R: Bootstrap Boosting

bboost {bamlss} | R Documentation

## 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)

[Package bamlss version 1.1-7 Index]