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QuickStart Samples

Two-Way Anova QuickStart Sample (C#)

Illustrates how to use the TwoWayAnovaModel class to perform a two-way analysis of variance in C#.

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using System;
using System.Data;

using Extreme.DataAnalysis;
using Extreme.Statistics;

namespace Extreme.Statistics.Quickstart.CSharp
{
    /// <summary>
    /// Illustrates the use of the TwoWayAnovaModel class for performing 
    /// a two-way analysis of variance.
    /// </summary>
    class AnovaTwoWay
    {
        /// <summary>
        /// The main entry point for the application.
        /// </summary>
        [STAThread]
        static void Main(string[] args)
        {
            // This example investigates the effect of the color and shape
            // of packages on the sales of the product. The data comes from
            // 12 stores. Packages can be either red, green or blue in color.
            // The shape can be either square or rectangular.

            // Set up the data as anonymous records:
            var values = new[] {
                new { Store = 1, Color = "Blue", Shape = "Square", Sales = 6 },
                new { Store = 2, Color = "Blue", Shape = "Square", Sales = 14 },
                new { Store = 3, Color = "Blue", Shape = "Rectangle", Sales = 19 },
                new { Store = 4, Color = "Blue", Shape = "Rectangle", Sales = 17 },

                new { Store = 5, Color = "Red", Shape = "Square", Sales = 18 },
                new { Store = 6, Color = "Red", Shape = "Square", Sales = 11 },
                new { Store = 7, Color = "Red", Shape = "Rectangle", Sales = 20 },
                new { Store = 8, Color = "Red", Shape = "Rectangle", Sales = 23 },

                new { Store = 9, Color = "Green", Shape = "Square", Sales = 7 },
                new { Store = 10, Color = "Green", Shape = "Square", Sales = 11 },
                new { Store = 11, Color = "Green", Shape = "Rectangle", Sales = 18 },
                new { Store = 12, Color = "Green", Shape = "Rectangle", Sales = 10 },
            };
            var dataFrame = DataFrame.FromObjects(values);

            // Construct the OneWayAnova object.
            var anova = new TwoWayAnovaModel(dataFrame, "Sales", "Color", "Shape");
            // Alternatively, we could have used a formula to define the model:
            anova = new TwoWayAnovaModel(dataFrame, "Sales ~ Color + Shape");

            // Perform the calculation.
            anova.Compute();
            // Verify that the design is balanced:
            if (!anova.IsBalanced)
                Console.WriteLine("The design is not balanced.");
            
            // The AnovaTable property gives us a classic anova table.
            // We can write the table directly to the console:
            Console.WriteLine(anova.AnovaTable.ToString());
            Console.WriteLine();
            
            // A Cell object represents the data in a cell of the model,
            // i.e. the data related to one combination of levels of each factor. 
            // We can use it to access the group means of our color groups.

            // First we get the index so we can easily iterate
            // through the levels:
            var colorFactor = anova.GetFactor(0);
            foreach(string level in colorFactor)
                Console.WriteLine("Mean for square boxes group '{0}': {1:F4}", 
                    level, anova.Cells.Get(level, "Square").Mean);
            
            // We could have accessed the cells directly as well:
            Console.WriteLine("Variance for red, rectangular packages: {0}", 
                anova.Cells.Get("Red", "Rectangle").Variance);
            Console.WriteLine();

            // The RowTotals and ColumnTotals properties permits us to 
            // summarize the data over all levels of a factor. For example, 
            // to get the means of the shape groups, we use:
            var shapeFactor = anova.GetFactor(1);
            foreach(string level in shapeFactor)
                Console.WriteLine("Mean for group '{0}': {1:F4}",
                    level, anova.ColumnTotals.Get(level).Mean);
            Console.WriteLine();

            // We can get the summary data for the entire model 
            // from the TotalCell property:
            Cell totalSummary = anova.TotalCell;
            Console.WriteLine("Summary data:");
            Console.WriteLine("# observations: {0}", totalSummary.Count);
            Console.WriteLine("Grand mean:     {0:F4}", totalSummary.Mean);

            Console.Write("Press any key to exit.");
            Console.ReadLine();
        }
    }
}