R/Plot_trait.R

In James-Thorson/FishLife: Predict Life History Parameters For Any Fish

#' Plots trait predictions
#'
#' Plots predictive interval for a given taxon and trait-pair
#'
#' @param Taxon A taxon (matching row from \code{ParentChild_gz[,'ChildName']})
#' @param params character vector (length of two) of parameters to plot
#' @param SpeciesMatch Species for which to plot data
#' @param partial_match Should \code{Taxon} be an partial match or not (exact match)
#' @param verbose Boolean whether to print name matches or not
#' @param g_i Vector that associates every observation with a level of the taxonomic tree
#' @inheritParams Plot_ellipse
#' @inheritParams Fit_model
#' @inheritParams Calculate_ratio

#' @export
Plot_trait <-
function( Taxon,
          params = c('K','M'),
          Database = FishLife::FishBase_and_RAM,
          Cov_gjj = Database$Cov_gvv,
          Mean_gj = Database$beta_gv,
          ParentChild_gz = Database$ParentChild_gz,
          Y_ij = Database$Y_ij,
          g_i = Database$g_i,
          SpeciesMatch = NULL,
          prob = 0.95,
          add = FALSE,
          xlim = log(c(0.01,2)),
          ylim = xlim,
          partial_match = TRUE,
          main = "",
          xlab = "",
          ylab = "",
          lcol = "black",
          plot_lines = FALSE,
          verbose = FALSE,
          ticks = c(0,5),
          logticks = c(1,2,5),
          obsCov_jj = Database$obsCov_jj,
          include_obscov = FALSE,
          lty = "solid",
          xaxt = "s",
          yaxt = "s",
          ... ){

  # Check for problems
  if( !all(params %in% colnames(Mean_gj)) ){
    stop("Please change params to match `colnames(Mean_gj)`")
  }

  # Match taxon
  if(partial_match==TRUE) Which = grep(Taxon, ParentChild_gz[,'ChildName'])
  if(partial_match==FALSE) Which = which(Taxon == ParentChild_gz[,'ChildName'])
  if( length(Which)!=1 ) stop( paste0("'Taxon' ",Taxon," input matches more or less than one element") )
  if(verbose==TRUE) print( ParentChild_gz[Which,] )

  # Plot ellipse
  axis_scale = sapply(params, FUN = switch,
             "Temperature" = "natural",
             "h" = "natural",
             "rho" = "natural",
             "G" = "natural",
             "r" = "natural",
             "logitbound_h" = "logit_0.2_1.0",
             "Tmid_Spawner" = "natural",
             "Trange_Spawner" = "natural",
             "Tmid_Embryo" = "natural",
             "Trange_Embryo" = "natural",
             "Tmid_Larvae" = "natural",
             "Trange_Larvae" = "natural",
             "Tmid_Adult" = "natural",
             "Trange_Adult" = "natural",
             "Loo" = "log",
             "K" = "log",
             "Winfinity" = "log",
             "tmax" = "log",
             "tm" = "log",
             "M" = "log",
             "Lm" = "log",
             "natural")
  #Plot_ellipse( Cov=Cov_gjj[Which,params,params], Mean=Mean_gj[Which,params], add=add, whichlog=paste(c("x","y")[which(!params%in%c("Temperature","h"))],collapse=""), xlim=xlim, ylim=ylim, main=main, xlab=xlab, lcol=lcol, plot_lines=plot_lines, ticks=ticks, logticks=logticks, prob=prob, ... )
  Plot_ellipse( Cov = Cov_gjj[Which,params,params],
                Mean = Mean_gj[Which,params],
                add = add,
                axis_scale = axis_scale,
                xlim = xlim,
                ylim = ylim,
                main = main,
                xlab = xlab,
                lcol = lcol,
                plot_lines = plot_lines,
                ticks = ticks,
                logticks = logticks,
                prob = prob,
                lty = lty,
                xaxt = xaxt,
                yaxt = yaxt,
                ... )
  #plot.new()

  # Plot observations
  if( !is.null(SpeciesMatch) ){
    Which = grep(SpeciesMatch, ParentChild_gz[,'ChildName'])
    Which = which( g_i %in% Which )
    points( x=Y_ij[Which,params[1]], y=Y_ij[Which,params[2]] )
  }

  # insibile return
  return( invisible(list("Cov_pred"=Cov_gjj[Which,,], "Mean_pred"=Mean_gj[Which,])) )
}