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#' Create an nlme model for an mmkin row object
#'
#' This functions sets up a nonlinear mixed effects model for an mmkin row
#' object. An mmkin row object is essentially a list of mkinfit objects that
#' have been obtained by fitting the same model to a list of datasets.
#'
#' @param model An \code{\link{mmkin}} row object.
#' @param data Ignored, data are taken from the mmkin model
#' @param fixed Ignored, all degradation parameters fitted in the
#' mmkin model are used as fixed parameters
#' @param random If not specified, all fixed effects are complemented
#' with uncorrelated random effects
#' @param groups See the documentation of nlme
#' @param start If not specified, mean values of the fitted degradation
#' parameters taken from the mmkin object are used
#' @param correlation See the documentation of nlme
#' @param weights passed to nlme
#' @param subset passed to nlme
#' @param method passed to nlme
#' @param na.action passed to nlme
#' @param naPattern passed to nlme
#' @param control passed to nlme
#' @param verbose passed to nlme
#' @importFrom stats na.fail
#' @return Upon success, a fitted nlme.mmkin object, which is an nlme object
#' with additional elements
#' @export
#' @seealso \code{\link{nlme_function}}
#' @examples
#' ds <- lapply(experimental_data_for_UBA_2019[6:10],
#' function(x) subset(x$data[c("name", "time", "value")], name == "parent"))
#' f <- mmkin("SFO", ds, quiet = TRUE, cores = 1)
#' library(nlme)
#' f_nlme <- nlme(f)
#' print(f_nlme)
#' f_nlme_2 <- nlme(f, start = c(parent_0 = 100, log_k_parent_sink = 0.1))
#' update(f_nlme_2, random = parent_0 ~ 1)
#' \dontrun{
#' # Test on some real data
#' ds_2 <- lapply(experimental_data_for_UBA_2019[6:10],
#' function(x) x$data[c("name", "time", "value")])
#' m_sfo_sfo <- mkinmod(parent = mkinsub("SFO", "A1"),
#' A1 = mkinsub("SFO"), use_of_ff = "min", quiet = TRUE)
#' m_sfo_sfo_ff <- mkinmod(parent = mkinsub("SFO", "A1"),
#' A1 = mkinsub("SFO"), use_of_ff = "max", quiet = TRUE)
#' m_fomc_sfo <- mkinmod(parent = mkinsub("FOMC", "A1"),
#' A1 = mkinsub("SFO"), quiet = TRUE)
#' m_dfop_sfo <- mkinmod(parent = mkinsub("DFOP", "A1"),
#' A1 = mkinsub("SFO"), quiet = TRUE)
#'
#' f_2 <- mmkin(list("SFO-SFO" = m_sfo_sfo,
#' "SFO-SFO-ff" = m_sfo_sfo_ff,
#' "FOMC-SFO" = m_fomc_sfo,
#' "DFOP-SFO" = m_dfop_sfo),
#' ds_2, quiet = TRUE)
#' plot(f_2["SFO-SFO", 3:4]) # Separate fits for datasets 3 and 4
#'
#' f_nlme_sfo_sfo <- nlme(f_2["SFO-SFO", ])
#' # plot(f_nlme_sfo_sfo) # not feasible with pkgdown figures
#' plot(f_nlme_sfo_sfo, 3:4) # Global mixed model: Fits for datasets 3 and 4
#'
#' # With formation fractions
#' f_nlme_sfo_sfo_ff <- nlme(f_2["SFO-SFO-ff", ])
#' plot(f_nlme_sfo_sfo_ff, 3:4) # chi2 different due to different df attribution
#'
#' # For more parameters, we need to increase pnlsMaxIter and the tolerance
#' # to get convergence
#' f_nlme_fomc_sfo <- nlme(f_2["FOMC-SFO", ],
#' control = list(pnlsMaxIter = 100, tolerance = 1e-4), verbose = TRUE)
#' f_nlme_dfop_sfo <- nlme(f_2["DFOP-SFO", ],
