bamlss: dl.bamlss – R documentation

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dl.bamlss

Deep Learning BAMLSS

Description

This function interfaces keras infrastructures for high-level neural networks. The function can be used as a standalone model fitting engine such as bamlss or as an on top model engine to capture special features in the data that could not be captures by other model fitting engines.

Usage

## Deep learning bamlss.
dl.bamlss(object, offset = NULL, weights = NULL,
  eps = .Machine$double.eps^0.25, maxit = 100,
  force.stop = TRUE, epochs = 30, optimizer = NULL,
  batch_size = NULL, keras.model = NULL,
  verbose = TRUE, digits = 4, ...)

## Predict method.
## S3 method for class 'dl.bamlss'
predict(object, newdata, model = NULL, drop = TRUE, ...)

Arguments

`object`	An object of class `"bamlss"` or a `bamlss.formula`.
`offset`	A `list` or `data.frame`. Can be used to supply model offsets for use in fitting, e.g., fitted values from an initial model fit.
`weights`	Prior weights on the data.
`eps`	The relative convergence tolerance of the algorithm.
`maxit`	Integer, maximum number of iterations of the algorithm.
`force.stop`	Logical. should the algorithm stop if relative change is smaller than `eps`.
`epochs`	Number of times to iterate over the training data arrays, see `fit`.
`optimizer`	Character or call to optimizer functions to be used within `fit`. For character, options are: `"adam"` `"sgd"`, `"rmsprop"`, `"adagrad"`, `"adadelta"`, `"adamax"`, `"nadam"`. The default is `optimizer_rmsprop` with learning rate set to `1e-04`.
`batch_size`	Number of samples per gradient update, see `fit`.
`keras.model`	A compiled model using keras, e.g., using `keras_model_sequential` and `compile`. Note that the last layer only has one unit with linear activation function.
`verbose`	Print information during runtime of the algorithm.
`digits`	Set the digits for printing when `verbose = TRUE`.
`newdata`	A `list` or `data.frame` that should be used for prediction.
`model`	Character or integer specifying for which distributional parameter predictions should be computed.
`drop`	If predictions for only one `model` are returned, the list structure is dropped.
`...`	For function `dl.boost()`, arguments passed to `bamlss.frame`.

Details

The default keras model is a sequential model with two hidden layers with "relu" activation function and 100 units in each layer. Between each layer is a dropout layer with 0.1 dropout rate.

Value

For function dl.bamlss() an object of class "dl.bamlss". Note that extractor functions fitted and residuals.bamlss can be applied. For function predict.dl.bamlss() a list or vector of predicted values.

WARNINGS

The BAMLSS deep learning infrastructure is still experimental!

Examples

## Not run: ## Simulate data.
set.seed(123)
n <- 300
x <- runif(n, -3, 3)
fsigma <- -2 + cos(x)
y <- sin(x) + rnorm(n, sd = exp(fsigma))

## Setup model formula.
f <- list(
  y ~ x,
  sigma ~ x
)

## Fit neural network.
library("keras")
b <- dl.bamlss(f)

## Plot estimated functions.
par(mfrow = c(1, 2))
plot(x, y)
plot2d(fitted(b)$mu ~ x, add = TRUE)
plot2d(fitted(b)$sigma ~ x,
  ylim = range(c(fitted(b)$sigma, fsigma)))
plot2d(fsigma ~ x, add = TRUE, col.lines = "red")

## Another example identifying structures that are
## not captured by the initial model.
set.seed(123)
d <- GAMart()
b1 <- bamlss(num ~ s(x1) + s(x2) + s(x3), data = d, sampler = FALSE)
b2 <- dl.bamlss(num ~ lon + lat, data = d, offset = fitted(b1))
p <- predict(b2, model = "mu")
par(mfrow = c(1, 1))
plot3d(p ~ lon + lat, data = d, symmetric = FALSE)

## End(Not run)

bamlss

Bayesian Additive Models for Location, Scale, and Shape (and Beyond)

v1.1-3

GPL-2 | GPL-3

Authors

Nikolaus Umlauf [aut, cre] (<https://orcid.org/0000-0003-2160-9803>), Nadja Klein [aut] (<https://orcid.org/0000-0002-5072-5347>), Achim Zeileis [aut] (<https://orcid.org/0000-0003-0918-3766>), Meike Koehler [ctb], Thorsten Simon [aut] (<https://orcid.org/0000-0002-3778-7738>), Stanislaus Stadlmann [ctb]

Initial release

2021-01-25