Multiple Linear Regression in Visual Basic QuickStart Sample

Illustrates how to use the LinearRegressionModel class to perform a multiple linear regression in Visual Basic.

View this sample in: C# F# IronPython

Option Infer On

Imports Extreme.DataAnalysis
Imports Extreme.Statistics
Imports Extreme.Data.Text

' Illustrates the use of the LinearRegressionModel class 
' to perform multiple linear regression.
Module MultipleRegression

    Sub Main()
        ' The license is verified at runtime. We're using
        ' a demo license here. For more information, see
        ' https://numerics.net/trial-key
        Extreme.License.Verify("Demo license")

        ' Multiple linear regression can be performed using 
        ' the LinearRegressionModel class.
        '
        ' This QuickStart sample uses data test scores of 200 high school
        ' students, including science, math, and reading.

        ' First, read the data from a file into a data frame. 
        Dim frame = DelimitedTextFile.ReadDataFrame("..\..\..\..\Data\hsb2.csv")

        ' Now create the regression model. Parameters are the data frame,
        ' the name of the dependent variable, and a string array containing 
        ' the names of the independent variables.
        Dim lm As New LinearRegressionModel(frame,
                "science", {"math", "female", "socst", "read"})

        ' Alternatively, we can use a formula to describe the variables
        ' in the model. The dependent variable goes on the left, the
        ' independent variables on the right of the ~
        Dim lm2 = New LinearRegressionModel(frame,
                "science ~ math + female + socst + read")

        ' We can set model options now, such as whether to exclude 
        ' the constant term:
        ' model.NoIntercept = false

        ' The Fit method performs the actual regression analysis.
        lm.Fit()

        ' The Parameters collection contains information about the regression 
        ' parameters.
        Console.WriteLine("Variable              Value    Std.Error  t-stat  p-Value")
        For Each param As Parameter(Of Double) In lm.Parameters
            ' Parameter objects have the following properties:
            ' - Name, usually the name of the variable:
            ' - Estimated value of the parameter:
            ' - Standard error:
            ' - The value of the t statistic for the hypothesis that the parameter
            '    is zero.
            ' - Probability corresponding to the t statistic.
            Console.WriteLine("{0,-19}{1,12:E4}{2,12:E2}{3,8:F2} {4,7:F4}",
                    param.Name,
                    param.Value,
                    param.StandardError,
                    param.Statistic,
                    param.PValue)
        Next
        Console.WriteLine()

        ' In addition to these properties, Parameter objects have 
        ' a GetConfidenceInterval method that returns 
        ' a confidence interval at a specified confidence level.
        ' Notice that individual parameters can be accessed 
        ' using their numeric index. Parameter 0 is the intercept, 
        ' if it was included.
        Dim confidenceInterval = lm.Parameters(0).GetConfidenceInterval(0.95)
        Console.WriteLine("95% confidence interval for intercept: {0:F4} - {1:F4}",
                confidenceInterval.LowerBound, confidenceInterval.UpperBound)

        ' Parameters can also be accessed by name:
        confidenceInterval = lm.Parameters.Get("math").GetConfidenceInterval(0.95)
        Console.WriteLine("95% confidence interval for 'math': {0:F4} - {1:F4}",
                confidenceInterval.LowerBound, confidenceInterval.UpperBound)
        Console.WriteLine()

        ' There is also a wealth of information about the analysis available
        ' through various properties of the LinearRegressionModel object:
        Console.WriteLine("Residual standard error: {0:F3}", lm.StandardError)
        Console.WriteLine("R-Squared:               {0:F4}", lm.RSquared)
        Console.WriteLine("Adjusted R-Squared:      {0:F4}", lm.AdjustedRSquared)
        Console.WriteLine("F-statistic:             {0:F4}", lm.FStatistic)
        Console.WriteLine("Corresponding p-value:   {0:F5}", lm.PValue)
        Console.WriteLine()

        ' Much of this data can be summarized in the form of an ANOVA table:
        Console.WriteLine(lm.AnovaTable.ToString())

        ' All this information can be printed using the Summarize method.
        ' You will also see summaries using the library in C# interactive.
        Console.WriteLine(lm.Summarize())

        Console.WriteLine("Press Enter key to continue.")
        Console.ReadLine()
    End Sub

End Module