R/AVGRatioPlot.R

In lem-usp/EvoMod-R: functions for dealing with evomod simulations results

#'AVGRatio Plot
#'
#'Plots AVGRatio output
#'@export
#'@import ggplot2 reshape2 plyr dplyr
AVGRatioPlot  <- function(pop.list, modules = FALSE, x.axis = 'selection.strength', x.label = 'Peak Movement Rate'){
  avg <- ldply(pop.list, function(x) AVGRatio(x$p.cor, x[[x.axis]]), .progress = 'text')
  if(modules){
    m.avg = melt(avg[,-c(1, 3, 4, 7)], id.vars = c('hypothesis', 'Selection_Strength'))
    m.avg = filter(m.avg, (hypothesis == "Full Integration" & variable == "AVG-") |
                          (hypothesis == "module.1"         & variable == "AVG+") |
                          (hypothesis == "module.2"         & variable == "AVG+"))
    
    m.avg = ddply(m.avg, .(Selection_Strength, hypothesis, variable), 
          function(x) {y <- x$value 
                       c(mean = mean(y), quantile(y, c(0.025, 0.975)))
          })
    names(m.avg) = c('Selection_Strength', 'hypothesis', 'variable', 'mean', 'ymin', 'ymax')
    avg.plot = ggplot(m.avg, aes(Selection_Strength,
                                 mean,
                                 group=interaction(variable, Selection_Strength, hypothesis),
                                 colour=interaction(hypothesis, variable))) +
      geom_point() + geom_errorbar(aes(ymax = ymax, ymin = ymin)) +
      labs(x=x.label,
           y="Average Correlation",
           color = "Module") +
      scale_colour_discrete(labels=c("Within Module 1",
                                     "Within Module 2",
                                     "Between Modules")) + theme_bw()
  }
  else{
    avg.full = avg[avg['hipothesis'] == "Full Integration",-3]
    avg.full = ddply(avg.full, .(Selection_Strength), 
                     function(x) {y <- x$Avg_Ratio 
                                  c(mean = mean(y), quantile(y, c(0.025, 0.975)))
                     })
    names(avg.full) = c('Selection_Strength', 'mean', 'ymin', 'ymax')
    avg.plot = ggplot(avg.full, aes(Selection_Strength,
                                    mean,
                                    group = Selection_Strength)) + 
      geom_point() + geom_errorbar(aes(ymax = ymax, ymin = ymin)) +
      labs(x="Peak Movement Rate", y="AVG Ratio") + theme_bw()
  }
  return(avg.plot)
}