WLS, weighted linear regression in pure Go w/o any 3d party dependencies or frameworks.
package main
import (
"fmt"
"github.com/vspaz/wls-go/pkg/models"
)
func main() {
xPoints := []float64{1.0, 2.0, 3.0, 4.0, 5.0, 6.0, 7.0} // ∈ {int8, int16, int32, int, float32, float64}
yPoints := []float64{1.0, 3.0, 4.0, 5.0, 2.0, 3.0, 4.0} // ∈ {int8, int16, int32, int, float32, float64}
weights := []float64{10.0, 1.0, 3.0, 8.0, 14.0, 21.0, 13.0} // ∈ {int8, int16, int32, int, float32, float64}
wls := models.NewWlsWithWeights(xPoints, yPoints, weights)
point := wls.FitLinearRegression()
fmt.Println(point.GetSlope())
fmt.Println(point.GetIntercept())
}WLS is based on the OLS method and help solve problems of model inadequacy or violations of the basic regression assumptions.
Estimating a linear regression with WLS is useful, but can appear to be daunting w/o special stats packages, such as Python statsmodels or Pandas.