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# Code inspired by nlme::nlme.nlsList and R/nlme_fit.R from nlmixr
# We need to assign the degradation function created in nlme.mmkin to an
# environment that is always accessible, also e.g. when evaluation is done by
# testthat or pkgdown. Therefore parent.frame() is not good enough. The
# following environment will be in the mkin namespace.
.nlme_env <- new.env(parent = emptyenv())
#' Retrieve a degradation function from the mmkin namespace
#'
#' @importFrom utils getFromNamespace
#' @return A function that was likely previously assigned from within
#' nlme.mmkin
#' @export
get_deg_func <- function() {
return(get("deg_func", getFromNamespace(".nlme_env", "mkin")))
}
#' 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 as.formula
#' @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$data[c("name", "time", "value")], name == "parent"))
#' f <- mmkin(c("SFO", "DFOP"), ds, quiet = TRUE, cores = 1)
#' library(nlme)
#' f_nlme_sfo <- nlme(f["SFO", ])
#' f_nlme_dfop <- nlme(f["DFOP", ])
#' AIC(f_nlme_sfo, f_nlme_dfop)
#' print(f_nlme_dfop)
#' endpoints(f_nlme_dfop)
#' \dontrun{
#' f_nlme_2 <- nlme(f["SFO", ], start = c(parent_0 = 100, log_k_parent = 0.1))
#' update(f_nlme_2, random = parent_0 ~ 1)
#' ds_2 <- lapply(experimental_data_for_UBA_2019[6:10],
#' function(x) x$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
#'
#' endpoints(f_nlme_sfo_sfo)
#' endpoints(f_nlme_dfop_sfo)
#'
#' if (length(findFunction("varConstProp")) > 0) { # tc error model for nlme available
#' # Attempts to fit metabolite kinetics with the tc error model
#' #f_2_tc <- 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,
#' # error_model = "tc")
#' #f_nlme_sfo_sfo_tc <- nlme(f_2_tc["SFO-SFO", ], control = list(maxIter = 100))
#' #f_nlme_dfop_sfo_tc <- nlme(f_2_tc["DFOP-SFO", ])
#' #f_nlme_dfop_sfo_tc <- update(f_nlme_dfop_sfo, weights = varConstProp(),
#' # control = list(sigma = 1, msMaxIter = 100, pnlsMaxIter = 15))
#' # Fitting metabolite kinetics with nlme.mmkin and the two-component
#' # error model currently does not work, at least not with these data.
#'
#' f_tc <- mmkin(c("SFO", "DFOP"), ds, quiet = TRUE, error_model = "tc")
#' f_nlme_sfo_tc <- nlme(f_tc["SFO", ])
#' f_nlme_dfop_tc <- nlme(f_tc["DFOP", ])
#' AIC(f_nlme_sfo, f_nlme_sfo_tc, f_nlme_dfop, f_nlme_dfop_tc)
#' print(f_nlme_dfop_tc)
#' }
#' f_2_obs <- mmkin(list("SFO-SFO" = m_sfo_sfo,
#' "DFOP-SFO" = m_dfop_sfo),
#' ds_2, quiet = TRUE, error_model = "obs")
#' f_nlme_sfo_sfo_obs <- nlme(f_2_obs["SFO-SFO", ])
#' print(f_nlme_sfo_sfo_obs)
#' # The same with DFOP-SFO does not converge, apparently the variances of
#' # parent and A1 are too similar in this case, so that the model is
#' # overparameterised
#' #f_nlme_dfop_sfo_obs <- nlme(f_2_obs["DFOP-SFO", ], control = list(maxIter = 100))
#' }
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 arguments were used 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, getFromNamespace(".nlme_env", "mkin"))
# For the formula, get the degradation function from the mkin namespace
this_model_text <- paste0("value ~ mkin::get_deg_func()(",
paste(names(formals(deg_func)), collapse = ", "), ")")
this_model <- as.formula(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"]])
}
error_model <- model[[1]]$err_mod
if (missing(weights)) {
thisCall[["weights"]] <- switch(error_model,
const = NULL,
obs = varIdent(form = ~ 1 | name),
tc = varConstProp())
sigma <- switch(error_model,
tc = 1,
NULL)
}
control <- thisCall[["control"]]
if (error_model == "tc") {
control$sigma = 1
thisCall[["control"]] <- control
}
val <- do.call("nlme.formula", thisCall)
val$mmkin_orig <- model
val$data <- thisCall[["data"]]
val$mkinmod <- model[[1]]$mkinmod
class(val) <- c("nlme.mmkin", "nlme", "lme")
return(val)
}
#' @export
#' @rdname nlme.mmkin
#' @param x An nlme.mmkin object to print
#' @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)
}
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