# $Id: kinfit.R 116 2011-06-14 08:46:47Z kati $
# Copyright (C) 2008-2010 Johannes Ranke
# Contact: mkin-devel@lists.berlios.de
# This file is part of the R package kinfit
# kinfit is free software: you can redistribute it and/or modify it under the
# terms of the GNU General Public License as published by the Free Software
# Foundation, either version 3 of the License, or (at your option) any later
# version.
# This program is distributed in the hope that it will be useful, but WITHOUT
# ANY WARRANTY; without even the implied warranty of MERCHANTABILITY or FITNESS
# FOR A PARTICULAR PURPOSE. See the GNU General Public License for more
# details.
# You should have received a copy of the GNU General Public License along with
# this program. If not, see <http://www.gnu.org/licenses/>
kinfit <- function(kindata, kinmodels = c("SFO"),
parent.0.user = NA,
parent.0.fixed = FALSE,
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 = "default")
{
kindata <- subset(kindata, !is.na(kindata$parent))
kinfits <- list()
if (!is.na(parent.0.user)) {
start.SFO$parent.0 = parent.0.user
start.FOMC$parent.0 = parent.0.user
start.DFOP$parent.0 = parent.0.user
start.HS$parent.0 = parent.0.user
}
lmlogged = lm(log(parent) ~ t, data = kindata)
k.est = -coef(lmlogged)[["t"]]
for (kinmodel in kinmodels)
{
if (kinmodel == "SFO") {
if (is.na(start.SFO$parent.0)) {
start.SFO$parent.0 = max(kindata$parent)
}
if (is.na(start.SFO$k)) {
start.SFO$k = - coef(lmlogged)[["t"]]
}
if (parent.0.fixed)
{
start.SFO = start.SFO[-1]
kinfits[[kinmodel]] = try(
nls(parent ~ SFO(t, parent.0.user, k),
data = kindata, model = TRUE,
start = start.SFO,
algorithm = algorithm), silent=TRUE)
} else {
kinfits[[kinmodel]] = try(
nls(parent ~ SFO(t, parent.0, k),
data = kindata, model = TRUE,
start = start.SFO,
algorithm = algorithm), silent=TRUE)
}
k.est = 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$alpha / k.est
}
if (parent.0.fixed)
{
start.FOMC = list(alpha = start.FOMC$alpha, beta = start.FOMC$beta)
kinfits[[kinmodel]] = try(
nls(parent ~ FOMC(t, parent.0.user, alpha, beta),
data = kindata, model = TRUE,
start = start.FOMC,
algorithm = algorithm), silent=TRUE)
} else {
kinfits[[kinmodel]] = try(
nls(parent ~ FOMC(t, parent.0, alpha, beta),
data = kindata, model = TRUE,
start = start.FOMC,
algorithm = algorithm), silent=TRUE)
}
}
if (kinmodel == "DFOP") {
if (is.na(start.DFOP$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
}
if (parent.0.fixed)
{
start.DFOP = list(k1 = start.DFOP$k1, k2 = start.DFOP$k2, g = start.DFOP$g)
kinfits[[kinmodel]] = try(
nls(parent ~ DFOP(t, parent.0.user, k1, k2, g),
data = kindata, model = TRUE,
start = start.DFOP,
algorithm = algorithm), silent=TRUE)
}else{
kinfits[[kinmodel]] = try(
nls(parent ~ DFOP(t, parent.0, k1, k2, g),
data = kindata, model = TRUE,
start = start.DFOP,
algorithm = algorithm), silent=TRUE)
}
}
if (kinmodel == "HS") {
if (is.na(start.HS$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$t)
}
if (parent.0.fixed)
{
start.HS = list(k1 = start.HS$k1, k2 = start.HS$k2, tb = start.HS$tb)
kinfits[[kinmodel]] = try(
nls(parent ~ HS(t, parent.0.user, k1, k2, tb),
data = kindata, model = TRUE,
start = start.HS,
algorithm = algorithm), silent=TRUE)
}else{
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)
}
