Class RegressionResults

Namespace

TimeSeriesAnalysis

Assembly

TimeSeriesAnalysis.dll

Class that holds the results of a run of Vec.Regress.

public class RegressionResults

Inheritance

object

RegressionResults

Inherited Members

object.Equals(object)

object.Equals(object, object)

object.GetHashCode()

object.GetType()

object.MemberwiseClone()

object.ReferenceEquals(object, object)

object.ToString()

Constructors

RegressionResults()

Default constructor, sets all values to null or zero.

public RegressionResults()

Properties

AbleToIdentify

True if able to identify, otherwise false

public bool AbleToIdentify { get; set; }

Property Value

bool

Bias

The bias term of the linear regression

public double Bias { get; set; }

Property Value

double

Gains

The gains of the linear regression

public double[] Gains { get; set; }

Property Value

double[]

NfittingBadDataPoints

Number of bad data point ignored in the fitting data set

public int NfittingBadDataPoints { get; set; }

Property Value

int

NfittingTotalDataPoints

Total number of data points in the fitting data set

public int NfittingTotalDataPoints { get; set; }

Property Value

int

ObjectiveFunctionValue

The value of the objective function after regression

public double ObjectiveFunctionValue { get; set; }

Property Value

double

Param

All regression parameters, first the gains, then the bias term.

public double[] Param { get; set; }

Property Value

double[]

Param95prcConfidence

The 95 percent confidence of parameters The confidence interval will be Param +/- Param95prcConfidence

public double[] Param95prcConfidence { get; set; }

Property Value

double[]

RegressionWarnings

Regression warnings

public List<RegressionWarnings> RegressionWarnings { get; set; }

Property Value

List<RegressionWarnings>

Rsq

R2-root-means-squared between Y and Y_modelled for the tuning dataset(a value between 0 and 100, higher is better)

public double Rsq { get; set; }

Property Value

double

VarCovarMatrix

The variance/covariance matrix of the regression run

public double[][] VarCovarMatrix { get; set; }

Property Value

double[][]

Y_modelled

The modelled output

public double[] Y_modelled { get; set; }

Property Value

double[]