Fit one learner at one grain

Usage

fit_learner(
  learner,
  x,
  y,
  response = "presence_absence",
  control = NULL,
  group = NULL,
  ...
)

# S3 method for class 'timesift_fit'
predict(object, newdata, ...)

Arguments

learner: A learner(), or the name of a registered one.
x: A grain_matrix() result.
y: The response for the same units.
response: Name of the registered response head. "presence_absence" ships.
control: The run's train_control(). The learner's own control overrides it on the settings that control names, and a setting given in ... overrides both.
group: One value per unit of x naming the group it belongs to, or NULL. A learner whose fit declares group draws its inner splits by it. Under grouped_cv() the run hands every fit the grouping its fold map carries.
...: Passed to the learner's fit.
object: A timesift_fit.
newdata: A representation of the same channels for the units to predict.

Value

A timesift_fit, which stats::predict() takes a new representation.

Examples

set.seed(1)
t <- seq(as.POSIXct("2021-09-01", tz = "UTC"), by = "hour", length.out = 24 * 120)
units <- sprintf("p%02d", 1:40)
d <- data.frame(plot = rep(units, each = length(t)), t = rep(t, length(units)),
                temp = as.numeric(replicate(length(units), rnorm(length(t)))))
x <- grain_matrix(d, plot, t, temp, grain = "month")
y <- matrix(rbinom(80, 1, 0.4), nrow = 40, dimnames = list(units, c("sp1", "sp2")))
fit <- fit_learner(elasticnet(), x, y)
dim(stats::predict(fit, x))