targeted (version 0.6)

learner_gam: Construct a learner

Description

Constructs learner class object for fitting generalized additive models with mgcv::gam.

Usage

learner_gam(
  formula,
  info = "mgcv::gam",
  family = gaussian(),
  select = FALSE,
  gamma = 1,
  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.

family

This is a family object specifying the distribution and link to use in fitting etc (see glm and family). See family.mgcv for a full list of what is available, which goes well beyond exponential family. Note that quasi families actually result in the use of extended quasi-likelihood if method is set to a RE/ML method (McCullagh and Nelder, 1989, 9.6).

select

If this is TRUE then gam can add an extra penalty to each term so that it can be penalized to zero. This means that the smoothing parameter estimation that is part of fitting can completely remove terms from the model. If the corresponding smoothing parameter is estimated as zero then the extra penalty has no effect. Use gamma to increase level of penalization.

gamma

Increase this beyond 1 to produce smoother models. gamma multiplies the effective degrees of freedom in the GCV or UBRE/AIC. n/gamma can be viewed as an effective sample size in the GCV score, and this also enables it to be used with REML/ML. Ignored with P-RE/ML or the efs optimizer.

learner.args

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

...

Additional arguments to mgcv::gam.

Examples

n <- 5e2
x1 <- rnorm(n, sd = 2)
x2 <- rnorm(n)
y <- x1 + cos(x1) + rnorm(n, sd = 0.5**.5)
d0 <- data.frame(y, x1, x2)

lr <- learner_gam(y ~ s(x1) + x2)
lr$estimate(d0)
if (interactive()) {
  plot(lr$fit)
}