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Separate README for v1 and v2
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‎README.md

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regression v2
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=======
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[![License][license-image]][license-url]
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[license-image]: http://img.shields.io/badge/license-MIT-green.svg?style=flat-square
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[license-url]: LICENSE
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This repository contains v2 of regression library that provides totally different API from the original version.
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Goで重回帰分析するライブラリ
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Multivariable Linear Regression in Go (golang)
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v2 is under `v2/` directory, or you can find at https://github.com/anyappinc/regression/tree/master/v2.
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*NOTE: This project is originally based on https://github.com/sajari/regression*
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installation
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------------
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$ go get github.com/anyappinc/regression/v2
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example usage
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-------------
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Import the package, create a regression and add data to it. You can use as many variables as you like, in the below example there are 3 variables for each observation.
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```go
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package main
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import (
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"fmt"
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"github.com/anyappinc/regression/v2"
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)
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func main() {
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r := regression.NewRegression()
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r.SetObjectiveVariableLabel("Murders per annum per 1,000,000 inhabitants")
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r.SetExplanatoryVariableLabel(0, "Inhabitants")
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r.SetExplanatoryVariableLabel(1, "Percent with incomes below $5000")
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r.SetExplanatoryVariableLabel(2, "Percent unemployed")
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r.AddObservations(
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regression.NewObservation(11.2, []float64{587000, 16.5, 6.2}),
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regression.NewObservation(13.4, []float64{643000, 20.5, 6.4}),
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regression.NewObservation(40.7, []float64{635000, 26.3, 9.3}),
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regression.NewObservation(5.3, []float64{692000, 16.5, 5.3}),
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regression.NewObservation(24.8, []float64{1248000, 19.2, 7.3}),
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regression.NewObservation(12.7, []float64{643000, 16.5, 5.9}),
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regression.NewObservation(20.9, []float64{1964000, 20.2, 6.4}),
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regression.NewObservation(35.7, []float64{1531000, 21.3, 7.6}),
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regression.NewObservation(8.7, []float64{713000, 17.2, 4.9}),
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regression.NewObservation(9.6, []float64{749000, 14.3, 6.4}),
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regression.NewObservation(14.5, []float64{7895000, 18.1, 6}),
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regression.NewObservation(26.9, []float64{762000, 23.1, 7.4}),
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regression.NewObservation(15.7, []float64{2793000, 19.1, 5.8}),
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regression.NewObservation(36.2, []float64{741000, 24.7, 8.6}),
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regression.NewObservation(18.1, []float64{625000, 18.6, 6.5}),
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regression.NewObservation(28.9, []float64{854000, 24.9, 8.3}),
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regression.NewObservation(14.9, []float64{716000, 17.9, 6.7}),
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regression.NewObservation(25.8, []float64{921000, 22.4, 8.6}),
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regression.NewObservation(21.7, []float64{595000, 20.2, 8.4}),
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regression.NewObservation(25.7, []float64{3353000, 16.9, 6.7}),
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)
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model, err := r.Run()
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if err != nil {
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log.Fatalln(err)
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}
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fmt.Printf("Regression formula:\n%v\n", model.FormulaString())
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}
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```
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Note: You can also add observations one by one.
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Once calculated, you can look at the R^2, Standard Error, ANOVA, Coefficients, etc. e.g.
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```go
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// Get the coefficient for the "Inhabitants" variable 0:
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c := model.ExplanatoryVars[0].Coeff
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```
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You can also use the model to predict new observation
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```go
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prediction, err := model.Predict([]float64{587000, 16.5, 6.2})
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```
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The original library that this one is based on is available at https://github.com/sajari/regression.

‎v2/README.md

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regression v2
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=======
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[go-reference-badge]: https://pkg.go.dev/badge/github.com/anyappinc/regression/v2.svg
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[go-reference-url]: https://pkg.go.dev/github.com/anyappinc/regression/v2
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[license-image]: http://img.shields.io/badge/license-MIT-green.svg?style=flat-square
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[license-url]: LICENSE
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[![Go Reference][go-reference-badge]][go-reference-url]
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[![License][license-image]][license-url]
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Multiple linear regression analysis library written in Go
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*NOTE: This project is originally based on https://github.com/sajari/regression*
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installation
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------------
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$ go get github.com/anyappinc/regression/v2
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example usage
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-------------
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Import the package, create a regression and add data to it. You can use as many variables as you like, in the below example there are 3 variables for each observation.
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```go
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package main
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import (
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"fmt"
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"github.com/anyappinc/regression/v2"
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)
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func main() {
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r := regression.NewRegression()
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r.SetObjectiveVariableLabel("Murders per annum per 1,000,000 inhabitants")
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r.SetExplanatoryVariableLabel(0, "Inhabitants")
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r.SetExplanatoryVariableLabel(1, "Percent with incomes below $5000")
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r.SetExplanatoryVariableLabel(2, "Percent unemployed")
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r.AddObservations(
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regression.NewObservation(11.2, []float64{587000, 16.5, 6.2}),
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regression.NewObservation(13.4, []float64{643000, 20.5, 6.4}),
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regression.NewObservation(40.7, []float64{635000, 26.3, 9.3}),
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regression.NewObservation(5.3, []float64{692000, 16.5, 5.3}),
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regression.NewObservation(24.8, []float64{1248000, 19.2, 7.3}),
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regression.NewObservation(12.7, []float64{643000, 16.5, 5.9}),
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regression.NewObservation(20.9, []float64{1964000, 20.2, 6.4}),
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regression.NewObservation(35.7, []float64{1531000, 21.3, 7.6}),
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regression.NewObservation(8.7, []float64{713000, 17.2, 4.9}),
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regression.NewObservation(9.6, []float64{749000, 14.3, 6.4}),
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regression.NewObservation(14.5, []float64{7895000, 18.1, 6}),
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regression.NewObservation(26.9, []float64{762000, 23.1, 7.4}),
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regression.NewObservation(15.7, []float64{2793000, 19.1, 5.8}),
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regression.NewObservation(36.2, []float64{741000, 24.7, 8.6}),
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regression.NewObservation(18.1, []float64{625000, 18.6, 6.5}),
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regression.NewObservation(28.9, []float64{854000, 24.9, 8.3}),
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regression.NewObservation(14.9, []float64{716000, 17.9, 6.7}),
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regression.NewObservation(25.8, []float64{921000, 22.4, 8.6}),
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regression.NewObservation(21.7, []float64{595000, 20.2, 8.4}),
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regression.NewObservation(25.7, []float64{3353000, 16.9, 6.7}),
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)
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model, err := r.Run()
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if err != nil {
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log.Fatalln(err)
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}
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fmt.Printf("Regression formula:\n%v\n", model.FormulaString())
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}
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```
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Note: You can also add observations one by one.
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Once calculated, you can look at the R^2, Standard Error, ANOVA, Coefficients, etc. e.g.
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```go
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// Get the coefficient for the "Inhabitants" variable 0:
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c := model.ExplanatoryVars[0].Coeff
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```
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You can also use the model to predict new observation
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```go
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prediction, err := model.Predict([]float64{587000, 16.5, 6.2})
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```

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