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, 0 insertions, 294 deletions

diff --git a/R/plot_mixed.R b/R/plot_mixed.R
deleted file mode 100644
index 68404de4..00000000
--- a/R/plot_mixed.R
+++ /dev/null
@@ -1,294 +0,0 @@
-if(getRversion() >= '2.15.1') utils::globalVariables("ds")
-
-#' Plot predictions from a fitted nonlinear mixed model obtained via an mmkin row object
-#'
-#' @name plot_mixed
-#' @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 functions are called for their side effect.
-#' @author Johannes Ranke
-#' @examples
-#' ds <- lapply(experimental_data_for_UBA_2019[6:10],
-#'  function(x) x$data[c("name", "time", "value")])
-#' names(ds) <- paste0("ds ", 6:10)
-#' dfop_sfo <- mkinmod(parent = mkinsub("DFOP", "A1"),
-#'   A1 = mkinsub("SFO"), quiet = TRUE)
-#' \dontrun{
-#' f <- mmkin(list("DFOP-SFO" = dfop_sfo), ds, quiet = TRUE)
-#' plot(f[, 3:4], standardized = TRUE)
-#'
-#' library(nlme)
-#' # For this fit we need to increase pnlsMaxiter, and we increase the
-#' # tolerance in order to speed up the fit for this example evaluation
-#' f_nlme <- nlme(f, control = list(pnlsMaxIter = 120, tolerance = 1e-3))
-#' plot(f_nlme)
-#'
-#' f_saem <- saem(f)
-#' plot(f_saem)
-#' }
-#' @rdname plot_mixed
-#' @export
-plot.saem.mmkin <- function(x, i = 1:ncol(x$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, ...)
-{
-  fit_1 <- x$mmkin[[1]]
-  ds_names <- colnames(x$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$so@results@fixed.effects
-  names(degparms_pop) <- degparms_optim_names
-
-  .plot_mixed(x, i,
-    degparms_optim, degparms_pop, residuals,
-    obs_vars, standardized, xlab, xlim,
-    resplot, ymax, maxabs,
-    ncol.legend, nrow.legend,
-    rel.height.legend, rel.height.bottom,
-    pch_ds, col_ds, lty_ds, frame, ...)
-}
-
-#' @rdname plot_mixed
-#' @export
-plot.nlme.mmkin <- function(x, i = 1:ncol(x$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, ...)
-{
-  degparms_optim <- coefficients(x)
-  degparms_pop <- nlme::fixef(x)
-
-  residuals <- residuals(x,
-    type = ifelse(standardized, "pearson", "response"))
-
-  .plot_mixed(x, i,
-    degparms_optim, degparms_pop, residuals,
-    obs_vars, standardized, xlab, xlim,
-    resplot, ymax, maxabs,
-    ncol.legend, nrow.legend,
-    rel.height.legend, rel.height.bottom,
-    pch_ds, col_ds, lty_ds, frame, ...)
-}
-
-.plot_mixed <- function(x, i = 1:ncol(x$mmkin),
-  degparms_optim,
-  degparms_pop,
-  residuals,
-  obs_vars = names(x$mkinmod$map),
-  standardized = TRUE,
-  xlab = "Time",
-  xlim = range(x$data$time),
-  resplot = c("predicted", "time"),
-  ymax = "auto", maxabs = "auto",
-  ncol.legend = ifelse(length(i) <= 3, length(i) + 1, ifelse(length(i) <= 8, 3, 4)),
-  nrow.legend = ceiling((length(i) + 1) / ncol.legend),
-  rel.height.legend = 0.03 + 0.08 * nrow.legend,
-  rel.height.bottom = 1.1,
-  pch_ds = 1:length(i),
-  col_ds = pch_ds + 1,
-  lty_ds = col_ds,
-  frame = TRUE, ...)
-{
-
-  oldpar <- par(no.readonly = TRUE)
-
-  fit_1 <- x$mmkin[[1]]
-  ds_names <- colnames(x$mmkin)
-
-  degparms_fixed <- fit_1$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)
-
-  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))
-
-  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$rtol))
-
-  resplot <- match.arg(resplot)
-
-  # Loop plot rows
-  for (plot_row in 1:n_plot_rows) {
-
-    obs_var <- obs_vars[plot_row]
-    observed_row <- subset(observed, name == obs_var)
-
-    # Set ylim to sensible default, or use ymax
-    if (identical(ymax, "auto")) {
-      ylim_row = c(0,
-        max(c(observed_row$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])
-      }
-    }
-  }
-}