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Diffstat (limited to 'R/plot.saem.mmkin.R')
| -rw-r--r-- | R/plot.saem.mmkin.R | 218 |
1 files changed, 0 insertions, 218 deletions
diff --git a/R/plot.saem.mmkin.R b/R/plot.saem.mmkin.R deleted file mode 100644 index ce43fdb6..00000000 --- a/R/plot.saem.mmkin.R +++ /dev/null @@ -1,218 +0,0 @@ -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]) - } - } - } -} |