# 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())
#' @export
nlme::nlme
#' 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.
#'
#' Note that the convergence of the nlme algorithms depends on the quality
#' of the data. In degradation kinetics, we often only have few datasets
#' (e.g. data for few soils) and complicated degradation models, which may
#' make it impossible to obtain convergence with nlme.
#'
#' @param model An [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, no correlations between random effects are
#' set up for the optimised degradation model parameters. This is
#' achieved by using the [nlme::pdDiag] method.
#' @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. It also inherits from 'mixed.mmkin'.
#' @note As the object inherits from [nlme::nlme], there is a wealth of
#' methods that will automatically work on 'nlme.mmkin' objects, such as
#' [nlme::intervals()], [nlme::anova.lme()] and [nlme::coef.lme()].
#' @export
#' @seealso [nlme_function()], [plot.mixed.mmkin], [summary.nlme.mmkin]
#' @examples
#' ds <- lapply(experimental_data_for_UBA_2019[6:10],
#' function(x) subset(x$data[c("name", "time", "value")], name == "parent"))
#'
#' \dontrun{
#' f <- mmkin(c("SFO", "DFOP"), ds, quiet = TRUE, cores = 1)
#' library(nlme)
#' f_nlme_sfo <- nlme(f["SFO", ])
#' f_nlme_dfop <- nlme(f["DFOP", ])
#' anova(f_nlme_sfo, f_nlme_dfop)
#' print(f_nlme_dfop)
#' plot(f_nlme_dfop)
#' endpoints(f_nlme_dfop)
#'
#' ds_2 <- lapply(experimental_data_for_UBA_2019[6:10],
#' function(x) x$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_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,
#' "DFOP-SFO" = m_dfop_sfo),
#' ds_2, quiet = TRUE)
#'
#' f_nlme_sfo_sfo <- nlme(f_2["SFO-SFO", ])
#' plot(f_nlme_sfo_sfo)
#'
#' # With formation fractions this does not coverge with defaults
#' # f_nlme_sfo_sfo_ff <- nlme(f_2["SFO-SFO-ff", ])
#' #plot(f_nlme_sfo_sfo_ff)
#'
#' # For the following, we need to increase pnlsMaxIter and the tolerance
#' # to get convergence
#' f_nlme_dfop_sfo <- nlme(f_2["DFOP-SFO", ],
#' control = list(pnlsMaxIter = 120, tolerance = 5e-4))
#'
#' plot(f_nlme_dfop_sfo)
#'
#' anova(f_nlme_dfop_sfo, f_nlme_sfo_sfo)
#'
#' 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 are possible,
#' # but need tweeking of control values and sometimes do not converge
#'
#' 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 <- update(f_2, error_model = "obs")
#' f_nlme_sfo_sfo_obs <- nlme(f_2_obs["SFO-SFO", ])
#' print(f_nlme_sfo_sfo_obs)
#' f_nlme_dfop_sfo_obs <- nlme(f_2_obs["DFOP-SFO", ],
#' control = list(pnlsMaxIter = 120, tolerance = 5e-4))
#'
#' f_2_tc <- update(f_2, error_model = "tc")
#' # f_nlme_sfo_sfo_tc <- nlme(f_2_tc["SFO-SFO", ]) # No convergence with 50 iterations
#' # f_nlme_dfop_sfo_tc <- nlme(f_2_tc["DFOP-SFO", ],
#' # control = list(pnlsMaxIter = 120, tolerance = 5e-4)) # Error in X[, fmap[[nm]]] <- gradnm
#'
#' anova(f_nlme_dfop_sfo, f_nlme_dfop_sfo_obs)
#'
#' }
nlme.mmkin <- function(model, data = "auto",
fixed = lapply(as.list(names(mean_degparms(model))),
function(el) eval(parse(text = paste(el, 1, sep = "~")))),
random = pdDiag(fixed),
groups,
start = mean_degparms(model, random = TRUE, test_log_parms = TRUE),
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("data"))))) {
warning("'nlme.mmkin' will redefine 'data'")
}
