methodDR: Generic functions for deepregression models
In deepregression: Fitting Deep Distributional Regression

Description Usage Arguments Value

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

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

## 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(),
  convertfun = function(x) tf$constant(x, dtype = "float32"),
  ...
)

## 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, ...)

`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.
`type`	either NULL (all types of coefficients are returned), "linear" for linear coefficients or "smooth" for coefficients of smooth terms
`...`	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
`convertfun`	function to convert R into Tensor object
`plot`	whether to plot the resulting losses in each fold
`print_folds`	whether to print the current fold
`cv_folds`	an integer if list with train and test data sets
`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)