R/PLOT_plotCrit.R

In RMixtComp: Mixture Models with Heterogeneous and (Partially) Missing Data

# MixtComp version 4.0  - july 2019
# Copyright (C) Inria - Université de Lille - CNRS

# This program is free software: you can redistribute it and/or modify
# it under the terms of the GNU Affero General Public License as
# published by the Free Software Foundation, either version 3 of the
# License, or (at your option) any later version.
# This program is distributed in the hope that it will be useful,
# but WITHOUT ANY WARRANTY; without even the implied warranty of
# MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE.  See the
# GNU Affero General Public License for more details.
#
# You should have received a copy of the GNU Affero General Public License
# along with this program.  If not, see <https://www.gnu.org/licenses/>

#' Plot BIC and ICL
#'
#' Plot BIC and ICL with regards to the number of classes
#'
#' @param output \emph{MixtCompLearn} object
#' @param crit criterion to plot (can be "BIC", "ICL" or  c("BIC", "ICL") (default))
#' @param pkg "ggplot2" or "plotly". Package used to plot
#' @param ... arguments to be passed to plot_ly
#'
#' @return \code{ggplot2} or \code{plotly} object
#'
#' @examples
#' data(iris)
#'
#' # define the algorithm's parameters
#' algo <- createAlgo()
#'
#' # keep only 3 variables
#' model <- list(
#'   Petal.Width = "Gaussian", Petal.Length = "Gaussian",
#'   Sepal.Width = "Gaussian", Sepal.Length = "Gaussian"
#' )
#'
#' # run RMixtComp in unsupervised clustering mode + data as matrix
#' res <- mixtCompLearn(iris, model, algo, nClass = 1:4, nCore = 1)
#'
#' # plot
#' plotCrit(res)
#'
#' @family plot
#' @author Quentin Grimonprez
#' @export
plotCrit <- function(output, crit = c("BIC", "ICL"), pkg = c("ggplot2", "plotly"), ...) {
  pkg <- match.arg(pkg)

  crit <- intersect(crit, c("BIC", "ICL"))
  if (length(crit) == 0) {
    crit <- c("BIC", "ICL")
  }

  p <- switch(pkg,
    "ggplot2" = ggplotCrit(output$crit[rownames(output$crit) %in% crit, , drop = FALSE], output$nClass),
    "plotly" = plotlyCrit(output$crit, output$nClass, ...)
  )

  p
}

# @author Quentin Grimonprez
plotlyCrit <- function(crit, nClass, ...) {
  p <- plot_ly(x = nClass, y = crit[1, ], type = "scatter", mode = "lines+markers", name = "BIC", ...) %>%
    add_trace(x = nClass, y = crit[2, ], name = "ICL") %>%
    layout(
      title = "Criterion",
      showlegend = TRUE,
      xaxis = list(title = "Number of classes", range = range(nClass)),
      yaxis = list(title = "Value")
    )

  p
}

# @author Quentin Grimonprez
ggplotCrit <- function(crit, nClass) {
  df <- data.frame(class = nClass, value = as.vector(t(crit)), Criterion = rep(rownames(crit), each = length(nClass)))

  p <- ggplot(data = df, aes_string(x = "class", y = "value", col = "Criterion")) +
    labs(title = "Criterion", x = "Number of classes", y = "value") +
    geom_line() +
    geom_point(aes_string(shape = "Criterion"), size = 2) +
    scale_x_continuous(breaks = pretty_breaks()(nClass))
  p
}