1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16
17
18
19
20
21
22
23
24
25
26
27
28
29
30
31
32
33
34
35
36
37
38
39
40
41
42
43
44
45
46
47
48
49
50
51
52
53
54
55
56
57
58
59
60
61
62
63
64
65
66
67
68
69
70
71
72
73
74
75
76
77
78
79
80
81
82
83
84
85
86
87
88
89
90
91
92
93
94
95
96
97
98
99
100
101
102
103
104
105
106
107
108
109
110
111
112
113
114
115
116
117
118
119
120
121
122
123
124
125
126
127
128
129
130
131
132
133
134
135
136
137
138
139
140
141
142
143
144
145
146
147
148
149
150
151
152
153
154
155
156
157
158
159
160
161
162
163
164
165
166
167
168
169
170
171
172
173
174
175
176
177
178
179
180
181
182
183
184
185
186
187
188
189
190
191
192
193
194
195
196
197
198
199
200
201
202
203
204
205
|
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 rel.height.bottom The relative height of the bottom plot row
#' @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$data[c("name", "time", "value")])
#' names(ds) <- paste0("ds ", 6:10)
#' dfop_sfo <- mkinmod(parent = mkinsub("DFOP", "A1"),
#' A1 = mkinsub("SFO"), quiet = TRUE)
#' f <- mmkin(list("DFOP-SFO" = dfop_sfo), ds, quiet = TRUE, cores = 1)
#' plot(f[, 3:4], standardized = TRUE)
#' library(nlme)
#' # For this fit we need to increase pnlsMaxiter, and we increase the
#' # tolerance in order to speed up the fit for this example evaluation
#' f_nlme <- nlme(f, control = list(pnlsMaxIter = 120, tolerance = 1e-3))
#' plot(f_nlme)
#' @export
plot.nlme.mmkin <- function(x, i = 1:ncol(x$mmkin_orig),
main = NULL,
obs_vars = names(x$mkinmod$map),
standardized = TRUE,
xlab = "Time",
xlim = range(x$data$time),
legends = 1,
lpos = "topright", inset = c(0.05, 0.05),
resplot = c("predicted", "time"),
ymax = "auto", maxabs = "auto",
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$mmkin_orig[[1]]
ds_names <- colnames(x$mmkin_orig)
degparms_optim <- coefficients(x)
degparms_optim_names <- names(degparms_optim)
degparms_fixed <- fit_1$fixed$value
names(degparms_fixed) <- rownames(fit_1$fixed)
degparms_all <- cbind(as.matrix(degparms_optim),
matrix(rep(degparms_fixed, nrow(degparms_optim)),
ncol = length(degparms_fixed), byrow = TRUE))
degparms_all_names <- c(degparms_optim_names, names(degparms_fixed))
colnames(degparms_all) <- degparms_all_names
odeini_names <- grep("_0$", degparms_all_names, value = TRUE)
odeparms_names <- setdiff(degparms_all_names, odeini_names)
residual_type = ifelse(standardized, "pearson", "response")
observed <- cbind(x$data,
residual = residuals(x, type = residual_type))
n_plot_rows = length(obs_vars)
n_plots = n_plot_rows * 2
# Set relative plot heights, so the first and the last plot are the norm
# and the middle plots (if n_plot_rows >2) are smaller by rel.height.middle
rel.heights <- if (n_plot_rows > 1) c(rep(1, n_plot_rows - 1), rel.height.bottom) else 1
layout_matrix = matrix(1:n_plots,
n_plot_rows, 2, byrow = TRUE)
layout(layout_matrix, heights = rel.heights)
solution_type = fit_1$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$transform_fractions)
odeini <- degparms_all[ds_i, odeini_names]
names(odeini) <- gsub("_0", "", odeini_names)
out <- mkinpredict(x$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_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$transform_fractions)
odeini_pop <- degparms_all_pop[odeini_names]
names(odeini_pop) <- gsub("_0", "", odeini_names)
pred_pop <- as.data.frame(
mkinpredict(x$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 (plot_row %in% legends) {
legend(lpos, inset = inset,
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))
}
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")
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])
}
}
}
}
|
