PolrNN {deeptrafo}

Deep (proportional odds) logistic regression

Description

Deep (proportional odds) logistic regression

Usage

PolrNN(
  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 = "logistic",
  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

df <- data.frame(y = ordered(sample.int(5, 50, replace = TRUE)),
     x = rnorm(50))
if (reticulate::py_module_available("tensorflow") &
    reticulate::py_module_available("keras") &
    reticulate::py_module_available("tensorflow_probability")) {
    m <- PolrNN(y ~ x, data = df)
    coef(m)
}