Libraries.Compute.Statistics.Tests.Regression Documentation
This class conducts an Ordinary Least Squares regression on a DataFrame. By default, an intercept is calculated and included in the model. More information about this kind of statistical test can be found at here: https://en.wikipedia.org/wiki/Ordinary_least_squares. It was partially adapted from the same model in Apache Commons, but was expanded upon to simplify the library and add a variety of helper actions that were missing. More information about this class can be found on its documentation page: https://commons.apache.org/proper/commons-math/javadocs/api-3.6/org/apache/commons/math3/stat/regression/OLSMultipleLinearRegression.html
Example Code
use Libraries.Compute.Statistics.DataFrame
use Libraries.Compute.Statistics.Tests.Regression
DataFrame frame
frame:Load("data.csv")
frame:AddSelectedColumn(0)
frame:AddSelectedFactorRange(1,3)
Regression regression = frame:RegressionOnSelected()
output regression:GetSummary()
Inherits from: Libraries.Compute.Statistics.DataFrameCalculation, Libraries.Language.Object
Actions Documentation
Calculate(Libraries.Compute.Statistics.DataFrame frame)
Total sum of squares: index for each dependent variable
Parameters
Compare(Libraries.Language.Object object)
This action compares two object hash codes and returns an integer. The result is larger if this hash code is larger than the object passed as a parameter, smaller, or equal. In this case, -1 means smaller, 0 means equal, and 1 means larger. This action was changed in Quorum 7 to return an integer, instead of a CompareResult object, because the previous implementation was causing efficiency issues.
Parameters
- Libraries.Language.Object: The object to compare to.
Return
integer: The Compare result, Smaller, Equal, or Larger.
Example
Object o
Object t
integer result = o:Compare(t) //1 (larger), 0 (equal), or -1 (smaller)
Equals(Libraries.Language.Object object)
This action determines if two objects are equal based on their hash code values.
Parameters
- Libraries.Language.Object: The to be compared.
Return
boolean: True if the hash codes are equal and false if they are not equal.
Example
use Libraries.Language.Object
use Libraries.Language.Types.Text
Object o
Text t
boolean result = o:Equals(t)
GetAdjustedEffectSize()
Returns the total adjusted effect size, in statistics typically termed adjusted R^2 (R-squared). This action returns 0 unless the regression has been calculated.
Return
number: The adjusted R^2 if the regression is calculated
GetAdjustedEffectSizeVector()
Returns the total adjusted effect size, in statistics typically termed adjusted R^2 (R-squared). This action returns 0 unless the regression has been calculated.
Return
Libraries.Compute.Vector: The adjusted R^2 if the regression is calculated
GetCoefficientMatrix()
Returns the total beta coefficients. This action returns 0 unless the regression has been calculated.
Return
Libraries.Compute.Matrix: The beta coefficients
GetCoefficientProbabilityValueMatrix()
GetCoefficientProbabilityValues()
GetCoefficientStandardErrorMatrix()
GetCoefficientStandardErrors()
GetCoefficientTestStatisticMatrix()
GetCoefficientTestStatistics()
GetCoefficients()
Returns the total beta coefficients. This action returns 0 unless the regression has been calculated.
Return
Libraries.Compute.Vector: The beta coefficients
GetCriticalValue()
Outdated, this returns the test statistic not the critical value at alpha.
Return
number:
GetDenominatorDegreesOfFreedom()
This calculates the degrees of freedom of the denominator in the F-ratio. It is equivalent to the number of rows in the matrix - the number of columns
Return
number: The number of rows in the matrix - the number of columns.
GetEffectSize()
Returns the total effect size, in statistics typically termed R^2 (R-squared). This action returns 0 unless the regression has been calculated.
Return
number: The R^2 if the regression is calculated
GetEffectSizeVector()
Returns the total effect size, in statistics typically termed R^2 (R-squared). This action returns 0 unless the regression has been calculated.
Return
Libraries.Compute.Vector: The R^2 if the regression is calculated
GetEquation()
This action return the linear equation for the regression model.
Return
text:
GetEquation(integer index)
This action return the linear equation for the regression model.
Parameters
- integer index
Return
text:
GetFormalNumericalResult()
This action summarizes the result and places it into formal academic language, in APA format. For more information: https://apastyle.apa.org/instructional-aids/numbers-statistics-guide.pdf
Return
text:
GetFormalNumericalResult(integer index)
This action summarizes the result and places it into formal academic language, in APA format. For more information: https://apastyle.apa.org/instructional-aids/numbers-statistics-guide.pdf
Parameters
- integer index
Return
text:
GetFormalSummary()
This action summarizes the result and places it into formal academic language, in APA format.
Return
text:
GetHashCode()
This action gets the hash code for an object.
Return
integer: The integer hash code of the object.
