methodDR: Generic functions for deepregression models

In deepregression: Fitting Deep Distributional Regression

Generic functions for deepregression models

Description

Generic functions for deepregression models

Predict based on a deepregression object

Function to extract fitted distribution

Fit a deepregression model (pendant to fit for keras)

Extract layer weights / coefficients from model

Print function for deepregression model

Cross-validation for deepgression objects

mean of model fit

Standard deviation of fit distribution

Calculate the distribution quantiles

Usage

## S3 method for class 'deepregression'
plot(
  x,
  which = NULL,
  which_param = 1,
  only_data = FALSE,
  grid_length = 40,
  main_multiple = NULL,
  type = "b",
  get_weight_fun = get_weight_by_name,
  ...
)

## S3 method for class 'deepregression'
predict(
  object,
  newdata = NULL,
  batch_size = NULL,
  apply_fun = tfd_mean,
  convert_fun = as.matrix,
  ...
)

## S3 method for class 'deepregression'
fitted(object, apply_fun = tfd_mean, ...)

## S3 method for class 'deepregression'
fit(
  object,
  batch_size = 32,
  epochs = 10,
  early_stopping = FALSE,
  early_stopping_metric = "val_loss",
  verbose = TRUE,
  view_metrics = FALSE,
  patience = 20,
  save_weights = FALSE,
  validation_data = NULL,
  validation_split = ifelse(is.null(validation_data), 0.1, 0),
  callbacks = list(),
  na_handler = na_omit_list,
  ...
)

## S3 method for class 'deepregression'
coef(object, which_param = 1, type = NULL, ...)

## S3 method for class 'deepregression'
print(x, ...)

## S3 method for class 'deepregression'
cv(
  x,
  verbose = FALSE,
  patience = 20,
  plot = TRUE,
  print_folds = TRUE,
  cv_folds = 5,
  stop_if_nan = TRUE,
  mylapply = lapply,
  save_weights = FALSE,
  callbacks = list(),
  save_fun = NULL,
  ...
)

## S3 method for class 'deepregression'
mean(x, data = NULL, ...)

## S3 method for class 'deepregression'
stddev(x, data = NULL, ...)

## S3 method for class 'deepregression'
quant(x, data = NULL, probs, ...)

Arguments

x	a deepregression object
which	character vector or number(s) identifying the effect to plot; default plots all effects
which_param	integer, indicating for which distribution parameter coefficients should be returned (default is first parameter)
only_data	logical, if TRUE, only the data for plotting is returned
grid_length	the length of an equidistant grid at which a two-dimensional function is evaluated for plotting.
main_multiple	vector of strings; plot main titles if multiple plots are selected
type	either NULL (all types of coefficients are returned), "linear" for linear coefficients or "smooth" for coefficients of smooth terms
get_weight_fun	function to extract weight from model given x, a name and param_nr
...	arguments passed to the predict function
object	a deepregression model
newdata	optional new data, either data.frame or list
batch_size	integer, the batch size used for mini-batch training
apply_fun	function applied to fitted distribution, per default tfd_mean
convert_fun	how should the resulting tensor be converted, per default as.matrix
epochs	integer, the number of epochs to fit the model
early_stopping	logical, whether early stopping should be user.
early_stopping_metric	character, based on which metric should early stopping be trigged (default: "val_loss")
verbose	whether to print training in each fold
view_metrics	logical, whether to trigger the Viewer in RStudio / Browser.
patience	number of patience for early stopping
save_weights	logical, whether to save weights in each epoch.
validation_data	optional specified validation data
validation_split	float in [0,1] defining the amount of data used for validation
callbacks	a list of callbacks used for fitting
na_handler	function to deal with NAs
plot	whether to plot the resulting losses in each fold
print_folds	whether to print the current fold
cv_folds	an integer; can also be a list of lists with train and test data sets per fold
stop_if_nan	logical; whether to stop CV if NaN values occur
mylapply	lapply function to be used; defaults to lapply
save_fun	function applied to the model in each fold to be stored in the final result
data	either NULL or a new data set
probs	the quantile value(s)

Value

Returns an object drCV, a list, one list element for each fold containing the model fit and the weighthistory.

deepregression documentation built on Sept. 9, 2025, 5:27 p.m.