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kinresults <- function(kinfits, alpha = 0.05, SFORB=TRUE)
{
kindata <- data.frame(t = kinfits[[1]]$model$t, parent = kinfits[[1]]$model$parent)
kindata.means <- aggregate(kindata, list(kindata$t), mean)
kindata.means.mean <- mean(kindata.means$parent, na.rm=TRUE)
n.times <- length(kindata.means$parent)
parms <- list()
df <- err.min <- RSS <- vector()
DT50 <- DT90 <- vector()
f <- list()
for (kinmodel in names(kinfits))
{
m = kinfits[[kinmodel]]
if(class(m) == "nls") {
kindata.means$est <- predict(m, kindata.means)
parms[[kinmodel]] <- switch(kinmodel,
SFO = list(parent.0 = coef(m)[["parent.0"]],
k = coef(m)[["k"]]),
FOMC = list(parent.0 = coef(m)[["parent.0"]],
alpha = coef(m)[["alpha"]],
beta = coef(m)[["beta"]]),
HS = list(parent.0 = coef(m)[["parent.0"]],
k1 = coef(m)[["k1"]],
k2 = coef(m)[["k2"]],
tb = coef(m)[["tb"]]),
DFOP = list(parent.0 = coef(m)[["parent.0"]],
k1 = coef(m)[["k1"]],
k2 = coef(m)[["k2"]],
g = coef(m)[["g"]]))
if(kinmodel == "DFOP" & SFORB) {
k1 = coef(m)[["k1"]]
k2 = coef(m)[["k2"]]
g = coef(m)[["g"]]
parms[["SFORB"]] =
list(parent.0 = coef(m)[["parent.0"]],
k1out = g * k1 + (1 - g) * k2,
k21 = k1 * k2 / (g * k1 + (1 - g) * k2),
k12 = (g * (1 - g) * (k1 - k2)^2) / (g * k1 + (1 - g) * k2))
}
n.parms = length(coef(m))
f[[kinmodel]] = switch(kinmodel,
HS = function(t, x) {
(HS(t, coef(m)[["parent.0"]],
coef(m)[["k1"]], coef(m)[["k2"]], coef(m)[["tb"]]) -
(1 - x/100) * coef(m)[["parent.0"]])^2
},
DFOP = function(t, x) {
(DFOP(t, coef(m)[["parent.0"]],
coef(m)[["k1"]], coef(m)[["k2"]], coef(m)[["g"]]) -
(1 - x/100) * coef(m)[["parent.0"]])^2
}
)
coef(m)
df[[kinmodel]] = n.times - n.parms
RSS[[kinmodel]] = sum(summary(m)$residuals^2)
DT50[[kinmodel]] = switch(kinmodel,
SFO = log(2)/coef(m)[["k"]],
FOMC = coef(m)[["beta"]] * (2^(1/coef(m)[["alpha"]]) - 1),
HS = optimize(f[[kinmodel]], c(0, max(kindata$t)), x=50)$minimum,
DFOP = optimize(f[[kinmodel]], c(0, max(kindata$t)), x=50)$minimum)
DT90[[kinmodel]] = switch(kinmodel,
SFO = log(10)/coef(m)[["k"]],
FOMC = coef(m)[["beta"]] * (10^(1/coef(m)[["alpha"]]) - 1),
HS = optimize(f[[kinmodel]], c(0, max(kindata$t)), x=90)$minimum,
DFOP = optimize(f[[kinmodel]], c(0, max(kindata$t)), x=90)$minimum)
err.min[[kinmodel]] <- kinerrmin(kinfits, kinmodel)
}
}
stats <- data.frame(n.times = n.times, df = df, mean.means = kindata.means.mean,
RSS = RSS, err.min = err.min)
results <- data.frame(DT50 = DT50, DT90 = DT90)
list(parms = parms, stats = stats, results = results)
}
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