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mkinplot <- function(fit, xlab = "Time", ylab = "Observed", xlim = range(fit$data$time), ylim = range(fit$data$observed, na.rm = TRUE), ...)
{
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$fixed, type == "deparm")))
odeparms <- parms.all[odenames]
# Solve the ode
out <- ode(
y = odeini,
times = outtimes,
func = fit$mkindiff,
parms = odeparms)
# 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])
legend("topright", inset=c(0.05, 0.05), legend=names(fit$map),
col=col_obs, pch=pch_obs, lty=1:length(pch_obs))
}
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