targeted (version 0.6)

learner_mars: Construct a learner

Description

Constructs a learner class object for fitting multivariate adaptive regression splines with earth::earth.

Usage

learner_mars(
  formula,
  info = "earth::earth",
  degree = 1,
  nprune = NULL,
  glm = NULL,
  learner.args = NULL,
  ...
)

Value

learner object.

Arguments

formula

(formula) Formula specifying response and design matrix.

info

(character) Optional information to describe the instantiated learner object.

degree

Maximum degree of interaction (Friedman's \(mi\)). Default is 1, meaning build an additive model (i.e., no interaction terms).

nprune

Maximum number of terms (including intercept) in the pruned model. Default is NULL, meaning all terms created by the forward pass (but typically not all terms will remain after pruning). Use this to enforce an upper bound on the model size (that is less than nk), or to reduce exhaustive search time with pmethod="exhaustive".

The following arguments are for cross validation.

glm

NULL (default) or a list of arguments to pass on to glm. See the documentation of glm for a description of these arguments See “Generalized linear models” in the vignette. Example:
earth(survived~., data=etitanic, degree=2, glm=list(family=binomial))

The following arguments are for the forward pass.

learner.args

(list) Additional arguments to learner$new().

...

Additional arguments to earth::earth.

Examples

# poisson regression
n <- 5e2
x <- rnorm(n)
w <- 50 + rexp(n, rate = 1 / 5)
y <- rpois(n, exp(2 + 0.5 * x + log(w)) * rgamma(n, 1 / 2, 1 / 2))
d0 <- data.frame(y, x, w)

lr <- learner_mars(y ~ x + offset(log(w)), degree = 2,
  glm = list(family = poisson())
)
lr$estimate(d0)
lr$predict(data.frame(x = 0, w = c(1, 2)))