Overload List
| # | Signature | Description |
|---|---|---|
| 1 | void LogNormalFit(TVec X, ref Double mu, ref Double sigma) | Calculate parameters for log-normally distributed values. |
| 2 | void LogNormalFit(TVec X, ref Double mu, ref Double sigma, ref Double[] PCIMu, ref Double[] PCISigma, Double Alpha) | Calculate parameters for log-normally distributed values. |
Overload 1: void LogNormalFit(TVec X, ref Double mu, ref Double sigma)
Calculate parameters for log-normally distributed values.
| # | Name | Type | Description |
|---|---|---|---|
| 1 | X | TVec | source TVec |
| 2 | mu | Double (ref) | output |
| 3 | sigma | Double (ref) | output |
Result: stored in self (calling object)
Overload 2: void LogNormalFit(TVec X, ref Double mu, ref Double sigma, ref Double[] PCIMu, ref Double[] PCISigma, Double Alpha)
Calculate parameters for log-normally distributed values.
| # | Name | Description |
|---|---|---|
| 1 | X | Stores data which is assumed to be log-normaly distributed. |
| 2 | mu | Return log-normal distribution parameter estimator M u. |
| 3 | sigma | Return log-normal distribution parameter estimator Sigma. |
| 4 | PCIMu | Mu (1-Alpha)*100 percent confidence interval. |
| 5 | PCISigma | Sigma (1-Alpha)*100 percent confidence interval. |
| 6 | Alpha | Confidence interval percentage. |
Result: stored in self (calling object)
Examples
using Dew.Math;
using Dew.Stats;
using Dew.Stats.Units;
namespace Dew.Examples;
{
private void Example()
{
Vector Data = new Vector(0);
Data.Size(100, false);
StatRandom.RandomLogNormal(3, 0.2, Data, -1);
double mu, sigma;
double[] MuCI = new double[2];
double[] SigmaCI = new double[2];
Statistics.LogNormalFit(Data, out mu, out sigma, out MuCI, out SigmaCI, 0.05);
// mu approx 3.0
// sigma approx 0.2
}
}See Also: StatRandom.RandomLogNormal