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POWhich classification packages in R have a non-formula interface (e.g., f(x,y) instead of f(y ~ ., data))?
 

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  1. POWhich classification packages in R have a non-formula interface (e.g., f(x,y) instead of f(y ~ ., data))?
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    1. COI don't see how a parametrized list would be any more convenient than a formula. If you're constructing the argument list by name, it is no more difficult to simply construct the formula expression itself. If you're utilizing a list of elements to pass as an argument (e.g., through do.call), then this is no more difficult than using formula functions like model.matrix and model.frame, among others, to parse a formula given a data set. In either case, you still need to automate your selection of parameters, either by name or by reference to entities (or some manipulation thereof). Make sense?
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      1. POWhich classification packages in R have a non-formula interface (e.g., f(x,y) instead of f(y ~ ., data))?
      1. USBryan Goodrich
    2. CO@Bryan My concern is not convenience but computational cost. Evaluating `y ~ .` hundreds of times on a high-dimensional data set takes forever because model.frame is slow. See for example this snippet from ?gbm -- 'gbm is a front-end to gbm.fit that uses the familiar R modeling formulas. However, model.frame is very slow if there are many predictor variables. For power-users with many variables use gbm.fit. For general practice gbm is preferable.'
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      1. POWhich classification packages in R have a non-formula interface (e.g., f(x,y) instead of f(y ~ ., data))?
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    3. COMany methods allow to use non-formula interface. Look at `e1071` package, there are several classification techniques collected in one place - svm, knn, ann, random forest, etc. But you can of course use the separate package in which certain method is implemented.
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      1. USDrDom
 

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