GeDSboost-class: GeDSboost Class

In alattuada/GeDS: Geometrically Designed Spline Regression

GeDSboost Class

Description

A fitted GeDSboost object returned by the function NGeDSboost inheriting the methods from class "GeDSboost". Methods for functions coef, knots, print, predict, visualize_boosting, and bl_imp are available.

Slots

extcall

call to the NGeDSboost function.

formula

A formula object representing the model to be fitted.

args

A list containing the arguments passed to the NGeDSboost function. This list includes:

• response: data.frame containing observations of the response variable.

• predictors: data.frame containing observations of the vector of predictor variables included in the model.

• base_learners: description of model's base learners.

• family: the statistical family. The possible options are

  • mboost::AdaExp()

  • mboost::AUC()

  • mboost::Binomial(type = c("adaboost", "glm"), link = c("logit", "probit", "cloglog", "cauchit", "log"), ...)

  • mboost::Gaussian()

  • mboost::Huber(d = NULL)

  • mboost::Laplace()

  • mboost::Poisson()

  • mboost::GammaReg(nuirange = c(0, 100))

  • mboost::CoxPH()

  • mboost::QuantReg(tau = 0.5, qoffset = 0.5)

  • mboost::ExpectReg(tau = 0.5)

  • mboost::NBinomial(nuirange = c(0, 100))

  • mboost::PropOdds(nuirange = c(-0.5, -1), offrange = c(-5, 5))

  • mboost::Weibull(nuirange = c(0, 100))

  • mboost::Loglog(nuirange = c(0, 100))

  • mboost::Lognormal(nuirange = c(0, 100))

  • mboost::Gehan()

  • mboost::Hurdle(nuirange = c(0, 100))

  • mboost::Multinomial()

  • mboost::Cindex(sigma = 0.1, ipcw = 1)

  • mboost::RCG(nuirange = c(0, 1), offrange = c(-5, 5))

• initial_learner: if TRUE a NGeDS fit was used as initial learner; otherwise, the empirical risk minimizer corresponding to the selected family was employed.

• int.knots_init: if initial_learner = TRUE the maximum number of internal knots set to the NGeDS function before the initial learner fit.

• shrinkage: shrinkage/step-length/learning rate utilized throughout the boosting iterations.

• normalize_data: if TRUE, then response and predictors were standardized before running the FGB algorithm.

• X_mean: mean of the predictor variables (only if normalize_data = TRUE).

• X_sd: standard deviation of the predictors (only if normalize_data = TRUE).

• Y_mean: mean of the response variable (only if normalize_data = TRUE).

• Y_sd: standard deviation of the response variable (only if normalize_data = TRUE).

models

A list containing the 'model' generated at each boosting iteration. Each of these models includes:

• best_bl: fit of the base learner that minimized the residual sum of squares (RSS) in fitting the gradient at the i-th boosting iteration.

• Y_hat: model fitted values at the i-th boosting iteration.

• base_learners: knots and coefficients for each of the base-learners at the i-th boosting iteration.

final_model

A list detailing the final GeDSboost model after the gradient descent algorithm is run. Apart of the components present in any model it includes the quadratic and cubic fits obtained through Schoenberg variation diminishing spline approximation. These include the same elements as Quadratic and Cubic in a GeDS-class object (see SplineReg for details). best_bl is eliminated to simplify the output.

predictions

A list containing the predicted values obtained (linear, quadratic, and cubic).

internal_knots

A list detailing the internal knots obtained for the fits of different order (linear, quadratic, and cubic).

References

Dimitrova, D. S., Kaishev, V. K., Lattuada, A. and Verrall, R. J. (2023). Geometrically designed variable knot splines in generalized (non-)linear models. Applied Mathematics and Computation, 436.
DOI: \Sexpr[results=rd]{tools:::Rd_expr_doi("10.1016/j.amc.2022.127493")}

Dimitrova, D. S., Guillen, E. S. and Kaishev, V. K. (2024). GeDS: An R Package for Regression, Generalized Additive Models and Functional Gradient Boosting, based on Geometrically Designed (GeD) Splines. Manuscript submitted for publication.

alattuada/GeDS documentation built on April 26, 2024, 11:36 a.m.