MVS {mvs}

Multi-View Stacking

Description

Fit a multi-view stacking model with two or more levels.

Usage

MVS(
  x,
  y,
  views,
  type = "StaPLR",
  levels = 2,
  alphas = c(0, 1),
  nnc = c(0, 1),
  parallel = FALSE,
  seeds = NULL,
  progress = TRUE,
  ...
)

mvs(
  x,
  y,
  views,
  type = "StaPLR",
  levels = 2,
  alphas = c(0, 1),
  nnc = c(0, 1),
  parallel = FALSE,
  seeds = NULL,
  progress = TRUE,
  ...
)

Arguments

x

input matrix of dimension nobs x nvars.

y

outcome vector of length nobs.

views

a matrix of dimension nvars x (levels - 1), where each entry is an integer describing to which view each feature corresponds.

type

the type of MVS model to be fitted. Currently only type "StaPLR" is supported.

levels

an integer >= 2, specifying the number of levels in the MVS procedure.

alphas

a vector specifying the value of the alpha parameter to use at each level.

nnc

a binary vector specifying whether to apply nonnegativity constraints or not (1/0) at each level.

parallel

whether to use foreach to fit the learners and obtain the cross-validated predictions at each level in parallel. Executes sequentially unless a parallel back-end is registered beforehand.

seeds

(optional) a vector specifying the seed to use at each level.

progress

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

...

additional arguments to pass to the learning algorithm. See e.g. ?StaPLR. Note that these arguments are passed to the the learner at every level of the MVS procedure.

Value

An object of S3 class "MVS".

Author(s)

Wouter van Loon <[e-mail]>

Examples

 
set.seed(012)
n <- 1000
X <- matrix(rnorm(8500), nrow=n, ncol=85)
top_level <- c(rep(1,45), rep(2,20), rep(3,20))
bottom_level <- c(rep(1:3, each=15), rep(4:5, each=10), rep(6:9, each=5))
views <- cbind(bottom_level, top_level)
beta <- c(rep(10, 55), rep(0, 30)) * ((rbinom(85, 1, 0.5)*2)-1)
eta <- X %*% beta
p <- 1 /(1 + exp(-eta))
y <- rbinom(n, 1, p)

fit <- MVS(x=X, y=y, views=views, type="StaPLR", levels=3, alphas=c(0,1,1), nnc=c(0,1,1))
coefficients <- coef(fit)

new_X <- matrix(rnorm(2*85), nrow=2)
predict(fit, new_X)