LehmanNN: Lehmann-type neural network transformation models

In deeptrafo: Fitting Deep Conditional Transformation Models

Lehmann-type neural network transformation models

Description

Lehmann-type neural network transformation models

Usage

LehmanNN(
  formula,
  data,
  response_type = get_response_type(data[[all.vars(formula)[1]]]),
  order = get_order(response_type, data[[all.vars(formula)[1]]]),
  addconst_interaction = 0,
  latent_distr = "gumbel",
  monitor_metrics = NULL,
  trafo_options = trafo_control(order_bsp = order, response_type = response_type),
  ...
)

Arguments

formula	Formula specifying the response, interaction, shift terms as response | interacting ~ shifting. auto-regressive transformation models (ATMs).
data	Named list or data.frame which may contain both structured and unstructured data.
response_type	Character; type of response. One of "continuous", "survival", "count", or "ordered". If not supplied manually it is determined by the first entry in data[[response]].
order	Integer; order of the response basis. Default 10 for Bernstein basis or number of levels minus one for ordinal responses.
addconst_interaction	Positive constant; a constant added to the additive predictor of the interaction term. If NULL, terms are left unchanged. If 0 and predictors have negative values in their design matrix, the minimum value of all predictors is added to ensure positivity. If > 0, the minimum value plus the addconst_interaction is added to each predictor in the interaction term. This ensures a monotone non-decreasing transformation function in the response when using (tensor product) spline bases in the interacting term.
latent_distr	A tfd_distribution or character; the base distribution for transformation models. If character, can be "normal", "logistic", "gumbel" or "gompertz".
monitor_metrics	See deepregression
trafo_options	Options for transformation models such as the basis function used, see trafo_control for more details.
...	Additional arguments passed to deepregression

Value

See return statement of deeptrafo

Examples

if (.Platform$OS.type != "windows" &&
    reticulate::py_available() &&
    reticulate::py_module_available("tensorflow") &&
    reticulate::py_module_available("keras") &&
    reticulate::py_module_available("tensorflow_probability")) {
    df <- data.frame(y = rnorm(50), x = rnorm(50))
    m <- LehmanNN(y ~ 0 + x, data = df)
    coef(m)
}

deeptrafo documentation built on April 3, 2025, 10:38 p.m.