step_nls: step_nls

In jkennel/hydrorecipes: Hydrogeology steps

step_nls

Description

Uses the Eigen C++ library fast versions to generate predictions and coefficients from a recipe.

Usage

step_nls(
  .rec,
  formula,
  algorithm = "lm",
  n_subset = 1L,
  n_shift = 0L,
  range = c(-Inf, Inf),
  control = gsl_nls_control(xtol = 1e-08),
  trace = FALSE,
  role = "predictor",
  ...
)

Arguments

.rec	the R6 recipe object.
formula	formula for the regression
algorithm	character string specifying the algorithm to use. The following choices are supported: "lm" Levenberg-Marquardt algorithm (default). "lmaccel" Levenberg-Marquardt algorithm with geodesic acceleration. Stability is controlled by the avmax parameter in control, setting avmax to zero is analogous to not using geodesic acceleration. "dogleg" Powell's dogleg algorithm. "ddogleg" Double dogleg algorithm, an improvement over "dogleg" by including information about the Gauss-Newton step while the iteration is still far from the minimum. "subspace2D" 2D generalization of the dogleg algorithm. This method searches a larger subspace for a solution, it can converge more quickly than "dogleg" on some problems.
n_subset	integer - spacing between adjacent samples in the result.
n_shift	integer - number of values to shift the starting position when n_subset is not equal to 0. The value of n_shift has to be less than 'n_subset'.
range	limit the fitting range to observations between range[1] and range[2]
control	an optional list of control parameters to tune the least squares iterations and multistart algorithm. See gsl_nls_control for the available control parameters and their default values.
trace	logical value indicating if a trace of the iteration progress should be printed. Default is FALSE. If TRUE, the residual (weighted) sum-of-squares and the current parameter estimates are printed after each iteration.
role	character - the name of the role
...	additional arguments

Value

an updated recipe

See Also

Other ols: step_ols()

Examples

data("kennel_2020")
kennel_2020[, datetime := as.numeric(datetime)]
formula <- as.formula(wl~.)
n_knots <- 12
deg_free <- 27
max_lag <- 1 + 720

frec = recipe(formula = formula, data = unclass(kennel_2020)) |>
  step_distributed_lag(baro, knots = hydrorecipes:::log_lags_arma(n_knots, max_lag)) |>
  step_spline_b(datetime, df = deg_free, intercept = FALSE) |>
  step_intercept() |>
  step_drop_columns(baro) |>
  step_drop_columns(datetime) |>
  step_ols(formula) |>
  prep() |>
  bake()

jkennel/hydrorecipes documentation built on Feb. 23, 2025, 11:03 a.m.