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Implement model-fitting statistics using compute-panel functions #30
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I am not sure to understand your question. Within ggplot2 one can only define a model using as variables the name of aesthetics, as statistics do not have accesses to the user's data. There more than one statistic that supports lm, so please explain in more detail what you are trying to do. |
Within ggplot2 all grouping variables are always added with full interaction to the statistical model: but in an analysis it is sometimes wanted to add the grouping variable without interaction: It is easy to add model predictions of such a predefined model but to add the model equations is hard. It would be great if ggpmisc could handle these cases but I fully understand that it is against the framework. |
I guess you are using |
@tillrose Thanks for suggesting this enhancement! |
Thanks! o.k, got it now. You fit a single model but still want to have one equation for each group. |
Moved to a later milestone. Requires implementing a new set of stats based on |
A possible way of handling this request is to implement new statistics using compute panel functions and include the grouping in the model formula. This can be fairly tricky with multiple groupings unless we use an approach similar to that used for |
Is it right that ggpmisc does not support lm models without interactions? Any easy way to handle it?
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