Convert/decompose formula terms similarly to LinearModel

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Sven . 2013년 5월 2일
I'm trying to do something which must be implicitly done inside LinearModel.predict(), but I can't seem to get to it.
The task I'm trying to do is to have a linear formula given as a set of terms:
formulaTerms = {'X1','X2','X1*X2'} % ie, y = X1 + X2 + X1*X2
and then have the input terms defined in their simplest state such as:
inputTerms = {'X1','X2'}
and then produce a function that will take in each term in inputTerms and return to me each term in formulaTerms. For example in this instance the result would be:
outFcn = @(input)[input(1), input(2), input(1)*input(2)]
outFcn([2 3]) % Which gets the 3 terms in the formula: [2 3 6]
What I'm trying to do is generalise this to a function such as:
function linearFcn = convert(formulaTerms, inputTerms)
% linearFcn = ... (well, this is what I'm trying to write!)
The closest I can get to a general solution is something like the following where I can convert my formulaTerms into a formula string similar to what accepts:
f = classreg.regr.LinearFormula('y ~ X1 + X2 + X1*X2',{'y','X1','X2'},'',[],'identity')
This is basically what is created inside the LinearModel object, but I don't know how LinearModel.predict(input) combines this input variable with the LinearFormula object to produce the actual terms that are summed up to complete the linear model prediction.

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Shashank Prasanna
Shashank Prasanna 2013년 5월 3일
편집: Shashank Prasanna 님. 2013년 5월 3일
Sven, what you are interested in is the designmatrix: x2fx
Alternatively, if you are still interested in the what predict does:
mdl =,y)
formulaTerms=classreg.regr.modelutils.designmatrix([X ones(length(X),1)],...
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Sven 2013년 5월 5일
Aha... nice one Shashank, I'd found a designmatrix being built inside the LinearFunction object, but x2fx is indeed more suitable (and accessible). Thanks.

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