Nothing
##' Simulated longitudinal data with functional predictor and scalar response,
##' and structural information associated with predictor function
##'
##' \code{PEER.Sim} contains simulated observations from 100 subjects, each
##' observed at 4 distinct timepoints. At each timepoint bumpy predictor
##' profile is generated randomly and the scalar response variable is generated
##' considering a time-varying regression function and subject intercept.
##' Accompanying the functional predictor and scalar response are the subject
##' ID numbers and time of measurements.
##'
##' \code{Q} represents the 7 x 100 matrix where each row provides structural
##' information about the functional predictor profile for data
##' \code{PEER.Sim}. For specific details about the simulation and Q matrix,
##' please refer to Kundu et. al. (2012).
##'
##'
##' @name PEER.Sim
##' @aliases PEER.Sim Q
##' @docType data
##' @format The data frame \code{PEER.Sim} is made up of subject ID
##' number(\code{id}), subject-specific time of measurement (\code{t}),
##' functional predictor profile (\code{W.1-W.100}) and scalar response
##' (\code{Y})
##' @references Kundu, M. G., Harezlak, J., and Randolph, T. W. (2012).
##' Longitudinal functional models with structured penalties. (please contact
##' J. Harezlak at \email{harezlak@@iupui.edu})
NULL
Any scripts or data that you put into this service are public.
Add the following code to your website.
For more information on customizing the embed code, read Embedding Snippets.