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kinfit <- function(kindata, kinmodels = c("SFO"),
parent.0.user = NA,
start.SFO = list(parent.0 = NA, k = NA),
start.FOMC = list(parent.0 = NA, alpha = NA, beta = NA),
start.DFOP = list(parent.0 = NA, k1 = NA, k2 = NA, g = NA),
start.HS = list(parent.0 = NA, k1 = NA, k2 = NA, tb = NA),
algorithm = "port")
{
kindata <- subset(kindata, !is.na(kindata$parent))
kinfits <- list()
if (!is.na(parent.0.user)) {
start.SFO$parent.0 = parent.0.user
start.FOMC$parent.0 = parent.0.user
}
lmlogged = lm(log(parent) ~ t, data = kindata)
for (kinmodel in kinmodels)
{
if (kinmodel == "SFO") {
if (is.na(start.SFO$parent.0)) {
start.SFO$parent.0 = max(kindata$parent)
}
if (is.na(start.SFO$k)) {
start.SFO$k = - coef(lmlogged)[["t"]]
}
kinfits[[kinmodel]] = try(
nls(parent ~ SFO(t, parent.0, k),
data = kindata, model = TRUE,
start = start.SFO,
algorithm = algorithm), silent=TRUE)
}
k.est = ifelse(is.na(coef(kinfits$SFO)[["k"]]),
-coef(lmlogged)[["t"]],
coef(kinfits$SFO)[["k"]])
if (kinmodel == "FOMC") {
if (is.na(start.FOMC$parent.0)) {
start.FOMC$parent.0 = max(kindata$parent)
}
if (is.na(start.FOMC$alpha)) {
start.FOMC$alpha = 1
}
if (is.na(start.FOMC$beta)) {
start.FOMC$beta = start.FOMC$alpha / k.est
}
kinfits[[kinmodel]] = try(
nls(parent ~ FOMC(t, parent.0, alpha, beta),
data = kindata, model = TRUE,
start = start.FOMC,
algorithm = algorithm), silent=TRUE)
}
if (kinmodel == "DFOP") {
if (is.na(start.DFOP$parent.0)) {
start.DFOP$parent.0 = max(kindata$parent)
}
if (is.na(start.DFOP$k1)) {
start.DFOP$k1 = k.est * 2
}
if (is.na(start.DFOP$k2)) {
start.DFOP$k2 = k.est / 2
}
if (is.na(start.DFOP$g)) {
start.DFOP$g = 0.5
}
kinfits[[kinmodel]] = try(
nls(parent ~ DFOP(t, parent.0, k1, k2, g),
data = kindata, model = TRUE,
start = start.DFOP,
algorithm = algorithm), silent=TRUE)
}
if (kinmodel == "HS") {
if (is.na(start.HS$parent.0)) {
start.HS$parent.0 = max(kindata$parent)
}
if (is.na(start.HS$k1)) {
start.HS$k1 = k.est
}
if (is.na(start.HS$k2)) {
start.HS$k2 = k.est / 10
}
if (is.na(start.HS$tb)) {
start.HS$tb = 0.05 * max(kindata$t)
}
kinfits[[kinmodel]] = try(
nls(parent ~ HS(t, parent.0, k1, k2, tb),
data = kindata, model = TRUE,
start = start.HS,
algorithm = algorithm), silent=TRUE)
}
}
return(kinfits)
}
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