GrowthModels (FSharp.Stats) | FSharp.Stats

FSharp.Stats

GrowthModels Module

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

Functions and values

Function or value	Description
`exponential` Full Usage: `exponential` Returns: `Model`	exponential growth model Returns: `Model`
`getSolverOptionsGompertz xData yDataLog expectedGenerationTime usedLogTransform` Full Usage: `getSolverOptionsGompertz xData yDataLog expectedGenerationTime usedLogTransform` Parameters: xData : `float[]` - yDataLog : `float[]` - expectedGenerationTime : `float` - usedLogTransform : `float -> float` - Returns: `SolverOptions`	determines the solver options for cell count growth data (must be in log space). For untransformed data use 'id' as transform. xData : `float[]` yDataLog : `float[]` expectedGenerationTime : `float` usedLogTransform : `float -> float` Returns: `SolverOptions` Example
`gompertz` Full Usage: `gompertz` Returns: `Model`	The gompertz function describes the log cell count at time point t. Returns: `Model`
`janoschek` Full Usage: `janoschek` Returns: `Model`	stable growth model similar to weibull model Returns: `Model`
`morganMercerFlodin` Full Usage: `morganMercerFlodin` Returns: `Model`	4 parameter Morgan-Mercer-Flodin growth model Returns: `Model`
`richards` Full Usage: `richards` Returns: `Model`	4 parameter richards curve with minimum at 0; d <> 1 Returns: `Model`
`richardsGeneric` Full Usage: `richardsGeneric` Returns: `Model`	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`
`verhulst` Full Usage: `verhulst` Returns: `Model`	3 parameter verhulst logistic model with lower asymptote=0 Returns: `Model`
`verhulst4Param` Full Usage: `verhulst4Param` Returns: `Model`	4 parameter verhulst model with variably lowers asymptote Returns: `Model`
`vonBertalanffy` Full Usage: `vonBertalanffy` Returns: `Model`	3 parameter von Bertalanffy growth model Returns: `Model`
`weibull` Full Usage: `weibull` Returns: `Model`	weibull growth model; if d=1 then it is a simple exponential growth model Returns: `Model`