Tomasz Żółtak
Tomasz Żółtak
Another approach to joins would be to check whether join adds or duplicates any rows and simply not to allow join in such cases.
Well, it may make sense to create clusters on duplicated rows, but whether it actually makes heavily depends on ones workflow - there's no way package can check this. Personally...
I can do this but within few weeks time.
And #132 is also solved.
The problem is due to marginal effect(s) for a factor variable are determined partially by a way that contrasts are constructed. For example: if you estimated a model as in...
First problem that must be solved is that `build_datalist()` with specified `at` regarding factor variable returns this variable as a factor with only one level - this on which `at`...
And perhaps `dxdy.factor` should get levels not from data but from (data that has been used in) a model? I mean, if user don't specify neither `data` nor `at` argument...
Replacing code at the beginning of `dydx.factor()` with this below should work. ```r levs
I tried a little myself and apart of this there is also a [problem within _prediction_ package](https://github.com/leeper/prediction/issues/37) and error message obtained with calling a code I provided applies to that...
Little correction, so it all works also with svrepdesign objects - weights should be obtained as: ``` wts