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-rw-r--r--R/mkinfit.R15
-rw-r--r--R/mkinplot.R82
2 files changed, 17 insertions, 80 deletions
diff --git a/R/mkinfit.R b/R/mkinfit.R
index 6e455e1..cb0396f 100644
--- a/R/mkinfit.R
+++ b/R/mkinfit.R
@@ -170,13 +170,11 @@ mkinfit <- function(mkinmod, observed,
# We need to return some more data for summary and plotting
fit$solution_type <- solution_type
- if (solution_type == "eigen") {
- fit$coefmat <- mkinmod$coefmat
- }
- # We also need various other information for summary and plotting
- fit$map <- mkinmod$map
- fit$diffs <- mkinmod$diffs
+ # We also need the model for summary and plotting
+ fit$mkinmod <- mkinmod
+
+ # We need data and predictions for summary and plotting
fit$observed <- mkin_long_to_wide(observed)
predicted_long <- mkin_wide_to_long(out_predicted, time = "time")
fit$predicted <- out_predicted
@@ -348,6 +346,7 @@ summary.mkinfit <- function(object, data = TRUE, distimes = TRUE, ...) {
Rversion = paste(R.version$major, R.version$minor, sep="."),
date.fit = object$date,
date.summary = date(),
+ use_of_ff = object$mkinmod$use_of_ff,
residuals = object$residuals,
residualVariance = resvar,
sigma = sqrt(resvar),
@@ -358,7 +357,7 @@ summary.mkinfit <- function(object, data = TRUE, distimes = TRUE, ...) {
stopmess = message,
par = param)
- ans$diffs <- object$diffs
+ ans$diffs <- object$mkinmod$diffs
if(data) ans$data <- object$data
ans$start <- object$start
@@ -411,7 +410,7 @@ print.summary.mkinfit <- function(x, digits = max(3, getOption("digits") - 3), .
}
printff <- !is.null(x$ff)
- if(printff){
+ if(printff & x$use_of_ff == "min"){
cat("\nEstimated formation fractions:\n")
print(data.frame(ff = x$ff), digits=digits,...)
}
diff --git a/R/mkinplot.R b/R/mkinplot.R
index d665bc2..789a6f9 100644
--- a/R/mkinplot.R
+++ b/R/mkinplot.R
@@ -1,14 +1,14 @@
mkinplot <- function(fit, xlab = "Time", ylab = "Observed", xlim = range(fit$data$time), ylim = range(fit$data$observed, na.rm = TRUE), legend = TRUE, ...)
{
- solution = fit$solution
+ solution_type = fit$solution_type
fixed <- fit$fixed$value
names(fixed) <- rownames(fit$fixed)
- parms.all <- c(fit$par, fixed)
+ parms.all <- c(fit$parms.all, fixed)
ininames <- c(
rownames(subset(fit$start, type == "state")),
rownames(subset(fit$fixed, type == "state")))
odeini <- parms.all[ininames]
- names(odeini) <- names(fit$diffs)
+ names(odeini) <- names(fit$mkinmod$diffs)
outtimes <- seq(xlim[1], xlim[2], length.out=100)
@@ -17,84 +17,22 @@ mkinplot <- function(fit, xlab = "Time", ylab = "Observed", xlim = range(fit$dat
rownames(subset(fit$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$map[[var]]])
- }
- }
+ out <- mkinpredict(fit$mkinmod, odeparms, odeini, outtimes,
+ solution_type = solution_type, ...)
# 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$map)) {
+ col_obs <- pch_obs <- 1:length(fit$mkinmod$map)
+ names(col_obs) <- names(pch_obs) <- names(fit$mkinmod$map)
+ for (obs_var in names(fit$mkinmod$map)) {
points(subset(fit$data, variable == obs_var, c(time, observed)),
pch = pch_obs[obs_var], col = col_obs[obs_var])
}
- matlines(out_transformed$time, out_transformed[-1])
+ matlines(out$time, out[-1])
if (legend == TRUE) {
- legend("topright", inset=c(0.05, 0.05), legend=names(fit$map),
+ legend("topright", inset=c(0.05, 0.05), legend=names(fit$mkinmod$map),
col=col_obs, pch=pch_obs, lty=1:length(pch_obs))
}
}

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