What is the most efficient way to use the fitted models?

Question

I'm generating a simple linear model ($y=mx+b$) by fitting to the background of some data, and then trying to plug in the x-values of the data I'm actually interested in.

I'm a bit surprised that FittedModels aren't listable, so I tried using Map. This turned out to be exceedingly slow. For example:

lm = LinearModelFit[Table[{x, 3 x + 6.2}, {x, 100}], x, x];
AbsoluteTiming[lm/@Range[300000];]
AbsoluteTiming[3 Range[300000] + 6.2;]
AbsoluteTiming[3 # + 6.2&/@Range[300000];]
AbsoluteTiming[Normal[lm]/.x -> Range[300000];]
(* {25.037, Null} *)
(* {0.002096, Null} *)
(* {0.010351, Null} *)
(* {0.004413, Null} *)

Using Normal seems to be the fastest way of using the linear model. Is this really the best way to extract values from a FittedModel?

What is the best (or most usual) way to extract many values from a model?

This seems like it would be a common problem, so I'm certain there must be something about it either here or elsewhere, but perhaps I'm not using the correct search terms.

Answer 1

Use lm["Function"]. (It is also listable in this case.)

lm = LinearModelFit[Table[{x, 3 x + 6.2}, {x, 100}], x, x];

AbsoluteTiming[res1 = lm["Function"] /@ Range[300000];]

(* {0.007482, Null} *)

AbsoluteTiming[res1a = lm["Function"][Range[300000]];]

(* {0.001949, Null} *)

AbsoluteTiming[res2 = lm /@ Range[300000];]

(* {21.0912, Null} *)

res1 == res1a == res2

(* True *)