R/plot_shiny.R

In refund.shiny: Interactive Plotting for Functional Data Analyses

#' Interactive Plotting for Functional Data
#'
#' Function for interactive plotting of functional data analysis results.
#'
#' This package builds on the \code{refund} package: tools in \code{refund} are used to
#' conduct analyses and functions in this package create interactive visualizations of the results
#' of those analyses. There are four major categories of analyses that can be viewed:
#' \enumerate{
#' \item{Functional principal components analyses implemented by \code{\link{fpca.sc}}, \code{\link{fpca.face}},
#' \code{\link{fpca.ssvd}}, and \code{\link{fpca2s}}. Plots show the mean +/- 2SD times each FPC; scree plots;
#' linear combinations of score values and FPCs; reconstructions for each subject; and score scatterplots.}
#' \item{Function-on-scalar regression analyses implemented by \code{\link{bayes_fosr}}. Plots show the raw data
#' colored by covariate values; fitted values depending on covariates; coefficient functions; and residuals.}
#' \item{Multilevel functional principal components analyses implemented by \code{\link{mfpca.sc}}.  Plots show the
#' mean +/- 2SD times each FPC; scree plots; linear combinations of score values and FPCs;
#' reconstructions for each subject; and score scatterplots for levels 1 and 2.}
#' #' \item{Longitudinal functional principal components analyses}
#' }
#'
#'
#'
#' @title plot_shiny The generic function for interactive plots of functional data analyses
#' @param obj object to be plotted. Currently, allowed data types are \code{fpca} \code{mfpca} \code{lfpca} and \code{fosr}.
#' @param ... additional arguments passed to plotting functions
#'
#' @author Jeff Goldsmith \email{jeff.goldsmith@@columbia.edu},
#' Julia Wrobel \email{julia.wrobel@@cuanschutz.edu}
#' @seealso \code{\link{plot_shiny.fpca}}, \code{\link{plot_shiny.mfpca}},  \code{\link{plot_shiny.fosr}}
#' @import shiny
#' @import ggplot2
#' @import dplyr
#' @importFrom refund fpca.sc bayes_fosr fpca.lfda fpca.sc mfpca.sc
#' @export plot_shiny
#'
#' @return This function outputs a shiny app based on the class of the input object.
#' @examples
#'
#'\dontrun{
#' library(dplyr)
#  library(refund)
#' ##### FPCA Example on real data #####
#'
#' data(cd4)
#' SC = fpca.sc(cd4)
#' plot_shiny(SC)
#'
#'

#' ##### FoSR Example #####
#'
#' data(DTI)
#' DTI = DTI[complete.cases(DTI),]
#' fit.fosr = refund::bayes_fosr(cca ~ pasat + sex, data = DTI)
#' plot_shiny(fit.fosr)
#'
#' ##### FoSR Example with outliers #####
#'
#' DTI$cca[1,] = DTI$cca[1,] + .4
#' DTI$cca[2,] = DTI$cca[2,] + .4
#'
#' fosr.dti2 = bayes_fosr(cca ~ pasat + sex, data = DTI)
#' plot_shiny(fosr.dti2)
#'
#' ##### Longitudinal FoSR Examples #####
#'
#' data(DTI2)
#' class(DTI2$cca) = class(DTI2$cca)[-1]
#' DTI2 = subset(DTI2, select = c(cca, id, pasat))
#' DTI2 = DTI2[complete.cases(DTI2),]
#'
#' fosr.dti3 = bayes_fosr(cca ~ pasat + re(id), data = DTI2, Kt = 10, Kp = 4, cov.method = "FPCA")
#' plot_shiny(fosr.dti3)
#' plot_shiny(fosr.dti3$fpca.obj)
#'
#' ##### LFPCA Example on real data #####
#'
#' data(DTI)
#' MS <- subset(DTI, case ==1)  # subset data with multiple sclerosis (MS) case
#'
#' index.na <- which(is.na(MS$cca))
#' Y <- MS$cca; Y[index.na] <- fpca.sc(Y)$Yhat[index.na]; sum(is.na(Y))
#' id <- MS$ID
#' visit.index <- MS$visit
#' visit.time <- MS$visit.time/max(MS$visit.time)
#'
#' lfpca.dti1 <- fpca.lfda(Y = Y, subject.index = id,
#'                        visit.index = visit.index, obsT = visit.time,
#'                        LongiModel.method = 'lme',
#'                        mFPCA.pve = 0.95)
#' plot_shiny(lfpca.dti1)
#'
#' lfpca.dti2 <- fpca.lfda(Y = Y, subject.index = id,
#'                        visit.index = visit.index, obsT = visit.time,
#'                        LongiModel.method = 'fpca.sc',
#'                        mFPCA.pve = 0.80, sFPCA.pve = 0.80)
#' plot_shiny(lfpca.dti2)
#'}
#'
#'
plot_shiny <- function(obj, ...){
  UseMethod("plot_shiny")
}