saemix.mmkin and nlme.mmkin inherit from mixed.mmkin

With a plot method. The class mixed.mmkin is currently only a virtual
class created to unify the plotting method.

1 files changed, 236 insertions, 0 deletions

diff --git a/R/plot.mixed.mmkin.R b/R/plot.mixed.mmkin.R
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+if(getRversion() >= '2.15.1') utils::globalVariables("ds")
+
+#' Plot predictions from a fitted nonlinear mixed model obtained via an mmkin row object
+#'
+#' @param x An object of class [saem.mmkin] or [nlme.mmkin]
+#' @param i A numeric index to select datasets for which to plot the individual predictions,
+#'   in case plots get too large
+#' @inheritParams plot.mkinfit
+#' @param standardized Should the residuals be standardized? Only takes effect if
+#'   `resplot = "time"`.
+#' @param rel.height.legend The relative height of the legend shown on top
+#' @param rel.height.bottom The relative height of the bottom plot row
+#' @param ymax Vector of maximum y axis values
+#' @param ncol.legend Number of columns to use in the legend
+#' @param nrow.legend Number of rows to use in the legend
+#' @param resplot Should the residuals plotted against time or against
+#'   predicted values?
+#' @param col_ds Colors used for plotting the observed data and the
+#'   corresponding model prediction lines for the different datasets.
+#' @param pch_ds Symbols to be used for plotting the data.
+#' @param lty_ds Line types to be used for the model predictions.
+#' @importFrom stats coefficients
+#' @return The function is called for its side effect.
+#' @author Johannes Ranke
+#' @examples
+#' ds <- lapply(experimental_data_for_UBA_2019[6:10],
+#'  function(x) x$data[c("name", "time", "value")])
+#' names(ds) <- paste0("ds ", 6:10)
+#' dfop_sfo <- mkinmod(parent = mkinsub("DFOP", "A1"),
+#'   A1 = mkinsub("SFO"), quiet = TRUE)
+#' \dontrun{
+#' f <- mmkin(list("DFOP-SFO" = dfop_sfo), ds, quiet = TRUE)
+#' plot(f[, 3:4], standardized = TRUE)
+#'
+#' # For this fit we need to increase pnlsMaxiter, and we increase the
+#' # tolerance in order to speed up the fit for this example evaluation
+#' f_nlme <- nlme(f, control = list(pnlsMaxIter = 120, tolerance = 1e-3))
+#' plot(f_nlme)
+#'
+#' f_saem <- saem(f)
+#' plot(f_saem)
+#' }
+#' @export
+plot.mixed.mmkin <- function(x,
+  i = 1:ncol(x$mmkin),
+  obs_vars = names(x$mkinmod$map),
+  standardized = TRUE,
+  xlab = "Time",
+  xlim = range(x$data$time),
+  resplot = c("predicted", "time"),
+  ymax = "auto", maxabs = "auto",
+  ncol.legend = ifelse(length(i) <= 3, length(i) + 1, ifelse(length(i) <= 8, 3, 4)),
+  nrow.legend = ceiling((length(i) + 1) / ncol.legend),
+  rel.height.legend = 0.03 + 0.08 * nrow.legend,
+  rel.height.bottom = 1.1,
+  pch_ds = 1:length(i),
+  col_ds = pch_ds + 1,
+  lty_ds = col_ds,
+  frame = TRUE, ...
+)
+{
+  # Prepare parameters and data
+  fit_1 <- x$mmkin[[1]]
+  ds_names <- colnames(x$mmkin)
+
+  if (inherits(x, "nlme.mmkin")) {
+    degparms_optim <- coefficients(x)
+    degparms_pop <- nlme::fixef(x)
+    residuals <- residuals(x,
+      type = ifelse(standardized, "pearson", "response"))
+  }
+
+  if (inherits(x, "saem.mmkin")) {
+    degparms_optim <- saemix::psi(x$so)
+    rownames(degparms_optim) <- ds_names
+    degparms_optim_names <- setdiff(names(fit_1$par), names(fit_1$errparms))
+    colnames(degparms_optim) <- degparms_optim_names
+    residual_type = ifelse(standardized, "iwres", "ires")
+    residuals <- x$so@results@predictions[[residual_type]]
+    degparms_pop <- x$so@results@fixed.effects
+    names(degparms_pop) <- degparms_optim_names
+  }
+
+  degparms_fixed <- fit_1$fixed$value
+  names(degparms_fixed) <- rownames(fit_1$fixed)
+  degparms_all <- cbind(as.matrix(degparms_optim),
+    matrix(rep(degparms_fixed, nrow(degparms_optim)),
+      ncol = length(degparms_fixed),
+      nrow = nrow(degparms_optim), byrow = TRUE))
+  degparms_all_names <- c(names(degparms_optim), names(degparms_fixed))
+  colnames(degparms_all) <- degparms_all_names
+
+  degparms_all_pop <- c(degparms_pop, degparms_fixed)
+
+  odeini_names <- grep("_0$", degparms_all_names, value = TRUE)
+  odeparms_names <- setdiff(degparms_all_names, odeini_names)
+
+  residual_type = ifelse(standardized, "iwres", "ires")
+
+  observed <- cbind(x$data,
+    residual = residuals)
+
+  solution_type = fit_1$solution_type
+
+  outtimes <- sort(unique(c(x$data$time,
