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
|
mkinplot <- function(fit, xlab = "Time", ylab = "Observed", xlim = range(fit$data$time), ylim = range(fit$data$observed, na.rm = TRUE), legend = TRUE, ...)
{
solution = fit$solution
fixed <- fit$fixed$value
names(fixed) <- rownames(fit$fixed)
parms.all <- c(fit$par, fixed)
ininames <- c(
rownames(subset(fit$start, type == "state")),
rownames(subset(fit$fixed, type == "state")))
odeini <- parms.all[ininames]
names(odeini) <- names(fit$diffs)
outtimes <- seq(xlim[1], xlim[2], length.out=100)
odenames <- c(
rownames(subset(fit$start, type == "deparm")),
rownames(subset(fit$fixed, type == "deparm")))
odeparms <- parms.all[odenames]
# Solve the system
evalparse <- function(string)
{
eval(parse(text=string), as.list(c(odeparms, odeini)))
}
if (solution == "analytical") {
parent.type = names(fit$map[[1]])[1]
parent.name = names(fit$diffs)[[1]]
o <- switch(parent.type,
SFO = SFO.solution(outtimes,
evalparse(parent.name),
evalparse(paste("k", parent.name, "sink", sep="_"))),
FOMC = FOMC.solution(outtimes,
evalparse(parent.name),
evalparse("alpha"), evalparse("beta")),
DFOP = DFOP.solution(outtimes,
evalparse(parent.name),
evalparse("k1"), evalparse("k2"),
evalparse("g")),
HS = HS.solution(outtimes,
evalparse(parent.name),
evalparse("k1"), evalparse("k2"),
evalparse("tb")),
SFORB = SFORB.solution(outtimes,
evalparse(parent.name),
evalparse(paste("k", parent.name, "free_bound", sep="_")),
evalparse(paste("k", parent.name, "bound_free", sep="_")),
evalparse(paste("k", parent.name, "free_sink", sep="_")))
)
out <- cbind(outtimes, o)
dimnames(out) <- list(outtimes, c("time", parent.name))
}
if (solution == "eigen") {
coefmat.num <- matrix(sapply(as.vector(fit$coefmat), evalparse),
nrow = length(odeini))
e <- eigen(coefmat.num)
c <- solve(e$vectors, odeini)
f.out <- function(t) {
e$vectors %*% diag(exp(e$values * t), nrow=length(odeini)) %*% c
}
o <- matrix(mapply(f.out, outtimes),
nrow = length(odeini), ncol = length(outtimes))
dimnames(o) <- list(names(odeini), NULL)
out <- cbind(time = outtimes, t(o))
}
if (solution == "deSolve") {
out <- ode(
y = odeini,
times = outtimes,
func = fit$mkindiff,
parms = odeparms,
atol = fit$atol
)
}
# Output transformation for models with unobserved compartments like SFORB
out_transformed <- data.frame(time = out[,"time"])
for (var in names(fit$map)) {
if(length(fit$map[[var]]) == 1) {
out_transformed[var] <- out[, var]
} else {
out_transformed[var] <- rowSums(out[, fit$map[[var]]])
}
}
# Plot the data and model output
plot(0, type="n",
xlim = xlim, ylim = ylim,
xlab = xlab, ylab = ylab, ...)
col_obs <- pch_obs <- 1:length(fit$map)
names(col_obs) <- names(pch_obs) <- names(fit$map)
for (obs_var in names(fit$map)) {
points(subset(fit$data, variable == obs_var, c(time, observed)),
pch = pch_obs[obs_var], col = col_obs[obs_var])
}
matlines(out_transformed$time, out_transformed[-1])
if (legend == TRUE) {
legend("topright", inset=c(0.05, 0.05), legend=names(fit$map),
col=col_obs, pch=pch_obs, lty=1:length(pch_obs))
}
}
|
