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# Copyright (C) 2015,2019 Johannes Ranke
# Contact: jranke@uni-bremen.de
# The summary function is an adapted and extended version of summary.modFit
# from the FME package, v 1.1 by Soetart and Petzoldt, which was in turn
# inspired by summary.nls.lm
# This file is part of the R package mkin
# mkin 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/>
mmkin <- function(models = c("SFO", "FOMC", "DFOP"), datasets,
cores = round(detectCores()/2), cluster = NULL, ...)
{
parent_models_available = c("SFO", "FOMC", "DFOP", "HS", "SFORB", "IORE", "logistic")
n.m <- length(models)
n.d <- length(datasets)
n.fits <- n.m * n.d
fit_indices <- matrix(1:n.fits, ncol = n.d)
# Check models and define their names
if (!all(sapply(models, function(x) inherits(x, "mkinmod")))) {
if (!all(models %in% parent_models_available)) {
stop("Please supply models as a list of mkinmod objects or a vector combined of\n ",
paste(parent_models_available, collapse = ", "))
} else {
names(models) <- models
}
} else {
if (is.null(names(models))) names(models) <- as.character(1:n.m)
}
# Check datasets and define their names
if (is.null(names(datasets))) names(datasets) <- as.character(1:n.d)
# Define names for fit index
dimnames(fit_indices) <- list(model = names(models),
dataset = names(datasets))
fit_function <- function(fit_index) {
w <- which(fit_indices == fit_index, arr.ind = TRUE)
model_index <- w[1]
dataset_index <- w[2]
mkinfit(models[[model_index]], datasets[[dataset_index]], ...)
}
if (is.null(cluster)) {
results <- mclapply(as.list(1:n.fits), fit_function, mc.cores = cores)
} else {
results <- parLapply(cluster, as.list(1:n.fits), fit_function)
}
attributes(results) <- attributes(fit_indices)
class(results) <- "mmkin"
return(results)
}
"[.mmkin" <- function(x, i, j, ..., drop = FALSE) {
class(x) <- NULL
x_sub <- x[i, j, drop = drop]
if (!drop) class(x_sub) <- "mmkin"
return(x_sub)
}
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