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EDIT: Turns out, I missread. This allows a different lambda parameter for each column of Y, not of X.
Thus it is not as useful as I was hoping. It is very fast, but unfortunately cannot work with sparse matrices.
Secondedit: I still think having separate lambda parameters for each columns (or at least blocks of columns) would be great. Maybe I am missing something. One wayto manually do it, would be to add diagonal matrices to the designmatrix and predict "0", this is a GLM way to shrink.
other ideas
They report things that should be relatively easy in unfold (once we have crossvalidation):
crossvalidated $R^2$
unique-variance (i.e. a kind of $R^2_{predictor}$, SS_{Full} - SS_{withoutx_i} / SS_{Full}
But importantly all of these things work on single subject level, not on group level. I.e. we still have to give some tips to do an ANOVA on grouplevel. But I actually right now think that is easy.
For group-level t-test, we test whether a single beta is different to 0. For a group of betas, we can use an ANOVA (and permuted ANOVAS). It is my understanding that thisis the same as in SPM. I think this should be straight forward to include in the development version of the statistics function using TFCE.
The text was updated successfully, but these errors were encountered:
uf_vif
This might give additional diagnostics for collinear predictors (haven't thought through the maths of this one)
uf_glmfit
Take their regularisation strategy, which allows each column to have their own lambda-regularisation parameter (MIT License, so we can include it & modify it)
https://github.com/churchlandlab/ridgeModel/blob/master/ridgeMML.m
EDIT: Turns out, I missread. This allows a different lambda parameter for each column of Y, not of X.
Thus it is not as useful as I was hoping. It is very fast, but unfortunately cannot work with sparse matrices.
Secondedit: I still think having separate lambda parameters for each columns (or at least blocks of columns) would be great. Maybe I am missing something. One wayto manually do it, would be to add diagonal matrices to the designmatrix and predict "0", this is a GLM way to shrink.
other ideas
They report things that should be relatively easy in unfold (once we have crossvalidation):
But importantly all of these things work on single subject level, not on group level. I.e. we still have to give some tips to do an ANOVA on grouplevel. But I actually right now think that is easy.
For group-level t-test, we test whether a single beta is different to 0. For a group of betas, we can use an ANOVA (and permuted ANOVAS). It is my understanding that thisis the same as in SPM. I think this should be straight forward to include in the development version of the statistics function using TFCE.
The text was updated successfully, but these errors were encountered: