mvs (version 2.1.0)

RF: Function for fitting random forests with multi-view stacking

Description

A wrapper function around randomForest from package of the same name that allows to use it in function MVS.

Usage

RF(
  x,
  y,
  view,
  view.names = NULL,
  skip.meta = FALSE,
  skip.cv = FALSE,
  na.action = "fail",
  na.arguments = NULL,
  progress = TRUE,
  ...
)

Value

An object with S3 class "RF".

Arguments

x

input matrix of dimension nobs x nvars

y

outcome vector of length nobs

view

a vector of length nvars, where each entry is an integer describing to which view each feature corresponds.

view.names

(optional) a character vector of length nviews specifying a name for each view.

skip.meta

whether to skip training the metalearner.

skip.cv

whether to skip generating the cross-validated predictions.

na.action

character specifying what to do with missing values (NA). Options are "pass", "fail", "mean", "mice", and "missForest". Options "mice" and "missForest" requires the respective R package to be installed. Defaults to "pass".

na.arguments

(optional) a named list of arguments to pass to the imputation function (e.g. to mice or missForest).

progress

whether to show a progress bar (only supported when parallel = FALSE).

...

Additional arguments to be passed to function randomForest.

Author

Marjolein Fokkema <[e-mail]>

Examples

# \donttest{
set.seed(012)
n <- 1000
cors <- seq(0.1,0.7,0.1)
X <- matrix(NA, nrow=n, ncol=length(cors)+1)
X[,1] <- rnorm(n)

for(i in 1:length(cors)){
  X[,i+1] <- X[,1]*cors[i] + rnorm(n, 0, sqrt(1-cors[i]^2))
}

beta <- c(1,0,0,0,0,0,0,0)
eta <- X %*% beta
p <- exp(eta)/(1+exp(eta))
y <- rbinom(n, 1, p) ## create binary response
view_index <- rep(1:(ncol(X)/2), each=2)

# Stacked random forest
fit <- RF(X, y, view_index, skip.meta = FALSE, skip.cv = FALSE)

# Stacked random forest
y <- eta + rnorm(100) ## create continuous response
fit <- RF(X, y, view_index,skip.meta = FALSE, skip.cv = FALSE)
# }