bboost {bamlss} | R Documentation |
Wrapper function for applying bootstrap estimation using gradient boosting.
## 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 passed to |
data |
The data frame to be used for modeling. |
type |
Type of algorithm, |
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 |
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 |
object |
The |
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 |
A list of bamlss
objects.
## 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)