Rudimentary support for setting up nlmixr models

- All degradation models are specified as ODE models. This appears to be
fast enough
- Error models are being translated to nlmixr as close to the mkin error
model as possible. When using the 'saem' backend, it appears not to be
possible to use the same error model for more than one observed variable
- No support yet for models with parallel formation of metabolites, where
the ilr transformation is used in mkin per default
- There is a bug in nlmixr which appears to be triggered if the data are
not balanced, see nlmixrdevelopment/nlmixr#530
- There is a print and a plot method, the summary method is not finished

1 files changed, 250 insertions, 0 deletions

diff --git a/R/summary.nlmixr.mmkin.R b/R/summary.nlmixr.mmkin.R
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+#' Summary method for class "nlmixr.mmkin"
+#'
+#' Lists model equations, initial parameter values, optimised parameters
+#' for fixed effects (population), random effects (deviations from the
+#' population mean) and residual error model, as well as the resulting
+#' endpoints such as formation fractions and DT50 values. Optionally
+#' (default is FALSE), the data are listed in full.
+#'
+#' @param object an object of class [nlmix.mmkin]
+#' @param x an object of class [summary.nlmix.mmkin]
+#' @param data logical, indicating whether the full data should be included in
+#'   the summary.
+#' @param verbose Should the summary be verbose?
+#' @param distimes logical, indicating whether DT50 and DT90 values should be
+#'   included.
+#' @param digits Number of digits to use for printing
+#' @param \dots optional arguments passed to methods like \code{print}.
+#' @return The summary function returns a list obtained in the fit, with at
+#'   least the following additional components
+#'   \item{nlmixrversion, mkinversion, Rversion}{The nlmixr, mkin and R versions used}
+#'   \item{date.fit, date.summary}{The dates where the fit and the summary were
+#'     produced}
+#'   \item{diffs}{The differential equations used in the degradation model}
+#'   \item{use_of_ff}{Was maximum or minimum use made of formation fractions}
+#'   \item{data}{The data}
+#'   \item{confint_trans}{Transformed parameters as used in the optimisation, with confidence intervals}
+#'   \item{confint_back}{Backtransformed parameters, with confidence intervals if available}
+#'   \item{confint_errmod}{Error model parameters with confidence intervals}
+#'   \item{ff}{The estimated formation fractions derived from the fitted
+#'      model.}
+#'   \item{distimes}{The DT50 and DT90 values for each observed variable.}
+#'   \item{SFORB}{If applicable, eigenvalues of SFORB components of the model.}
+#'   The print method is called for its side effect, i.e. printing the summary.
+#' @importFrom stats predict vcov
+#' @author Johannes Ranke for the mkin specific parts
+#'   nlmixr authors for the parts inherited from nlmixr.
+#' @examples
+#' # Generate five datasets following DFOP-SFO kinetics
+#' sampling_times = c(0, 1, 3, 7, 14, 28, 60, 90, 120)
+#' dfop_sfo <- mkinmod(parent = mkinsub("DFOP", "m1"),
+#'  m1 = mkinsub("SFO"), quiet = TRUE)
+#' set.seed(1234)
+#' k1_in <- rlnorm(5, log(0.1), 0.3)
+#' k2_in <- rlnorm(5, log(0.02), 0.3)
+#' g_in <- plogis(rnorm(5, qlogis(0.5), 0.3))
+#' f_parent_to_m1_in <- plogis(rnorm(5, qlogis(0.3), 0.3))
+#' k_m1_in <- rlnorm(5, log(0.02), 0.3)
+#'
+#' pred_dfop_sfo <- function(k1, k2, g, f_parent_to_m1, k_m1) {
+#'   mkinpredict(dfop_sfo,
+#'     c(k1 = k1, k2 = k2, g = g, f_parent_to_m1 = f_parent_to_m1, k_m1 = k_m1),
+#'     c(parent = 100, m1 = 0),
+#'     sampling_times)
+#' }
+#'
+#' ds_mean_dfop_sfo <- lapply(1:5, function(i) {
+#'   mkinpredict(dfop_sfo,
