R Graphical Manual

Last data update: 2014.03.03

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Results 1 - 10 of 18 found.
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Family (Package: mboost) : Gradient Boosting Families

boost_family objects provide a convenient way to specify loss functions and corresponding risk functions to be optimized by one of the boosting algorithms implemented in this package.

● Data Source: CranContrib
● Keywords: models
● Alias: AUC, AdaExp, Binomial, CoxPH, ExpectReg, Family, GammaReg, GaussClass, GaussReg, Gaussian, Gehan, Huber, Hurdle, Laplace, Loglog, Lognormal, Multinomial, NBinomial, Poisson, PropOdds, QuantReg, Weibull
● 0 images

mboost-package (Package: mboost) :

Functional gradient descent algorithm (boosting) for optimizing general risk functions utilizing component-wise (penalized) least squares estimates or regression trees as base-learners for fitting generalized linear, additive and interaction models to potentially high-dimensional data.

● Data Source: CranContrib
● Keywords: models, nonparametric, package, smooth
● Alias: mboost-package, mboost_package, package-mboost, package_mboost
● 0 images

stabsel (Package: mboost) :

Selection of influential variables or model components with error control.

● Data Source: CranContrib
● Keywords: nonparametric
● Alias: stabsel, stabsel.mboost, stabsel_parameters.mboost
● 0 images

survFit (Package: mboost) : Survival Curves for a Cox Proportional Hazards Model

Computes the predicted survivor function for a Cox proportional hazards model.

● Data Source: CranContrib
● Keywords:
● Alias: plot.survFit, survFit, survFit.mboost
● 0 images

IPCweights (Package: mboost) : Inverse Probability of Censoring Weights

Compute weights for censored regression via the inverted probability of censoring principle.

● Data Source: CranContrib
● Keywords: survival
● Alias: IPCweights
● 0 images

mboost (Package: mboost) : Model-based Gradient Boosting

Gradient boosting for optimizing arbitrary loss functions, where component-wise models are utilized as base-learners.

● Data Source: CranContrib
● Keywords: models, nonlinear
● Alias: mboost, mboost_fit
● 0 images

blackboost (Package: mboost) : Gradient Boosting with Regression Trees

Gradient boosting for optimizing arbitrary loss functions where regression trees are utilized as base-learners.

● Data Source: CranContrib
● Keywords: models, regression
● Alias: blackboost
● 0 images

confint.mboost (Package: mboost) :

Compute and display pointwise confidence intervals

● Data Source: CranContrib
● Keywords: methods
● Alias: confint.glmboost, confint.mboost
● 0 images

cvrisk (Package: mboost) : Cross-Validation

Cross-validated estimation of the empirical risk for hyper-parameter selection.

● Data Source: CranContrib
● Keywords: models, regression
● Alias: cv, cvrisk, cvrisk.mboost
● 0 images

boost_control (Package: mboost) : Control Hyper-parameters for Boosting Algorithms

Definition of the initial number of boosting iterations, step size and other hyper-parameters for boosting algorithms.

● Data Source: CranContrib
● Keywords: misc
● Alias: boost_control
● 0 images