bboost: Bootstrap Boosting

In bamlss: Bayesian Additive Models for Location, Scale, and Shape (and Beyond)

Bootstrap Boosting

Description

Wrapper function for applying bootstrap estimation using gradient boosting.

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 class '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

bamlss, boost, lasso, BayesX

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)

## End(Not run)

bamlss documentation built on Oct. 11, 2024, 5:07 p.m.