mboost (version 1.0-5)

methods: Methods for Gradient Boosting Objects

Description

Methods for models fitted by boosting algorithms.

Usage

## S3 method for class 'glmboost':
print(x, ...)
## S3 method for class 'gamboost':
print(x, ...)
## S3 method for class 'glmboost':
coef(object, ...)
## S3 method for class 'gamboost':
coef(object, ...)
## S3 method for class 'gamboost':
AIC(object, method = c("corrected", "classical"), ..., k = 2)
## S3 method for class 'glmboost':
AIC(object, method = c("corrected", "classical"), 
    df = c("trace", "actset"), ..., k = 2)
## S3 method for class 'gbAIC':
mstop(object, ...)
## S3 method for class 'gb':
mstop(object, ...)
## S3 method for class 'cvrisk':
mstop(object, ...)
## S3 method for class 'blackboost':
mstop(object, ...)
## S3 method for class 'gb':
predict(object, newdata = NULL, type = c("lp", "response"), 
       allIterations = FALSE, ...)
## S3 method for class 'blackboost':
predict(object, newdata = NULL, type = c("lp", "response"), 
       allIterations = FALSE, ...)
## S3 method for class 'gb':
fitted(object, type = c("lp", "response"), ...)
## S3 method for class 'gb':
logLik(object, ...)

Arguments

object

objects of class glmboost, gamboost or gbAIC.

x

objects of class glmboost or gamboost.

newdata

optionally, a data frame in which to look for variables with which to predict.

type

a character indicating whether the fit or the response (classes) should be predicted in case of classification problems.

allIterations

computes the (linear) predictor for all boosting iterations simultaneously and returns a matrix.

method

a character specifying if the corrected AIC criterion or a classical (-2 logLik + k * df) should be computed. df{ a character specifying how degrees of freedom should be computed: trace