Linear |
public class LinearFitBySvd
The LinearFitBySvd type exposes the following members.
Name | Description | |
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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. |
Name | Description | |
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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. |
Name | Description | |
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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) |