func:fitLinearRegression

fitLinearRegression(grid, opts: null)

Given a grid of x, y coordinates compute the best fit linear regression equation using the ordinary least squares method. The first column of the grid is used for x and the second column is y. Any rows without a Number for both x and y are skipped. Any special Numbers (infinity/NaN) are skipped.

Options:

• x: column name to use for x if not first column
• y: column name to use for y if not second column

The resulting linear equation is:

yᵢ = mxᵢ + b

The equation is returned as a dictionary with these keys:

• m: slope of the best fit regression line
• b: intercept of the best fit regression line
• r2: R² coefficient of determination as a number between 1.0 (perfect correlation) and 0.0 (no correlation)
• xmin: minimum value of x variable in sample data
• xmax: maximum value of x variable in sample data
• ymin: minimum value of y variable in sample data
• ymax: maximum value of y variable in sample data

Also see matrixFitLinearRegression() to compute a multiple linear regression.

Example:

data: [{x:1, y:2},
       {x:2, y:4},
       {x:4, y:4},
       {x:6, y:5}].toGrid
 fitLinearRegression(data)

 >>> {m:0.4915, b: 2.1525, r2: 0.7502}

def	func:fitLinearRegression
is	func
lib	lib:math
name	fitLinearRegression

feature	Feature namespace of definitions formatted as feature:name
func	Axon function