Univariable (FSharp.Stats)

Univariable Module

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

Univariable handles two dimensional x,y data.

Functions and values

Function or value	Description
`cooksDistance xData yData` Full Usage: `cooksDistance xData yData` Parameters: xData : `Vector<float>` - vector of x values yData : `Vector<float>` - vector of y values Returns: `vector` Collection of cooks distances for every input coordinate.	Fits a model f(x) = b + m * x) to the data and returns the cooks distance for every data pair present in the input collections as an estimator for the influence of each data point in coefficient estimation. xData : `Vector<float>` vector of x values yData : `Vector<float>` vector of y values Returns: `vector` Collection of cooks distances for every input coordinate. Example `// e.g. days since a certain event let xData = vector [\|1.;2.;3.;4.;5.;6.\|] // e.g. some measured feature let yData = vector [\|4.;7.;9.;10.;11.;15.\|] let distances = Univariable.cooksDistance xData yData`
`fit xData yData` Full Usage: `fit xData yData` Parameters: xData : `Vector<float>` - vector of x values yData : `Vector<float>` - vector of y values Returns: `Coefficients` vector of [intercept; slope]	Calculates the intercept and slope for a straight line fitting the data. Linear regression minimizes the sum of squared residuals. xData : `Vector<float>` vector of x values yData : `Vector<float>` vector of y values Returns: `Coefficients` vector of [intercept; slope] Example `// e.g. days since a certain event let xData = vector [\|1.;2.;3.;4.;5.;6.\|] // e.g. some measured feature let yData = vector [\|4.;7.;9.;10.;11.;15.\|] // Estimate the coefficients of a straight line fitting the given data let coefficients = Univariable.fit xData yData`
`fitCholesky xData yData` Full Usage: `fitCholesky xData yData` Parameters: xData : `Vector<float>` - vector of x values yData : `Vector<float>` - vector of y values Returns: `Coefficients` vector of [intercept; slope]	Calculates the intercept and slope for a straight line fitting the data using Cholesky Decomposition. Linear regression minimizes the sum of squared residuals. xData : `Vector<float>` vector of x values yData : `Vector<float>` vector of y values Returns: `Coefficients` vector of [intercept; slope] Example `// e.g. days since a certain event let xData = vector [\|1.;2.;3.;4.;5.;6.\|] // e.g. some measured feature let yData = vector [\|4.;7.;9.;10.;11.;15.\|] // Estimate the coefficients of a straight line fitting the given data let coefficients = Univariable.fitCholesky xData yData`
`fitConstrained xData yData (arg3, arg4)` Full Usage: `fitConstrained xData yData (arg3, arg4)` Parameters: xData : `Vector<float>` - vector of x values yData : `Vector<float>` - vector of y values arg2 : `float` arg3 : `float` Returns: `Coefficients` vector of [intercept; slope]	Calculates the intercept and slope for a straight line fitting the data and passing through a specified point (xC,yC) . Linear regression minimizes the sum of squared residuals. xData : `Vector<float>` vector of x values yData : `Vector<float>` vector of y values arg2 : `float` arg3 : `float` Returns: `Coefficients` vector of [intercept; slope] Example `// e.g. days since a certain event let xData = vector [\|1.;2.;3.;4.;5.;6.\|] // e.g. some measured feature let yData = vector [\|4.;7.;9.;10.;11.;15.\|] // Estimate the coefficients of a straight line fitting the given data let coefficients = Univariable.fitConstrained xData yData (6.,15.)`
`predict coef x` Full Usage: `predict coef x` Parameters: coef : `Coefficients` - vector of [intercept;slope] (e.g. determined by Univariable.coefficient) x : `float` - x value of which the corresponding y value should be predicted Returns: `float` predicted y value with given coefficients at X=x	Takes intercept and slope of simple linear regression to predict the corresponding y value. coef : `Coefficients` vector of [intercept;slope] (e.g. determined by Univariable.coefficient) x : `float` x value of which the corresponding y value should be predicted Returns: `float` predicted y value with given coefficients at X=x Example `// e.g. days since a certain event let xData = vector [\|1.;2.;3.;4.;5.;6.\|] // e.g. some measured feature let yData = vector [\|4.;7.;9.;10.;11.;15.\|] // Estimate the coefficients of a straight line fitting the given data let coefficients = Univariable.fit xData yData // Predict the feature at midnight between day 1 and 2. Univariable.predict coefficients 1.5`