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Variable importance #29
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- changed selprob order to decreasing=FALSE
still -> no selprob -> no "other" -> no maxchar
…s ordered factors instead of sorting the data.frame. Now, variables are correctly sorted as well. bl per variable are accumulated again
… the baselearners.
variables in interactions sorted for identifiability selprob -> selfreq
Thanks a lot. We currently have an issue with Travis-CI but I will try to fix this and have a look at your PR afterwards. |
Just as a short information - the Travis build still includes two notes caused by the variable importance code:
We already tried Hadley Wickhams advice to solve the problem via the following line:
Question is, can we keep it that way despite the notes? |
No, the issue has to be resolved.
Regarding the issue with |
Ok, thanks for the advice. We implemented remarks 1) and 2) and it helped eliminating the note for |
I now seem to have found the error.
I'd guess this should be |
..yes, that's it - thanks a lot!! The note for Actually the barchart was correctly created with the call in the post above (with Is there anything else that got your attention or can we leave the code as it is? |
Hi @tkuehn13, I was a bit too lazy (and had too little time) to pull in your patch bevor changing the structure of mboost (I got rid of the stupid sub folders mboostPatch and mboostDevel. Now, I cannot use your patch easily for mboostDevel anymore. Do you have any ideas how to solve this issue? I do not want to loose your change track etc by simply copy-pasting the files in the new structure... Sorry for this. |
Almond and me (in coordination with Fabian Scheipl) wrote a function to extract and plot the (in-bag) risk reduction per base-learner from a fitted mboost-model.
The contribution (to risk reduction) of each base-learner can be used as a measure for variable importance of the different base-learners or variables in the model.
Function
varimp(object)
simply returns a object of classvarimp
with the corresponding risk reductions per base-learner.The plot function (eventually a
lattice::barchart
) for thesevarimp
objects additionally offers some visualisation options like the display in percentages or absolute values, number and order of the displayed bars and whether to focus on base-learners or variables involved in the base-leaners.