mod_fun: Transforms a model into a function of inputs -> output

In mosaicModel: An Interface to Statistical Modeling Independent of Model Architecture

Description

Implicit in many statistical models is a function that takes the explanatory variables as inputs and returns the corresponding model value at those inputs. mod_fun creates an R function that works this way. The function returned by 'mod_fun“ has arguments named for each of the explanatory variables. In calling that returned function, you can specify as many or as few of these as you like.

Usage

mod_fun(mod, nlevels = 1)

Arguments

mod	the model to be rendered in a functional form
nlevels	the number of levels for which to find "typical levels" for those arguments not specified in the call to the returned function

Details

When you evaluate the function, you can set the values of all, any, or none of the arguments. Any arguments that you do not set will automatically be set to "typical values" as in mod_eval.

There's nothing essential about the behavior of 'mod_eval“ that explicitly names the arguments to the model function with the names of the explanatory variables. This has been done purely for pedagogical reasons, as a reminder of what those variables are and to make it possible to spot mistaken inputs to models.

Value

a function whose arguments are the explanatory variable used in the model

Examples

my_mod <- lm(mpg ~ hp * cyl, data = mtcars)
f <- mod_fun(my_mod)
names(formals(f)) # the arguments will be the explanatory variables
f(hp = 1:2)
f(hp = 1:2, cyl = 3:4)
f() # typical values for inputs

mosaicModel documentation built on May 2, 2019, 7:59 a.m.