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if(getRversion() >= '2.15.1') utils::globalVariables("ds")
#' Plot a fitted nonlinear mixed model obtained via an mmkin row object
#'
#' @param x An object of class \code{\link{nlme.mmkin}}
#' @param i A numeric index to select datasets for which to plot the nlme fit,
#' in case plots get too large
#' @param main The main title placed on the outer margin of the plot.
#' @inheritParams plot.mkinfit
#' @param legends An index for the fits for which legends should be shown.
#' @param standardized Should the residuals be standardized? Only takes effect if
#' `resplot = "time"`.
#' @param cex Passed to the plot functions and \code{\link{mtext}}.
#' @param rel.height.middle The relative height of the middle plot, if more
#' than two rows of plots are shown.
#' @param ymax Vector of maximum y axis values
#' @param \dots Further arguments passed to \code{\link{plot.mkinfit}} and
#' \code{\link{mkinresplot}}.
#' @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$data[c("name", "time", "value")])
#' names(ds) <- paste0("ds ", 6:10)
#' dfop_sfo <- mkinmod(parent = mkinsub("DFOP", "A1"),
#' A1 = mkinsub("SFO"), quiet = TRUE)
#' f <- mmkin(list("DFOP-SFO" = dfop_sfo), ds, quiet = TRUE, cores = 1)
#' plot(f[, 3:4], standardized = TRUE)
#' library(nlme)
#' # For this fit we need to increase pnlsMaxiter, and we increase the
#' # tolerance in order to speed up the fit for this example evaluation
#' f_nlme <- nlme(f, control = list(pnlsMaxIter = 120, tolerance = 1e-4))
#' plot(f_nlme)
#' @export
plot.nlme.mmkin <- function(x, i = 1:ncol(x$mmkin_orig),
main = rownames(x$mmkin_orig),
obs_vars = names(x$mkinmod$map),
standardized = TRUE,
xlab = "Time", ylab = "Observed",
xlim = range(x$data$time),
legends = 1,
lpos = "topright", inset = c(0.05, 0.05),
resplot = c("predicted", "time"),
ymax = "auto", maxabs = "auto",
cex = 0.7, rel.height.middle = 0.9,
pch_ds = 1:length(i),
col_ds = pch_ds + 1,
lty_ds = col_ds,
frame = TRUE, ...)
{
oldpar <- par(no.readonly = TRUE)
fit_1 = x$mmkin_orig[[1]]
ds_names <- colnames(x$mmkin_orig)
degparms_optim <- coefficients(x)
degparms_optim_names <- names(degparms_optim)
degparms_fixed <- fit_1$fixed$value
names(degparms_fixed) <- rownames(fit_1$fixed)
degparms_all <- cbind(as.matrix(degparms_optim), fit_1$bparms.fixed)
degparms_all_names <- c(degparms_optim_names, names(degparms_fixed))
colnames(degparms_all) <- degparms_all_names
odeini_names <- grep("_0$", degparms_all_names, value = TRUE)
odeparms_names <- setdiff(degparms_all_names, odeini_names)
residual_type = ifelse(standardized, "pearson", "response")
observed <- cbind(x$data,
residual = residuals(x, type = residual_type))
n_plot_rows = length(obs_vars)
n_plots = n_plot_rows * 2
# Set relative plot heights, so the first and the last plot are the norm
# and the middle plots (if n_plot_rows >2) are smaller by rel.height.middle
rel.heights <- if (n_plot_rows > 2) {
c(1, rep(rel.height.middle, n_plot_rows - 2), 1)
} else {
rep(1, n_plot_rows)
}
layout_matrix = matrix(1:n_plots,
n_plot_rows, 2, byrow = TRUE)
layout(layout_matrix, heights = rel.heights)
solution_type = fit_1$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]
odeparms <- backtransform_odeparms(odeparms_trans,
x$mkinmod,
transform_rates = fit_1$transform_rates,
transform_fractions = fit_1$transform_fractions)
odeini <- degparms_all[ds_i, odeini_names]
names(odeini) <- gsub("_0", "", names(odeini))
out <- mkinpredict(x$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_all_pop <- c(fixef(x), 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$transform_fractions)
odeini_pop <- degparms_all_pop[odeini_names]
names(odeini_pop) <- gsub("_0", "", names(odeini_pop))
pred_pop <- as.data.frame(
mkinpredict(x$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$value, pred_ds[[obs_var]]), na.rm = TRUE))
} else {
ylim_row = c(0, ymax[plot_row])
}
# Margins for top row of plots when we have more than one row
# Reduce bottom margin by 2.1 - hides x axis legend
if (plot_row == 1 & n_plot_rows > 1) {
par(mar = c(3.0, 4.1, 4.1, 2.1))
}
# Margins for middle rows of plots, if any
if (plot_row > 1 & plot_row < n_plot_rows) {
# Reduce top margin by 2 after the first plot as we have no main title,
# reduced plot height, therefore we need rel.height.middle in the layout
par(mar = c(3.0, 4.1, 2.1, 2.1))
}
# Margins for bottom row of plots when we have more than one row
if (plot_row == n_plot_rows & n_plot_rows > 1) {
# Restore bottom margin for last plot to show x axis legend
par(mar = c(5.1, 4.1, 2.1, 2.1))
}
plot(pred_pop$time, pred_pop[[obs_var]],
main = obs_var,
type = "l", lwd = 2,
xlim = xlim, ylim = ylim_row,
xlab = xlab, ylab = ylab, frame = frame,
cex = cex)
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 (plot_row %in% legends) {
legend(lpos, inset = inset,
legend = c("Population mean", 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))
}
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",
main = obs_var,
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",
main = obs_var,
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")
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])
}
}
}
}
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