On Tue, Oct 30, 2018 at 4:22 AM Jonathan MERCIER <jmerc...@cng.fr> wrote:
> *R examples:* > > Y ~ A | Y = βo + β1A | Straight-line with an implicit > y-intercept > Y ~ -1 + A | Y = β1A | Straight-line with no y-intercept; > that is, a fit forced through (0,0) > SimpleRegression should take care of these first cases. http://commons.apache.org/proper/commons-math/apidocs/org/apache/commons/math4/stat/regression/SimpleRegression.html Construct the SimpleRegression() object, then pass data using the addObservations() method. You can either call regress() and inspect the RegressionResults object returned, or after calling results, use the SimpleRegression methods getSlope() and getIntercept() . The results returned by RegressionResults are more exhaustive. The boolean includeIntercept should toggle whether the model fits an intercept. Y ~ A + I(A^2) | Y = βo+ β1A + β2A2| Polynomial model; note that the > identity function I( ) allows terms in the model to include normal > mathematical symbols. > I don't think we have such a compact syntax for this. I think you'll have to do it the old-fashioned way and create the appropriate model matrix, then solve using the OLS regression package http://commons.apache.org/proper/commons-math/apidocs/org/apache/commons/math4/stat/regression/OLSMultipleLinearRegression.html which uses a QR factorization. Add data with the newSampleData() method, then get your regression coefficients by calling calculateBeta() . > > Thanks for your help > -- > [image: Jonathan MERCIER] > [image: Centre National de Recherche en Génomique Humaine (CNRGH)] > > Researcher computational biology > > PhD, Jonathan MERCIER > > Bioinformatics (LBI) > > 2, rue Gaston Crémieux > > 91057 Evry Cedex > > Tel :(33) 1 60 87 83 44 > > Email :jonathan.merc...@cng.fr >
