From 6464d3999338d34c081f360694dbc0bc0abf68cb Mon Sep 17 00:00:00 2001 From: Johannes Ranke Date: Mon, 9 Nov 2020 14:23:16 +0100 Subject: Add plot method for saem.mmkin objects --- R/plot.saem.mmkin.R | 218 ++++++++++++++++++++++++++++++++++++++++++++++++++++ 1 file changed, 218 insertions(+) create mode 100644 R/plot.saem.mmkin.R (limited to 'R/plot.saem.mmkin.R') diff --git a/R/plot.saem.mmkin.R b/R/plot.saem.mmkin.R new file mode 100644 index 00000000..ce43fdb6 --- /dev/null +++ b/R/plot.saem.mmkin.R @@ -0,0 +1,218 @@ +if(getRversion() >= '2.15.1') utils::globalVariables("ds") + +#' Plot an saem fitted nonlinear mixed model obtained via an mmkin row object +#' +#' @param x An object of class \code{\link{saem.mmkin}} +#' @param i A numeric index to select datasets for which to plot the saem fit, +#' 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 saemix psi +#' @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$data[c("name", "time", "value")]) +#' names(ds) <- paste0("ds ", 6:10) +#' dfop_sfo <- mkinmod(parent = mkinsub("DFOP", "A1"), +#' A1 = mkinsub("SFO"), quiet = TRUE) +#' \dontrun{ +#' f <- mmkin(list("DFOP-SFO" = dfop_sfo), ds, quiet = TRUE) +#' plot(f[, 3:4], standardized = TRUE) +#' +#' f_saem <- saem(f) +#' plot(f_saem) +#' } +#' @export +plot.saem.mmkin <- function(x, i = 1:ncol(x$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, ...) +{ + + oldpar <- par(no.readonly = TRUE) + + fit_1 <- x$mmkin[[1]] + ds_names <- colnames(x$mmkin) + + degparms_optim <- psi(x$so) + rownames(degparms_optim) <- ds_names + degparms_optim_names <- setdiff(names(fit_1$par), names(fit_1$errparms)) + colnames(degparms_optim) <- degparms_optim_names + + degparms_fixed <- fit_1$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(degparms_optim_names, names(degparms_fixed)) + colnames(degparms_all) <- degparms_all_names + + 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 = x$so@results@predictions[[residual_type]]) + + n_plot_rows = length(obs_vars) + n_plots = n_plot_rows * 2 + + # Set relative plot heights, so the first plot row is the norm + rel.heights <- if (n_plot_rows > 1) { + c(rel.height.legend, c(rep(1, n_plot_rows - 1), rel.height.bottom)) + } else { + c(rel.height.legend, 1) + } + + layout_matrix = matrix(c(1, 1, 2:(n_plots + 1)), + n_plot_rows + 1, 2, byrow = TRUE) + layout(layout_matrix, heights = rel.heights) + + par(mar = c(0.1, 2.1, 0.6, 2.1)) + + plot(0, type = "n", axes = FALSE, ann = FALSE) + legend("center", bty = "n", ncol = ncol.legend, + legend = c("Population", ds_names[i]), + lty = c(1, lty_ds), lwd = c(2, rep(1, length(i))), + col = c(1, col_ds), + pch = c(NA, pch_ds)) + + + solution_type = fit_1$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])) + }) + + degparms_pop <- x$so@results@fixed.effects + names(degparms_pop) <- degparms_optim_names + degparms_all_pop <- c(degparms_pop, degparms_fixed) + + odeparms_pop_trans <- degparms_all_pop[odeparms_names] + odeparms_pop <- backtransform_odeparms(odeparms_pop_trans, + x$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]) + } + } + } +} -- cgit v1.2.1