inst/workflow/roc/code_plot_ps/u4-plot_aa_allinone.r

### This is for simulation only, ploting delta.t * phi aganst true values.

plot.aa.allinone <- function(
    i.aa, id.label, tl.codon,                  ### For AA.
    label.negsel.true,                         ### For label.
    b.Init, EPhi,                               ### For true.
    b.mcmc, t.phi.mcmc,                        ### For unscaled results.
    b.negsel.PM, phi.PM,                       ### For scaled results.
    workflow.name, i.case, model){
  ### Get right b for AA.
  b.AA.true <- b.Init[id.label]
  b.AA.PM <- matrix(b.mcmc[id.label,], nrow = tl.codon)
  b.AA.PM.scaled <- b.negsel.PM[id.label]

  ### Get true deltat * phi
  deltat.phi.true <- NULL
  for(i.codon in 1:tl.codon){
    deltat.phi.true <- cbind(deltat.phi.true,
                             b.AA.true[i.codon] * EPhi)
  }

  ### Get mean of unscaled deltat * phi
  deltat.phi.PM <- NULL
  for(i.codon in 1:tl.codon){
    deltat.phi.PM <- cbind(deltat.phi.PM,
                           colMeans(b.AA.PM[i.codon,] * t.phi.mcmc))
  }

  ### Get scaled deltat * scaled phi
  deltat.phi.PM.scaled <- NULL
  for(i.codon in 1:tl.codon){
    deltat.phi.PM.scaled <- cbind(deltat.phi.PM.scaled,
                                  b.AA.PM.scaled[i.codon] * phi.PM)
  }

  ### AA plot.
  # nf <- layout(matrix(1:3, nrow = 3), 1, c(1, 4, 4), respect = FALSE)

  ### Plot title.
  # par(mar = c(0, 0, 0, 0))
  # plot(NULL, NULL, xlim = c(0, 1), ylim = c(0, 1), axes = FALSE)
  # text(0.5, 0.5,
  #      paste(workflow.name, ", ", get.case.main(i.case, model), sep = ""))
  # par(mar = c(5.1, 4.1, 4.1, 2.1))

  ### Plot codon with large range first.
  diff.range <- apply(deltat.phi.true, 2, function(x){ abs(diff(range(x))) })
  order.plot <- order(diff.range, decreasing = TRUE)
  u.codon <- gsub(".\\.(.*)", "\\1", label.negsel.true[id.label])
  u.codon.sorted <- sort(u.codon)
  color <- cubfits:::get.color(u.codon.sorted)

  ### Plot top row.
  lim <- range(range(deltat.phi.true), range(deltat.phi.PM))
  plot(NULL, NULL,
       xlim = lim, ylim = lim,
       xlab = "true Delta.t * true Phi", ylab = "mean (Delta.t * Phi)",
       main = i.aa)
  for(i.codon in order.plot){
    points(deltat.phi.true[, i.codon], deltat.phi.PM[, i.codon],
           col = color[u.codon.sorted == u.codon[i.codon]],
           pch = 20, cex = 0.5)
  }
  abline(a = 0, b = 1, col = 4, lty = 2)
  # legend(lim[2] + (lim[2] - lim[1]) * (-0.3),
  #        lim[2] - (lim[2] - lim[1]) * 0.8,
  #        "1-to-1", col = 4, lty = 2)

  ### Plot bottom row.
  # lim <- range(range(deltat.phi.true), range(deltat.phi.PM.scaled))
  # plot(NULL, NULL,
  #      xlim = lim, ylim = lim,
  #      xlab = "true Delta.t * true Phi",
  #      ylab = "mean scaled Delta.t * mean scaled Phi",
  #      main = i.aa)
  # for(i.codon in order.plot){
  #   points(deltat.phi.true[, i.codon], deltat.phi.PM.scaled[, i.codon],
  #          col = color[u.codon.sorted == u.codon[i.codon]],
  #          pch = 20, cex = 0.5)
  # }
  # abline(a = 0, b = 1, col = 4, lty = 2)
  # legend(lim[2] + (lim[2] - lim[1]) * (-0.3),
  #        lim[2] - (lim[2] - lim[1]) * 0.8,
  #        "1-to-1", col = 4, lty = 2)

  invisible()
} # End of plot.aa.allinone().

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cubfits documentation built on May 2, 2019, 4:08 a.m.