Example
Object o
integer hash = o:GetHashCode()
GetNumeratorDegreesOfFreedom()
This calculates the degrees of freedom of the numerator in the F-ratio. It is equivalent to the number of columns in the matrix - 1
Return
number: The number of columns in the matrix - 1
GetProbabilityValue()
Returns the probability value
Return
number:
GetProbabilityValueVector()
GetRegressionDegreesOfFreedom()
This calculates the degrees of freedom of the numerator in the F-ratio. It is equivalent to the number of columns in the matrix - 1
Return
number: The number of columns in the matrix - 1
GetRegressionSumOfSquares()
Returns the regression/model sum of squares. This action returns 0 unless the regression has been calculated.
Return
number: The regression (model) sum of squares
GetRegressionSumOfSquaresVector()
Returns the regression/model sum of squares. This action returns 0 unless the regression has been calculated.
Return
Libraries.Compute.Vector: The regression (model) sum of squares
GetResidualDegreesOfFreedom()
This calculates the degrees of freedom of the denominator in the F-ratio. It is equivalent to the number of rows in the matrix - the number of columns
Return
number: The number of rows in the matrix - the number of columns.
GetResidualHistogram()
Returns the histogram chart of the residuals. Each column(series) will show each dependent variable's residuals
Return
GetResidualMatrix()
Returns the residuals. This action returns 0 unless the regression has been calculated.
Return
Libraries.Compute.Matrix: The residuals
GetResidualStandardError()
Returns the standard error for the residuals
Return
number:
GetResidualStandardErrorVector()
GetResidualSumOfSquares()
Returns the total residual sum of squares. This action returns 0 unless the regression has been calculated.
Return
number: The residual sum of squares
GetResidualSumOfSquaresVector()
Returns the total residual sum of squares. This action returns 0 unless the regression has been calculated.
Return
Libraries.Compute.Vector: The residual sum of squares
GetResiduals()
Returns the residuals. This action returns 0 unless the regression has been calculated.
Return
Libraries.Compute.Vector: The residuals
GetSignificanceLevel()
P-Value: index for each dependent variable
Return
number:
GetStatisticalFormatting()
GetSummary()
This action summarizes the result and lists it informally.
Return
text:
GetTestStatistic()
Returns the test statistic
Return
number:
GetTestStatisticVector()
GetTotalSumOfSquares()
Returns the total sum of squares. This action returns 0 unless the regression has been calculated.
Return
number: The total sum of squares
GetTotalSumOfSquaresVector()
Returns the total sum of squares. This action returns 0 unless the regression has been calculated.
Return
Libraries.Compute.Vector: The total sum of squares
HasIntercept()
Returns whether or not this regression includes an intercept.
Return
boolean:
Predict(Libraries.Compute.Statistics.DataFrame frame)
Outdated, this returns the test statistic not the critical value at alpha.
Parameters
Return
Libraries.Compute.Statistics.Predictions.RegressionPrediction:
SetHasIntercept(boolean hasIntercept)
Sets whether or not this regression includes an intercept.
Parameters
- boolean hasIntercept
SetSignificanceLevel(number significanceLevel)
Sets the significance level of the test (default is 0.05).
Parameters
- number significanceLevel: the significance level between 0 and 1.
SetStatisticalFormatting(Libraries.Compute.Statistics.Reporting.StatisticsFormatting formatting)
Regression sum of squares: index for each dependent variable
Parameters
On this page
Variables TableAction Documentation- Calculate(Libraries.Compute.Statistics.DataFrame frame)
- Compare(Libraries.Language.Object object)
- Equals(Libraries.Language.Object object)
- GetAdjustedEffectSize()
- GetAdjustedEffectSizeVector()
- GetCoefficientMatrix()
- GetCoefficientProbabilityValueMatrix()
- GetCoefficientProbabilityValues()
- GetCoefficientStandardErrorMatrix()
- GetCoefficientStandardErrors()
- GetCoefficientTestStatisticMatrix()
- GetCoefficientTestStatistics()
- GetCoefficients()
- GetCriticalValue()
- GetDenominatorDegreesOfFreedom()
- GetEffectSize()
- GetEffectSizeVector()
- GetEquation()
- GetEquation(integer index)
- GetFormalNumericalResult()
- GetFormalNumericalResult(integer index)
- GetFormalSummary()
- GetHashCode()
- GetNumeratorDegreesOfFreedom()
- GetProbabilityValue()
- GetProbabilityValueVector()
- GetRegressionDegreesOfFreedom()
- GetRegressionSumOfSquares()
- GetRegressionSumOfSquaresVector()
- GetResidualDegreesOfFreedom()
- GetResidualHistogram()
- GetResidualMatrix()
- GetResidualStandardError()
- GetResidualStandardErrorVector()
- GetResidualSumOfSquares()
- GetResidualSumOfSquaresVector()
- GetResiduals()
- GetSignificanceLevel()
- GetStatisticalFormatting()
- GetSummary()
- GetTestStatistic()
- GetTestStatisticVector()
- GetTotalSumOfSquares()
- GetTotalSumOfSquaresVector()
- HasIntercept()
- Predict(Libraries.Compute.Statistics.DataFrame frame)
- SetHasIntercept(boolean hasIntercept)
- SetSignificanceLevel(number significanceLevel)
- SetStatisticalFormatting(Libraries.Compute.Statistics.Reporting.StatisticsFormatting formatting)