SurvregNN {deeptrafo}

Deep parametric survival regression

Description

Deep parametric survival regression

Usage

SurvregNN(
  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 = "gompertz",
  monitor_metrics = NULL,
  trafo_options = NULL,
  ...
)

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

set.seed(1)
df <- data.frame(y = abs(1 + rnorm(50)), x = rnorm(50))
if (reticulate::py_module_available("tensorflow") &
    reticulate::py_module_available("keras") &
    reticulate::py_module_available("tensorflow_probability")) {
    m <- SurvregNN(y ~ 0 + x, data = df)

    optimizer <- optimizer_adam(learning_rate = 0.01, decay = 4e-4)
    m <- SurvregNN(y ~ 0 + x, data = df, optimizer = optimizer)
    library(tram)
    fit(m, epochs = 500L, validation_split = 0)
    logLik(mm <- Survreg(y ~ x, data = df, dist = "loglogistic")); logLik(m)
    coef(mm, with_baseline = TRUE); unlist(c(coef(m, which = "interacting"),
                                             coef(m, which = "shifting")))

}