mboost_fit | R Documentation |

Work-horse for gradient boosting for optimizing arbitrary loss functions, where component-wise models are utilized as base-learners. Usually, this function is not called directly by the user.

```
mboost_fit(blg, response, weights = rep(1, NROW(response)), offset = NULL,
family = Gaussian(), control = boost_control(), oobweights =
as.numeric(weights == 0))
```

`blg` |
a list of objects of elements of class |

`response` |
the response variable. |

`weights` |
(optional) a numeric vector of weights to be used in the fitting process. |

`offset` |
a numeric vector to be used as offset (optional). |

`family` |
a |

`control` |
a list of parameters controlling the algorithm. For
more details see |

`oobweights` |
an additional vector of out-of-bag weights, which is
used for the out-of-bag risk (i.e., if |

The function implements component-wise functional gradient boosting in
a generic way. This function is the main work horse and used as back-end by
all boosting algorithms in a unified way. Usually, this function is not
called directly. Note that the more convenient modelling interfaces
`gamboost`

, `glmboost`

and `blackboost`

all call `mboost_fit`

.

Basically, the algorithm is initialized with a function
for computing the negative gradient of the loss function (via its
`family`

argument) and one or more base-learners (given as
`blg`

). Usually `blg`

and `response`

are computed in
the functions `gamboost`

, `glmboost`

,
`blackboost`

or `mboost`

. See there for details
on the specification of base-learners.

The algorithm minimized the in-sample empirical risk defined as
the weighted sum (by `weights`

) of the loss function (corresponding
to the negative gradient) evaluated at the data.

The structure of the model is determined by the structure of the base-learners. If more than one base-learner is given, the model is additive in these components.

Base-learners can be specified via a formula interface
(function `mboost`

) or as a list of objects of class `bl`

,
see, e.g., `bols`

.

`oobweights`

is a vector used internally by `cvrisk`

. When carrying
out cross-validation to determine the optimal stopping iteration of a boosting
model, the default value of `oobweights`

(out-of-bag weights) assures
that the cross-validated risk is computed using the same observation weights
as those used for fitting the boosting model. It is strongly recommended to
leave this argument unspecified.

An object of class `mboost`

with `print`

,
`AIC`

, `plot`

and `predict`

methods being available.

Peter Buehlmann and Bin Yu (2003),
Boosting with the L2 loss: regression and classification.
*Journal of the American Statistical Association*, **98**,
324–339.

Peter Buehlmann and Torsten Hothorn (2007),
Boosting algorithms: regularization, prediction and model fitting.
*Statistical Science*, **22**(4), 477–505.

Torsten Hothorn, Peter Buehlmann, Thomas Kneib, Mattthias Schmid and
Benjamin Hofner (2010), Model-based Boosting 2.0. *Journal of
Machine Learning Research*, **11**, 2109–2113.

Yoav Freund and Robert E. Schapire (1996),
Experiments with a new boosting algorithm.
In *Machine Learning: Proc. Thirteenth International Conference*,
148–156.

Jerome H. Friedman (2001),
Greedy function approximation: A gradient boosting machine.
*The Annals of Statistics*, **29**, 1189–1232.

Benjamin Hofner, Andreas Mayr, Nikolay Robinzonov and Matthias Schmid
(2014). Model-based Boosting in R: A Hands-on Tutorial Using the R
Package mboost. *Computational Statistics*, **29**, 3–35.

\Sexpr[results=rd]{tools:::Rd_expr_doi("10.1007/s00180-012-0382-5")}

Available as vignette via: `vignette(package = "mboost", "mboost_tutorial")`

`glmboost`

for boosted linear models and
`blackboost`

for boosted trees. See e.g. `bbs`

for possible base-learners. See `cvrisk`

for
cross-validated stopping iteration. Furthermore see
`boost_control`

, `Family`

and
`methods`

.

```
data("bodyfat", package = "TH.data")
### formula interface: additive Gaussian model with
### a non-linear step-function in `age', a linear function in `waistcirc'
### and a smooth non-linear smooth function in `hipcirc'
mod <- mboost(DEXfat ~ btree(age) + bols(waistcirc) + bbs(hipcirc),
data = bodyfat)
layout(matrix(1:6, nc = 3, byrow = TRUE))
plot(mod, main = "formula")
### the same
with(bodyfat,
mod <- mboost_fit(list(btree(age), bols(waistcirc), bbs(hipcirc)),
response = DEXfat))
plot(mod, main = "base-learner")
```

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