data

plot_phase_1d function

<dl><dt>data</dt>
<dd>Data frame.</dd>
<dt>z_var</dt>
<dd>Name of state variable Z.</dd>
<dt>dz_var</dt>
<dd>Name of derivative dZ (auto-constructed if starts with "d_").</dd>
<dt>show_zero_line</dt>
<dd>Add horizontal line at dZ = 0?</dd>
<dt>show_fit</dt>
<dd>Add nonparametric fit?</dd>
<dt>fit_method</dt>
<dd>Method for fit: "loess", "gam", or "spline".</dd></dl>

Arguments

1D Phase Diagram — plot_phase_1d

<dl>

<dt>data</dt>
<dd>Data frame.</dd>


<dt>z_var</dt>
<dd>Name of state variable Z.</dd>


<dt>dz_var</dt>
<dd>Name of derivative dZ (auto-constructed if starts with "d_").</dd>


<dt>show_zero_line</dt>
<dd>Add horizontal line at dZ = 0?</dd>


<dt>show_fit</dt>
<dd>Add nonparametric fit?</dd>


<dt>fit_method</dt>
<dd>Method for fit: "loess", "gam", or "spline".</dd>

</dl>

plot_phase_1d: 1D Phase Diagram

Description

Usage

Value

Arguments

Details