R/plot.predict.slmfit.R

In highamm/sptotal: Predicting Totals and Weighted Sums from Spatial Data

#' Create a default map from predictions
#'
#' Creates a default map for the predictions of unobserved sites.
#'  Note that all predictions are stored in a data frame in the output
#' of \code{\link{predict.slmfit}()}. Therefore, if a user
#' would like to create his or her own plot, they can easily do so using
#' this data frame.
#'
#' @param x the output of the \code{\link{predict.slmfit}()} function,
#' of class \code{predict.slmfit}
#' @param ... further arguments passed to or from other methods.
#' @return a plot with x-coordinates on the x-axis and y-coordinates on
#' the y-axis that is coloured by predictions, with points with an X
#' denoting that a site was sampled and filled circles denoting
#' unsampled sites.
#' @examples
#' data(exampledataset) ## load a toy data set
#' slmobj <- slmfit(formula = counts ~ pred1 + pred2, data = exampledataset,
#' xcoordcol = 'xcoords', ycoordcol = 'ycoords', areacol = 'areavar')
#' predobj <- predict(slmobj)
#' plot(predobj)
#' @importFrom ggplot2 ggplot
#' @importFrom ggplot2 aes
#' @importFrom ggplot2 element_blank
#' @importFrom ggplot2 .data
#' @export

plot.predict.slmfit <- function(x, ...) {

  pred.vals <- data.frame(x$Pred_df)
  formula <- x$formula

  shapevals <- c(16, 13)

  nameresp <- as.vector((noquote(paste(base::all.vars(formula)[1],
                                       "_pred_count", sep = ""))))
  sampind <- pred.vals[ ,paste(base::all.vars(formula)[1], "_sampind",
                               sep = "")]
  pred.vals$sampindfact_ <- factor(sampind)
  preds <- pred.vals[ ,paste(base::all.vars(formula)[1], "_pred_count",
                             sep = "")]
  pcolname <- paste(base::all.vars(formula)[1], "_pred_count",
                    sep = "")

  p3 <- ggplot(data = pred.vals, aes(x = .data$xcoordsTM_,
                                               y = .data$ycoordsTM_,
                                              shape = .data$sampindfact_)) +
    ggplot2::geom_point(aes(colour = preds), size = 3, stroke = 2) +
    #, ##size = pointsize,
    ## stroke = 3) +
    ggplot2::scale_fill_viridis_c() +
    ggplot2::scale_colour_viridis_c(name = "Response") +
    ggplot2::theme_bw() +
    ggplot2::scale_shape_manual("Samp Indicator",
                       labels = c("Unsampled", "Sampled"),
                       values = shapevals) +
    ggplot2::theme(panel.background = element_blank(),
          panel.grid.major = element_blank(),
          panel.grid.minor = element_blank()) +
    ggplot2::xlab("") + ggplot2::ylab("")

  print(p3)
}