R/data.R

In plsmselect: Linear and Smooth Predictor Modelling with Penalisation and Variable Selection

#' Simulated dataset to be used for gamlasso
#'
#' The package includes a simulated dataset that we will use for the examples.
#'
#' @docType data
#' @usage data(simData)
#'
#' @format A 100-by-23 data frame. There are 10 variables
#'   (\code{x1},...,\code{x10}) corresponding to the linear predictors and 4
#'   (\code{z1},...,\code{z4}) corresponding to the smooth predictors. There are
#'   7 response variables corresponding to the different models fitted -
#'   \itemize{
#'     \item \code{Yg} for the Gaussian response
#'     \item \code{Yb} as Bernoulli and \code{success} and \code{failure} as
#'        Binomial count responses
#'     \item \code{Yp} as the Poisson response
#'     \item \code{time} and \code{status} as the survival model responses
#'   }
#'   The variables starting with \code{X} are the same as the linear predictors but are
#'   concatenated into a matrix \code{X} to be used for the formula implementation of
#'   \code{gamlasso}
#'
#' @details The code for creating this simulated dataset is included in the
#'   vignette of this package.
#'
#' @examples ## Please see examples in ?gamlasso
"simData"



#' Function to create the simulated dataset
#'
#' Undocumented function. Do not use directly
create_dataset = function() {
  ## Nothing in here right now
}