+    seq(xlim[1], xlim[2], length.out = 50))))
+
+  pred_ds <- purrr::map_dfr(i, function(ds_i)   {
+    odeparms_trans <- degparms_all[ds_i, odeparms_names]
+    names(odeparms_trans) <- odeparms_names # needed if only one odeparm
+    odeparms <- backtransform_odeparms(odeparms_trans,
+      x$mkinmod,
+      transform_rates = fit_1$transform_rates,
+      transform_fractions = fit_1$transform_fractions)
+
+    odeini <- degparms_all[ds_i, odeini_names]
+    names(odeini) <- gsub("_0", "", odeini_names)
+
+    out <- mkinpredict(x$mkinmod, odeparms, odeini,
+      outtimes, solution_type = solution_type,
+      atol = fit_1$atol, rtol = fit_1$rtol)
+    return(cbind(as.data.frame(out), ds = ds_names[ds_i]))
+  })
+
+  odeparms_pop_trans <- degparms_all_pop[odeparms_names]
+  odeparms_pop <- backtransform_odeparms(odeparms_pop_trans,
+    x$mkinmod,
+    transform_rates = fit_1$transform_rates,
+    transform_fractions = fit_1$transform_fractions)
+
+  odeini_pop <- degparms_all_pop[odeini_names]
+  names(odeini_pop) <- gsub("_0", "", odeini_names)
+
+  pred_pop <- as.data.frame(
+    mkinpredict(x$mkinmod, odeparms_pop, odeini_pop,
+      outtimes, solution_type = solution_type,
+      atol = fit_1$atol, rtol = fit_1$rtol))
+
+  # Start of graphical section
+  oldpar <- par(no.readonly = TRUE)
+
+  n_plot_rows = length(obs_vars)
+  n_plots = n_plot_rows * 2
+
+  # Set relative plot heights, so the first plot row is the norm
+  rel.heights <- if (n_plot_rows > 1) {
+    c(rel.height.legend, c(rep(1, n_plot_rows - 1), rel.height.bottom))
+  } else {
+    c(rel.height.legend, 1)
+  }
+
+  layout_matrix = matrix(c(1, 1, 2:(n_plots + 1)),
+    n_plot_rows + 1, 2, byrow = TRUE)
+  layout(layout_matrix, heights = rel.heights)
+
+  par(mar = c(0.1, 2.1, 0.6, 2.1))
+
+  plot(0, type = "n", axes = FALSE, ann = FALSE)
+  legend("center", bty = "n", ncol = ncol.legend,
+    legend = c("Population", ds_names[i]),
+    lty = c(1, lty_ds), lwd = c(2, rep(1, length(i))),
+    col = c(1, col_ds),
+    pch = c(NA, pch_ds))
+
+  resplot <- match.arg(resplot)
+
+  # Loop plot rows
+  for (plot_row in 1:n_plot_rows) {
+
+    obs_var <- obs_vars[plot_row]
+    observed_row <- subset(observed, name == obs_var)
+
+    # Set ylim to sensible default, or use ymax
+    if (identical(ymax, "auto")) {
+      ylim_row = c(0,
+        max(c(observed_row$value, pred_ds[[obs_var]]), na.rm = TRUE))
+    } else {
+      ylim_row = c(0, ymax[plot_row])
+    }
+
+    # Margins for bottom row of plots when we have more than one row
+    # This is the only row that needs to show the x axis legend
+    if (plot_row == n_plot_rows) {
+      par(mar = c(5.1, 4.1, 2.1, 2.1))
+    } else {
+      par(mar = c(3.0, 4.1, 2.1, 2.1))
+    }
+
+    plot(pred_pop$time, pred_pop[[obs_var]],
+      type = "l", lwd = 2,
+      xlim = xlim, ylim = ylim_row,
+      xlab = xlab, ylab = obs_var, frame = frame)
+
+    for (ds_i in seq_along(i)) {
+      points(subset(observed_row, ds == ds_names[ds_i], c("time", "value")),
+        col = col_ds[ds_i], pch = pch_ds[ds_i])
+      lines(subset(pred_ds, ds == ds_names[ds_i], c("time", obs_var)),
+        col = col_ds[ds_i], lty = lty_ds[ds_i])
+    }
+
+    if (identical(maxabs, "auto")) {
+      maxabs = max(abs(observed_row$residual), na.rm = TRUE)
+    }
+
+    if (identical(resplot, "time")) {
+      plot(0, type = "n", xlim = xlim, xlab = "Time",
+        ylim = c(-1.2 * maxabs, 1.2 * maxabs),
+        ylab = if (standardized) "Standardized residual" else "Residual")
+
+      abline(h = 0, lty = 2)
+
+      for (ds_i in seq_along(i)) {
+        points(subset(observed_row, ds == ds_names[ds_i], c("time", "residual")),
+          col = col_ds[ds_i], pch = pch_ds[ds_i])
+      }
+    }
+
+    if (identical(resplot, "predicted")) {
+      plot(0, type = "n",
+        xlim = c(0, max(pred_ds[[obs_var]])),
+        xlab = "Predicted",
+        ylim = c(-1.2 * maxabs, 1.2 * maxabs),
+        ylab = if (standardized) "Standardized residual" else "Residual")
+
+      abline(h = 0, lty = 2)
+
+      for (ds_i in seq_along(i)) {
+        observed_row_ds <- merge(
+          subset(observed_row, ds == ds_names[ds_i], c("time", "residual")),
+          subset(pred_ds, ds == ds_names[ds_i], c("time", obs_var)))
+        points(observed_row_ds[c(3, 2)],
+          col = col_ds[ds_i], pch = pch_ds[ds_i])
+      }
+    }
+  }
+}