I wonder if someone can give some pointers on alternatives to linear
regression (e.g. Loess) when dealing with multiple variables.
Taking any simple table with three variables, you can very easily get the
intercept and coefficients with:
summary(lm(read_table))
For obvious reasons, the coefficients in a multiple regression are quite
different from what you get if you calculate regressions for the single
variables separately. Alternative approaches such as Loess seem
straightforward when you have only one variable, and have the advantage that
they can cope even if the relationship is not linear.
My question is: how can you extend a flexible approach like Loess to a
multi-variable scenario? I assume that any non-parametric calculation
becomes very resource-intensive very quickly. Can anyone suggest
alternatives (preferably R-based) that cope with multiple variables, even
when the relationship (linear, etc) is not known in advance?
Thanks,
Guy
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