trafo_control {deeptrafo}

Options for transformation models

Description

Options for transformation models

Usage

trafo_control(
  order_bsp = 10L,
  support = function(y) range(y),
  y_basis_fun = NULL,
  y_basis_fun_lower = NULL,
  y_basis_fun_prime = NULL,
  penalize_bsp = 0,
  order_bsp_penalty = 2,
  tf_bsps = FALSE,
  response_type = c("continuous", "ordered", "survival", "count"),
  atm_toplayer = function(x) layer_dense(x, units = 1L, name = "atm_toplayer", use_bias
    = FALSE),
  basis = c("bernstein", "ordered", "shiftscale")
)

Arguments

order_bsp

The order of Bernstein polynomials in case y_basis_fun is a Bernstein polynomial defined by eval_bsp or (one less than) the number of classes of an ordinal outcome.

support

A function returning a vector with two elements, namely the support for the basis of y.

y_basis_fun

Function; basis function for Y

y_basis_fun_lower

Function; basis function for lower bound of interval censored response

y_basis_fun_prime

Function; basis function derivative

penalize_bsp

Scalar value > 0; controls amount of penalization of Bernstein polynomials.

order_bsp_penalty

Integer; order of Bernstein polynomial penalty. 0 results in a penalty based on integrated squared second order derivatives, values >= 1 in difference penalties.

tf_bsps

Logical; whether to use a TensorFlow implementation of the Bernstein polynomial functions.

response_type

Character; type of response can be continuous, ordered, survival, or count.

atm_toplayer

Function; a function specifying the layer on top of ATM lags.

basis

Character or function; implemented options are "bernstein" (a Bernstein polynomial basis), "ordered" (for ordinal responses), or "shiftscale" for (log-) linear bases

Value

Returns a named list with all options, basis functions, support, and penalties.