# Get native symbol info for speed
if (model[[1]]$solution_type == "deSolve" & !is.null(model[[1]]$mkinmod$cf)) {
# The mkinmod stored in the first fit will be used by nlme
model[[1]]$mkinmod$symbols <- deSolve::checkDLL(
dllname = model[[1]]$mkinmod$dll_info[["name"]],
func = "diffs", initfunc = "initpar",
jacfunc = NULL, nout = 0, outnames = NULL)
}
deg_func <- nlme_function(model)
assign("deg_func", deg_func, getFromNamespace(".nlme_env", "mkin"))
# For the formula, get the degradation function from the mkin namespace
this_model_text <- paste0("value ~ mkin::get_deg_func()(",
paste(names(formals(deg_func)), collapse = ", "), ")")
this_model <- as.formula(this_model_text)
thisCall[["model"]] <- this_model
thisCall[["data"]] <- nlme_data(model)
thisCall[["start"]] <- start
thisCall[["fixed"]] <- fixed
thisCall[["random"]] <- random
error_model <- model[[1]]$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$sigma = 1
thisCall[["control"]] <- control
}
fit_time <- system.time(val <- do.call("nlme.formula", thisCall))
val$time <- fit_time
val$mkinmod <- model[[1]]$mkinmod
# Don't return addresses that will become invalid
val$mkinmod$symbols <- NULL
val$data <- thisCall[["data"]]
val$mmkin <- model
if (is.list(start)) val$mean_dp_start <- start$fixed
else val$mean_dp_start <- start
val$transform_rates <- model[[1]]$transform_rates
val$transform_fractions <- model[[1]]$transform_fractions
val$solution_type <- model[[1]]$solution_type
val$err_mode <- error_model
val$bparms.optim <- backtransform_odeparms(val$coefficients$fixed,
val$mkinmod,
transform_rates = val$transform_rates,
transform_fractions = val$transform_fractions)
val$bparms.fixed <- model[[1]]$bparms.fixed
val$date.fit <- date()
val$nlmeversion <- as.character(utils::packageVersion("nlme"))
val$mkinversion <- as.character(utils::packageVersion("mkin"))
val$Rversion <- paste(R.version$major, R.version$minor, sep=".")
class(val) <- c("nlme.mmkin", "mixed.mmkin", "nlme", "lme")
return(val)
}
#' @export
#' @rdname nlme.mmkin
#' @param x An nlme.mmkin object to print
#' @param digits Number of digits to use for printing
print.nlme.mmkin <- function(x, digits = max(3, getOption("digits") - 3), ...) {
cat( "Kinetic nonlinear mixed-effects model fit by " )
cat( if(x$method == "REML") "REML\n" else "maximum likelihood\n")
cat("\nStructural model:\n")
diffs <- x$mmkin[[1]]$mkinmod$diffs
nice_diffs <- gsub("^(d.*) =", "\\1/dt =", diffs)
writeLines(strwrap(nice_diffs, exdent = 11))
cat("\nData:\n")
cat(nrow(x$data), "observations of",
length(unique(x$data$name)), "variable(s) grouped in",
length(unique(x$data$ds)), "datasets\n")
cat("\nLog-", if(x$method == "REML") "restricted-" else "",
"likelihood: ", format(x$logLik, digits = digits), "\n", sep = "")
fixF <- x$call$fixed
cat("\nFixed effects:\n",
deparse(
if(inherits(fixF, "formula") || is.call(fixF) || is.name(fixF))
x$call$fixed
else
lapply(fixF, function(el) as.name(deparse(el)))), "\n")
print(fixef(x), digits = digits, ...)
cat("\n")
print(summary(x$modelStruct), sigma = x$sigma, digits = digits, ...)
invisible(x)
}
#' @export
#' @rdname nlme.mmkin
#' @param object An nlme.mmkin object to update
#' @param ... Update specifications passed to update.nlme
update.nlme.mmkin <- function(object, ...) {
res <- NextMethod()
res$mmkin <- object$mmkin
class(res) <- c("nlme.mmkin", "nlme", "lme")
return(res)
}
