Logistic
Definition
Namespace: Extreme.Statistics.Distributions
Assembly: Extreme.Numerics (in Extreme.Numerics.dll) Version: 8.1.23
Assembly: Extreme.Numerics (in Extreme.Numerics.dll) Version: 8.1.23
Overload List
| Logistic | Estimates the parameters of the distribution of a variable assuming it follows a logistic distribution. |
| Logistic | Constructs a new LogisticDistribution with the specified location and scale parameters. |
| Logistic | Estimates the parameters of the distribution of a variable assuming it follows a logistic distribution. |
LogisticDistribution(Vector<Double>)
Estimates the parameters of the distribution of a variable
assuming it follows a logistic distribution.
public LogisticDistribution(
Vector<double> variable
)Parameters
- variable Vector<Double>
- A vector.
Return Value
The LogisticDistribution that best matches the distribution of variable.Remarks
Use this constructor to create a NormalDistribution whose parameters are estimated from variable. This constructor uses an unbiased estimator.
Exceptions
| ArgumentNullException | variable is null. |
LogisticDistribution(Double, Double)
Constructs a new LogisticDistribution with
the specified location and scale parameters.
public LogisticDistribution(
double location,
double scale
)Parameters
- location Double
- The location parameter of the distribution.
- scale Double
- The scale parameter of the distribution. Must be greater than zero.
Exceptions
| ArgumentOutOfRangeException | scale is less than or equal to zero. |
LogisticDistribution(Vector<Double>, EstimationMethod)
Estimates the parameters of the distribution of a variable
assuming it follows a logistic distribution.
public LogisticDistribution(
Vector<double> variable,
EstimationMethod method
)Parameters
- variable Vector<Double>
- A vector.
- method EstimationMethod
- An EstimationMethod value that specifies the method to be used to estimate the distribution parameters.
Return Value
The LogisticDistribution that best matches the distribution of variable.Remarks
Use this constructor to create a NormalDistribution whose parameters are estimated from variable. The maximum likelihood and matching moment methods use biased and unbiased estimators for the standard deviation.
Exceptions
| ArgumentNullException | variable is null. |