Linear |
Performs a linear fit to a given function base using singular value decomposition.
Inheritance HierarchyNamespace: Altaxo.Calc.Regression
Assembly: AltaxoCore (in AltaxoCore.dll) Version: 4.8.3261.0 (4.8.3261.0)
SyntaxC#
public class LinearFitBySvd
The LinearFitBySvd type exposes the following members.
Constructors| Name | Description | |
|---|---|---|
 | LinearFitBySvd(IROMatrixDouble, Double, Double, Int32, Int32, Double) | Fits a data set linear to a given x base. |
| LinearFitBySvd(Double, Double, Double, Int32, Int32, FunctionBaseEvaluator, Double) | Fits a data set linear to a given function base. |
| LinearFitBySvd(IReadOnlyListDouble, IReadOnlyListDouble, IReadOnlyListDouble, Int32, Int32, FunctionBaseEvaluator, Double) | Fits a data set linear to a given function base. |
Properties| Name | Description | |
|---|---|---|
 | AdjustedRSquared | Gives the adjusted coefficient of determination. |
| ConditionNumber | Gets the condition number. The decadic logarithm of the condition number is roughly the loss of precision (in digits) during the calculation. |
| Covariances | Get the variance-covariance-matrix for the fit. |
| EstimatedVariance | Get the estimated residual mean square, also called SigmaSquare.. |
| NumberOfData | Returns the number of data value. |
| NumberOfParameter | Returns the number of parameter (=Order+1) of the fit. |
| Parameter | Get the resulting parameters, so that the model y = SUM(parameter[i]*functionbase[i]) |
| PredictedValues | Gets the predicted dependent values |
| RegressionCorrectedSumOfSquares | Gets the regression sum of squares, i.e. SUM(yi`-ymean), where yi` is the predicted ith y value and y mean is the mean value of all y values. |
| ResidualSumOfSquares | Gets the sum of ChiSquare for the fit. This is SUM(yi-yi`)^2, where yi is the ith y value and yi` is the ith predicted y. |
| ResidualValues | Gets the array of residual values defined as the difference y[i]-ypredicted[i]. |
| RSquared | Gives the coefficient of determination, also called R^2, squared correlation coefficient. It is a measure, how much of the variability of the y data is accounted for by the regression model. |
| Sigma | Get the standard error of regression, defined as Sqrt(SigmaSquare). |
| TotalCorrectedSumOfSquares | Gives the corrected sum of squares of y, i.e. SUM(yi-ymean), where yi is the ith y value and ymean is the mean of all y values. |
Methods| Name | Description | |
|---|---|---|
| Calculate | Fits a data set linear to a given x base. |
 | CorrectedSumOfSquares | Calculates the corrected sum of squares of length elements of array x starting from index start. The corrected sum of squares is defined as sum of squares of the elements minus their mean value. |
| Equals | Determines whether the specified object is equal to the current object. (Inherited from Object) |
| ExternallyStudentizedResidual | Gives the ith studentized residual, with the ith observation removed from the model. |
 | Finalize | Allows an object to try to free resources and perform other cleanup operations before it is reclaimed by garbage collection. (Inherited from Object) |
| FitPolymomial | Fits data provided as xcolumn and ycolumn with a polynomial base. Here special measures are taken (scaling of the x-variable) in order to keep the precision high. |
| FitPolymomialDestructive | Fits data provided as xcolumn and ycolumn with a polynomial base. Here special measures are taken (scaling of the x-variable) in order to keep the precision high. |
| GetHashCode | Serves as the default hash function. (Inherited from Object) |
| GetPolynomialFunctionBase | Gets a default polynomial function base with intercept, i.e. f(y)=a+b*x+c*x*x ... |
| GetType | Gets the Type of the current instance. (Inherited from Object) |
| Mean | Calculates the mean value of length elements in array x starting from index start. |
| MemberwiseClone | Creates a shallow copy of the current Object. (Inherited from Object) |
| PredictionVariance | Gives the variance of the prediction of the ith y-value. |
| PRESSResidual | Gives the ith PRESS residual. |
| StandardErrorOfParameter | Gets the estimated standard error of parameter i. |
| StudentizedResidual | Gives the ith studentized residual. |
| TofParameter | |
| ToString | Returns a string that represents the current object. (Inherited from Object) |
See Also