Table (FSharp.Stats)

Table Module

Namespace: FSharp.Stats.Fitting
Assembly: FSharp.Stats.dll
Parent Module: NonLinearRegression

Nested modules

Modules	Description
Finances
GrowthModels

Functions and values

Function or value	Description
`LogisticFunctionAscending` Full Usage: `LogisticFunctionAscending` Returns: `Model`	Logistic function of the form "y = L/(1+e^(-k(t-x)))" Returns: `Model`
`LogisticFunctionDescending` Full Usage: `LogisticFunctionDescending` Returns: `Model`	Logistic function of the form "y = L/(1+e^(k(t-x)))" Returns: `Model`
`LogisticFunctionVarYAscending` Full Usage: `LogisticFunctionVarYAscending` Returns: `Model`	Logistic function of the form "y = L/(1+e^(-k(t-x)))+N" Modified version of the Logistic function model with a variable curve minimum. Returns: `Model`
`LogisticFunctionVarYDescending` Full Usage: `LogisticFunctionVarYDescending` Returns: `Model`	Logistic function of the form "y = L/(1+e^(k(t-x)))+N" Modified version of the Logistic function model with a variable curve minimum. Returns: `Model`
`emgModel` Full Usage: `emgModel` Returns: `Model`	Returns: `Model`
`emgSolverOptions initialParamGuess` Full Usage: `emgSolverOptions initialParamGuess` Parameters: initialParamGuess : `float[]` Returns: `SolverOptions`	initialParamGuess : `float[]` Returns: `SolverOptions`
`expModel` Full Usage: `expModel` Returns: `Model`	Exponential function of the form "y = a * exp(b * x)" Returns: `Model`
`expSolverOptions xData yData` Full Usage: `expSolverOptions xData yData` Parameters: xData : `float[]` - yData : `float[]` - Returns: `SolverOptions`	Takes the result of the linearization as initialGuessParams xData : `float[]` yData : `float[]` Returns: `SolverOptions` Example
`gaussModel` Full Usage: `gaussModel` Returns: `Model`	Gaussian function of the form "y = amp * exp( -1. * ( ( ( (x-meanX)*2. ) / (2.std**2.)) ) )" Returns: `Model`
`gaussSolverOptions initialParamGuess` Full Usage: `gaussSolverOptions initialParamGuess` Parameters: initialParamGuess : `float[]` Returns: `SolverOptions`	initialParamGuess : `float[]` Returns: `SolverOptions`
`hillModel` Full Usage: `hillModel` Returns: `Model`	Hill equation "y = Vm * x^n / (k^n+x^n)" Returns: `Model`
`hillSolverOptions Vm n k` Full Usage: `hillSolverOptions Vm n k` Parameters: Vm : `float` n : `float` k : `float` Returns: `SolverOptions`	Vm : `float` n : `float` k : `float` Returns: `SolverOptions`
`lineModel` Full Usage: `lineModel` Returns: `Model`	Line Line model of the form "y = a * x + b" Returns: `Model`
`lineSolverOptions initialParamGuess` Full Usage: `lineSolverOptions initialParamGuess` Parameters: initialParamGuess : `float[]` Returns: `SolverOptions`	initialParamGuess : `float[]` Returns: `SolverOptions`
`logNormalModel` Full Usage: `logNormalModel` Returns: `Model`	log normal distribution Returns: `Model`
`logNormalOptions sample` Full Usage: `logNormalOptions sample` Parameters: sample : `float[]` - Returns: `SolverOptions`	Takes the result of the linearization as initialGuessParams sample : `float[]` Returns: `SolverOptions` Example
`parabolaModel` Full Usage: `parabolaModel` Returns: `Model`	Parabola paraboola model of the form "y = a * x^2 + b * x + c" Returns: `Model`
`parabolaSolverOptions initialParamGuess` Full Usage: `parabolaSolverOptions initialParamGuess` Parameters: initialParamGuess : `float[]` Returns: `SolverOptions`	initialParamGuess : `float[]` Returns: `SolverOptions`
`richardsGenericDescending` Full Usage: `richardsGenericDescending` Returns: `Model`	Descending version of the generalized logistic function or curve, also known as Richards' curve with 7 parameters. Logistic function of the form "Y(t) = A + (K - A) / (C + Q * e^(B * (t - M)))**(1. / v)" Returns: `Model`

Table Module

Nested modules

Functions and values

Example

Example