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
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 | ## 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 |
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 |
convert_fun |
how should the resulting tensor be converted,
per default |
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 |
save_fun |
function applied to the model in each fold to be stored in the final result |
data |
either |
probs |
the quantile value(s) |
Returns an object drCV
, a list, one list element for each fold
containing the model fit and the weighthistory
.
Add the following code to your website.
For more information on customizing the embed code, read Embedding Snippets.