function::symline - Linux

Overview

function::symline is a specialized conversion utility that transforms a function-based linear model into a SymPy symbolic object. It provides an efficient means of converting linear models fitted using traditional statistical software like R or Python into a format that can be easily manipulated and analyzed symbolically.

Syntax

function::symline [options] function [arguments]

Options/Flags

• -c, –coef: Print coefficients as symbols.
• -n, –names: Print variable names as symbols.
• -s, –simplify: Simplify the expression before printing.
• -h, –help: Display this help message.

Examples

Simple Linear Model:

Convert a simple linear model y = 2x + 1 into SymPy:

function::symline "2*x + 1"

Multiple Linear Model:

Convert a multiple linear model y = 3x + 2y + 1 into SymPy with named variables:

function::symline -n "3*x + 2*y + 1"

Polynomials:

Convert a polynomial model y = x^2 + 3x + 2 into SymPy:

function::symline "x**2 + 3*x + 2"

Common Issues

• Missing Arguments: Ensure that all required arguments are specified.
• Syntax Errors: Verify that the function string is correctly formatted.
• Evaluation Errors: Check if the function is valid for symbolic evaluation.

Integration

SymPy Manipulation:

Once converted, the SymPy object can be manipulated using the full range of SymPy functions for further analysis, e.g.:

import sympy
model = function::symline("2*x + 1")
diff_model = sympy.diff(model, x)  # Compute derivative

Command Pipelines:

function::symline can be integrated into command pipelines to automate complex tasks, e.g.:

echo "2*x + 1" | function::symline | sympy diff x

Related Commands

• scipy.stats.linregress: Fit linear regression models in Python.
• statsmodels.api.OLS: Fit linear regression models in R.
• sympy.diff: Compute derivatives of symbolic expressions.