#' control = list(pnlsMaxIter = 120, tolerance = 5e-4), verbose = TRUE)
#' plot(f_2["FOMC-SFO", 3:4])
#' plot(f_nlme_fomc_sfo, 3:4)
#'
#' plot(f_2["DFOP-SFO", 3:4])
#' plot(f_nlme_dfop_sfo, 3:4)
#'
#' anova(f_nlme_dfop_sfo, f_nlme_fomc_sfo, f_nlme_sfo_sfo)
#' anova(f_nlme_dfop_sfo, f_nlme_sfo_sfo) # if we ignore FOMC
#' }
# Code inspired by nlme.nlsList
nlme.mmkin <- function(model, data = sys.frame(sys.parent()),
fixed, random = fixed,
groups, start, correlation = NULL, weights = NULL,
subset, method = c("ML", "REML"),
na.action = na.fail, naPattern,
control = list(), verbose= FALSE)
{
if (nrow(model) > 1) stop("Only row objects allowed")
thisCall <- as.list(match.call())[-1]
# warn in case of use of arguments that are overriden
if (any(!is.na(match(names(thisCall),
c("fixed", "data"))))) {
warning("'nlme.mmkin' will redefine 'fixed' and 'data'")
}
deg_func <- nlme_function(model)
assign("deg_func", deg_func, globalenv())
# specify the model formula
this_model_text <- paste0("value ~ deg_func(",
paste(names(formals(deg_func)), collapse = ", "), ")")
this_model <- eval(parse(text = this_model_text))
thisCall[["model"]] <- this_model
mean_dp <- mean_degparms(model)
dp_names <- names(mean_dp)
thisCall[["data"]] <- nlme_data(model)
if (missing(start)) {
thisCall[["start"]] <- mean_degparms(model, random = TRUE)
}
thisCall[["fixed"]] <- lapply(as.list(dp_names), function(el)
eval(parse(text = paste(el, 1, sep = "~"))))
if (missing(random)) {
thisCall[["random"]] <- pdDiag(thisCall[["fixed"]])
}
val <- do.call("nlme.formula", thisCall)
val$mmkin_orig <- model
class(val) <- c("nlme.mmkin", "nlme", "lme")
return(val)
}
#' @export
#' @rdname nlme.mmkin
#' @param x An nlme.mmkin object to print
#' @param data Should the data be printed?
#' @param ... Further arguments as in the generic
print.nlme.mmkin <- function(x, ...) {
x$call$data <- "Not shown"
NextMethod("print", x)
}
#' @export
#' @rdname nlme.mmkin
#' @param object An nlme.mmkin object to update
#' @param ... Update specifications passed to update.nlme
update.nlme.mmkin <- function(object, ...) {
res <- NextMethod()
res$mmkin_orig <- object$mmkin_orig
class(res) <- c("nlme.mmkin", "nlme", "lme")
return(res)
}
# The following is necessary as long as R bug 17761 is not fixed
# https://bugs.r-project.org/bugzilla/show_bug.cgi?id=17761
#' @export
anova.nlme.mmkin <- function(object, ...) {
thisCall <- as.list(match.call())[-1]
object_name <- as.character(thisCall[[1]])
other_object_names <- sapply(thisCall[-1], as.character)
remove_class <- function(object, classname) {
old_class <- class(object)
class(object) <- setdiff(old_class, classname)
return(object)
}
object <- remove_class(object, "nlme.mmkin")
other_objects <- list(...)
other_objects <- lapply(other_objects, remove_class, "nlme.mmkin")
env <- new.env()
assign(object_name, object, env)
for (i in seq_along(other_objects)) {
assign(other_object_names[i], other_objects[[i]], env)
}
res <- eval(parse(text = paste0("anova.lme(", object_name, ", ",
paste(other_object_names, collapse = ", "), ")")),
envir = env)
return(res)
}
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