Foldwise fit-predict function using a recurrent QRN
Usage
QRN_seq_predict_foldwise(
X,
y,
q_level,
n_folds = 3,
number_fits = 3,
seq_len = 10,
seed = NULL,
...
)Arguments
- X
Matrix of covariates, for training. Entries must be in sequential order.
- y
Response variable vector to model the conditional quantile of, for training. Entries must be in sequential order.
- q_level
Probability level of the desired conditional quantiles to predict.
- n_folds
Number of folds.
- number_fits
Number of restarts, for stability.
- seq_len
Data sequence length (i.e. number of past observations) used during training to predict each response quantile.
- seed
Integer random seed for reproducibility in network weight initialization.
- ...
Other parameters given to
QRN_seq_fit().
Value
A named list containing the foldwise predictions and fits. It namely contains:
- predictions
the numerical vector of quantile predictions for each observation entry in y,
- fits
a list containing the
"QRN_seq"fitted networks for each fold,- cuts
the fold cuts indices,
- folds
a list of lists containing the train indices, validation indices and fold separations as a list for each fold setup,
- n_folds
number of folds,
- q_level
probability level of the predicted quantiles,
- train_losses
the vector of train losses on each fold,
- valid_losses
the vector of validation losses on each fold,
- min_valid_losses
the minimal validation losses obtained on each fold,
- min_valid_e
the epoch index of the minimal validation losses obtained on each fold.