dr_init: Initializing a Distributional Regression Model
In davidruegamer/deepregression: Fitting Deep Distributional Regression

dr_init

R Documentation

Initializing a Distributional Regression Model

Description

Initializing a Distributional Regression Model

Usage

dr_init(
  n_obs,
  ncol_structured,
  list_structured,
  nr_params = 2,
  family,
  dist_fun = NULL,
  weights = NULL,
  monitor_metric = list(),
  output_dim = 1,
  mixture_dist = FALSE,
  ind_fun = function(x) x,
  extend_output_dim = 0,
  offset = NULL,
  additional_penalty = NULL,
  fsbatch_options = fsbatch_control(),
  optimizer = tf$keras$optimizers$SGD()
)

Arguments

`n_obs`	number of observations
`ncol_structured`	a vector of length #parameters defining the number of variables used for each of the parameters. If any of the parameters is not modelled using a structured part the corresponding entry must be zero.
`list_structured`	list of (non-linear) structured layers (list length between 0 and number of parameters)
`nr_params`	number of distribution parameters
`family`	family specifying the distribution that is modelled
`dist_fun`	a custom distribution applied to the last layer, see `make_tfd_dist` for more details on how to construct a custom distribution function.
`weights`	observation weights used in the likelihood
`monitor_metric`	see `?deepregression`
`output_dim`	dimension of the output (> 1 for multivariate outcomes)
`mixture_dist`	see `?deepregression`
`ind_fun`	see `?deepregression`
`extend_output_dim`	see `?deepregression`
`offset`	list of logicals corresponding to the paramters; defines per parameter if an offset should be added to the predictor
`additional_penalty`	to specify any additional penalty, provide a function that takes the `model$trainable_weights` as input and applies the additional penalty. In order to get the correct index for the trainable weights, you can run the model once and check its structure.
`fsbatch_options`	options for Fellner-Schall algorithm, see `?deepregression`
`optimizer`	see `?deepregression`