GoodnessOfFit (FSharp.Stats)

GoodnessOfFit Module

Namespace: FSharp.Stats.Fitting
Assembly: FSharp.Stats.dll

Types and nested modules

Type/Module	Description
OLS
SumOfSquares	Three sum of squares

Functions and values

Function or value	Description
`calcAIC k n sse` Full Usage: `calcAIC k n sse` Parameters: k : `float` n : `float` sse : `float` Returns: `float`	Calculates Akaike information criterion (AIC) which is a measure of the relative quality of a regression model for a given set of data k : `float` n : `float` sse : `float` Returns: `float`
`calcBIC k n sse` Full Usage: `calcBIC k n sse` Parameters: k : `float` n : `float` sse : `float` Returns: `float`	Calculates Bayesian information criterion (BIC) which is a measure of the relative quality of a regression model for a given set of data k : `float` n : `float` sse : `float` Returns: `float`
`calculateANOVA order fitFunc xData yData` Full Usage: `calculateANOVA order fitFunc xData yData` Parameters: order : `int` - fitFunc : `float -> float` - xData : `Vector<float>` - yData : `Vector<float>` - Returns: `AnovaVariationSource[]`	explained = total - unexplained order : `int` fitFunc : `float -> float` xData : `Vector<float>` yData : `Vector<float>` Returns: `AnovaVariationSource[]` Example
`calculateDetermination sumOfSquares` Full Usage: `calculateDetermination sumOfSquares` Parameters: sumOfSquares : `SumOfSquares` Returns: `float`	sumOfSquares : `SumOfSquares` Returns: `float`
`calculateDeterminationAdj actual expected variables` Full Usage: `calculateDeterminationAdj actual expected variables` Parameters: actual : `seq<float>` - expected : `seq<float>` - variables : `int` - Returns: `float`	Gets the adjusted coefficient of determination, as known as the R-Squared (R²adj). It is adjusted by the number of used variables (not including the constant term) (https://ebrary.net/1008/economics/adjusted_coefficient_determination_adjusted) actual : `seq<float>` expected : `seq<float>` variables : `int` Returns: `float` Example
`calculateDeterminationFromValue actual expected` Full Usage: `calculateDeterminationFromValue actual expected` Parameters: actual : `seq<float>` expected : `seq<float>` Returns: `float`	Gets the coefficient of determination, as known as the R-Squared (R²) actual : `seq<float>` expected : `seq<float>` Returns: `float`
`calculateSSE fitFunc xData yData` Full Usage: `calculateSSE fitFunc xData yData` Parameters: fitFunc : `float -> float` - xData : `Vector<float>` - yData : `Vector<float>` - Returns: `float`	Calculates SSE: sum of squares of errors also: unexplained sum of squares; residual sum of squares fitFunc : `float -> float` xData : `Vector<float>` yData : `Vector<float>` Returns: `float` Example
`calculateSSR fitFunc xData yData` Full Usage: `calculateSSR fitFunc xData yData` Parameters: fitFunc : `float -> float` - xData : `Vector<float>` - yData : `Vector<float>` - Returns: `float`	Calculates SSR: sum of squares regression. also: explained sum of squares fitFunc : `float -> float` xData : `Vector<float>` yData : `Vector<float>` Returns: `float` Example
`calculateSST yData` Full Usage: `calculateSST yData` Parameters: yData : `Vector<float>` - Returns: `float`	Calculates SST: sum of squares total. also: total sum of squares yData : `Vector<float>` Returns: `float` Example
`calculateSumOfSquares fitFunc xData yData` Full Usage: `calculateSumOfSquares fitFunc xData yData` Parameters: fitFunc : `float -> float` xData : `seq<float>` yData : `seq<float>` Returns: `SumOfSquares`	fitFunc : `float -> float` xData : `seq<float>` yData : `seq<float>` Returns: `SumOfSquares`
`createSumOfSquares ssr sse sst ssxx ssxy meanX meanY count` Full Usage: `createSumOfSquares ssr sse sst ssxx ssxy meanX meanY count` Parameters: ssr : `float` sse : `float` sst : `float` ssxx : `float` ssxy : `float` meanX : `float` meanY : `float` count : `float` Returns: `SumOfSquares`	ssr : `float` sse : `float` sst : `float` ssxx : `float` ssxy : `float` meanX : `float` meanY : `float` count : `float` Returns: `SumOfSquares`
`getResiduals fitFunc xData yData` Full Usage: `getResiduals fitFunc xData yData` Parameters: fitFunc : `float -> float` - xData : `Vector<float>` - yData : `Vector<float>` - Returns: `seq<float>`	Calculates the residuals fitFunc : `float -> float` xData : `Vector<float>` yData : `Vector<float>` Returns: `seq<float>` Example
`standardErrorEstimate sumOfSquares` Full Usage: `standardErrorEstimate sumOfSquares` Parameters: sumOfSquares : `SumOfSquares` - Returns: `float`	Standard error if the estimate Square root of variance s2y,x sumOfSquares : `SumOfSquares` Returns: `float` Example
`standardErrorIntercept sumOfSquares` Full Usage: `standardErrorIntercept sumOfSquares` Parameters: sumOfSquares : `SumOfSquares` - Returns: `float`	Standard error of intercept (alpha) sumOfSquares : `SumOfSquares` Returns: `float` Example
`standardErrorSlope sumOfSquares` Full Usage: `standardErrorSlope sumOfSquares` Parameters: sumOfSquares : `SumOfSquares` - Returns: `float`	Standard error of slope (beta) sumOfSquares : `SumOfSquares` Returns: `float` Example
`ttestIntercept intercept sumOfSquares` Full Usage: `ttestIntercept intercept sumOfSquares` Parameters: intercept : `float` sumOfSquares : `SumOfSquares` Returns: `TTestStatistics`	intercept : `float` sumOfSquares : `SumOfSquares` Returns: `TTestStatistics`
`ttestSlope slope sumOfSquares` Full Usage: `ttestSlope slope sumOfSquares` Parameters: slope : `float` sumOfSquares : `SumOfSquares` Returns: `TTestStatistics`	slope : `float` sumOfSquares : `SumOfSquares` Returns: `TTestStatistics`