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Use of Gradient Descent Boosting for model estimation

Source: R/engine_gdb.R
engine_gdb.Rd

Gradient descent boosting is an efficient way to optimize any loss function of a generalized linear or additive model (such as the GAMs available through the "mgcv" R-package). It furthermore automatically regularizes the fit, thus the resulting model only contains the covariates whose baselearners have some influence on the response. Depending on the type of the add_biodiversity data, either poisson process models or logistic regressions are estimated. If the "only_linear" term in train is set to FALSE, splines are added to the estimation, thus providing a non-linear additive inference.

Usage

engine_gdb(
  x,
  iter = 2000,
  learning_rate = 0.1,
  empirical_risk = "inbag",
  type = "response",
  ...
)

Arguments

x

distribution() (i.e. BiodiversityDistribution) object.

iter

An integer giving the number of boosting iterations (Default: 2e3L).

learning_rate

A bounded numeric value between 0 and 1 defining the shrinkage parameter.

empirical_risk

method for empirical risk calculation. Available options are 'inbag', 'oobag' and 'none'. (Default: 'inbag').

type

The mode used for creating posterior predictions. Either making "link", "response" or "class" (Default: "response").

...

Other variables or control parameters

Value

An engine.

Details

: This package requires the "mboost" R-package to be installed. It is in philosophy somewhat related to the engine_xgboost and "XGBoost" R-package, however providing some additional desirable features that make estimation quicker and particularly useful for spatial projections. Such as for instance the ability to specifically add spatial baselearners via add_latent_spatial or the specification of monotonically constrained priors via GDBPrior.

References

  • Hofner, B., Mayr, A., Robinzonov, N., & Schmid, M. (2014). Model-based boosting in R: a hands-on tutorial using the R package mboost. Computational statistics, 29(1-2), 3-35.

  • Hofner, B., Müller, J., Hothorn, T., (2011). Monotonicity-constrained species distribution models. Ecology 92, 1895–901.

  • Mayr, A., Hofner, B. and Schmid, M. (2012). The importance of knowing when to stop - a sequential stopping rule for component-wise gradient boosting. Methods of Information in Medicine, 51, 178–186.

See also

Other engine: engine_bart(), engine_breg(), engine_glm(), engine_glmnet(), engine_inla(), engine_inlabru(), engine_stan(), engine_xgboost()

Examples

if (FALSE) {
# Add GDB as an engine
x <- distribution(background) |> engine_gdb(iter = 1000)
}