# bboost

##### Bootstrap Boosting

Wrapper function for applying bootstrap estimation using gradient boosting.

- Keywords
- regression

##### Usage

```
## Bootstrap boosting.
bboost(..., data, type = 1, cores = 1,
n = 2, prob = 0.623, fmstop = NULL,
trace = TRUE, drop = FALSE, replace = FALSE)
```## Plotting function.
bboost_plot(object, col = NULL)

## Predict method.
# S3 method for bboost
predict(object, newdata, ..., cores = 1, pfun = NULL)

##### Arguments

- …
Arguments passed to

`bamlss`

and`predict.bamlss`

.- data
The data frame to be used for modeling.

- type
Type of algorithm,

`type = 1`

uses all observations and samples with replacement,`type = 2`

uses only a fraction specified in`prob`

and samples with replacement.- cores
The number of cores to be used.

- n
The number of bootstrap iterations.

- prob
The fraction that should be used to fit the model in each bootstrap iteration.

- fmstop
The function that should return the optimum stopping iteration. The function must have two arguments: (1) the

`model`

end (2) the`data`

. The function must return a list with two named arguments: (1)`"mstop"`

the optimum stopping iteration and (2) a vector of the objective criterion that should be evaluated by the hold out sample data during each bootstrap iteration. See the examples.- trace
Prints out the current state of the bootstrap algorithm.

- drop
Should only the best set of parameters be saved?

- replace
Sampling with replacement, or sampling

`ceiling(nobs * prob)`

rows of the data for fitting the`n`

models.- object
The

`"bboost"`

object used for prediction and plotting.- col
The color that should be used for plotting.

- newdata
The data frame predictions should be made for.

- pfun
The prediction function that should be used, for example

`predictn`

could be used, too. Note that this is experimental.

##### Value

A list of `bamlss`

objects.

##### See Also

##### Examples

```
# NOT RUN {
## Simulate data.
set.seed(123)
d <- GAMart()
## Estimate model.
f <- num ~ s(x1) + s(x2) + s(x3) + s(lon,lat)
## Function for evaluation of hold out sample
## criterion to find the optimum mstop.
fmstop <- function(model, data) {
p <- predict(model, newdata = data, model = "mu")
mse <- NULL
for(i in 1:nrow(model$parameters))
mse <- c(mse, mean((data$num - p[, i])^2))
list("MSE" = mse, "mstop" = which.min(mse))
}
## Bootstrap boosted models.
b <- bboost(f, data = d, n = 50, cores = 3, fmstop = fmstop)
## Plot hold out sample MSE.
bboost_plot(b)
## Predict for each bootstrap sample.
nd <- data.frame("x2" = seq(0, 1, length = 100))
p <- predict(b, newdata = nd, model = "mu", term = "x2")
plot2d(p ~ x2, data = nd)
# }
```

*Documentation reproduced from package bamlss, version 1.1-2, License: GPL-2 | GPL-3*