+#'     c(k1 = k1_in[i], k2 = k2_in[i], g = g_in[i],
+#'       f_parent_to_m1 = f_parent_to_m1_in[i], k_m1 = k_m1_in[i]),
+#'     c(parent = 100, m1 = 0),
+#'     sampling_times)
+#' })
+#' names(ds_mean_dfop_sfo) <- paste("ds", 1:5)
+#'
+#' ds_syn_dfop_sfo <- lapply(ds_mean_dfop_sfo, function(ds) {
+#'   add_err(ds,
+#'     sdfunc = function(value) sqrt(1^2 + value^2 * 0.07^2),
+#'     n = 1)[[1]]
+#' })
+#'
+#' \dontrun{
+#' # Evaluate using mmkin and nlmixr
+#' f_mmkin_dfop_sfo <- mmkin(list(dfop_sfo), ds_syn_dfop_sfo,
+#'   quiet = TRUE, error_model = "tc", cores = 5)
+#' f_saemix_dfop_sfo <- mkin::saem(f_mmkin_dfop_sfo)
+#' f_nlme_dfop_sfo <- mkin::nlme(f_mmkin_dfop_sfo)
+#' f_nlmixr_dfop_sfo_saem <- nlmixr(f_mmkin_dfop_sfo, est = "saem")
+#' # The following takes a very long time but gives
+#' f_nlmixr_dfop_sfo_focei <- nlmixr(f_mmkin_dfop_sfo, est = "focei")
+#' AIC(f_nlmixr_dfop_sfo_saem$nm, f_nlmixr_dfop_sfo_focei$nm)
+#' summary(f_nlmixr_dfop_sfo, data = TRUE)
+#' }
+#'
+#' @export
+summary.nlmixr.mmkin <- function(object, data = FALSE, verbose = FALSE, distimes = TRUE, ...) {
+
+  mod_vars <- names(object$mkinmod$diffs)
+
+  pnames <- names(object$mean_dp_start)
+  np <- length(pnames)
+
+  conf.int <- confint(object$nm)
+  confint_trans <- as.matrix(conf.int[pnames, c(1, 3, 4)])
+  colnames(confint_trans) <- c("est.", "lower", "upper")
+
+  bp <- backtransform_odeparms(confint_trans[, "est."], object$mkinmod,
+    object$transform_rates, object$transform_fractions)
+  bpnames <- names(bp)
+
+  # Transform boundaries of CI for one parameter at a time,
+  # with the exception of sets of formation fractions (single fractions are OK).
+  f_names_skip <- character(0)
+  for (box in mod_vars) { # Figure out sets of fractions to skip
+    f_names <- grep(paste("^f", box, sep = "_"), pnames, value = TRUE)
+    n_paths <- length(f_names)
+    if (n_paths > 1) f_names_skip <- c(f_names_skip, f_names)
+  }
+
+  confint_back <- matrix(NA, nrow = length(bp), ncol = 3,
+    dimnames = list(bpnames, colnames(confint_trans)))
+  confint_back[, "est."] <- bp
+
+  for (pname in pnames) {
+    if (!pname %in% f_names_skip) {
+      par.lower <- confint_trans[pname, "lower"]
+      par.upper <- confint_trans[pname, "upper"]
+      names(par.lower) <- names(par.upper) <- pname
+      bpl <- backtransform_odeparms(par.lower, object$mkinmod,
+                                            object$transform_rates,
+                                            object$transform_fractions)
+      bpu <- backtransform_odeparms(par.upper, object$mkinmod,
+                                            object$transform_rates,
+                                            object$transform_fractions)
+      confint_back[names(bpl), "lower"] <- bpl
+      confint_back[names(bpu), "upper"] <- bpu
+    }
+  }
+
+  #  Correlation of fixed effects (inspired by summary.nlme)
+  varFix <- vcov(object$nm)
+  stdFix <- sqrt(diag(varFix))
+  object$corFixed <- array(
+    t(varFix/stdFix)/stdFix,
+    dim(varFix),
+    list(pnames, pnames))
+
+  object$confint_back <- confint_back
+
+  object$date.summary = date()
+  object$use_of_ff = object$mkinmod$use_of_ff
+
+  object$diffs <- object$mkinmod$diffs
+  object$print_data <- data # boolean: Should we print the data?
+  predict(object$nm)
+  so_pred <- object$so@results@predictions
+
+  names(object$data)[4] <- "observed" # rename value to observed
+
+  object$verbose <- verbose
+
+  object$fixed <- object$mmkin_orig[[1]]$fixed
+  object$AIC = AIC(object$so)
+  object$BIC = BIC(object$so)
+  object$logLik = logLik(object$so, method = "is")
+
+  ep <- endpoints(object)
+  if (length(ep$ff) != 0)
+    object$ff <- ep$ff
+  if (distimes) object$distimes <- ep$distimes
+  if (length(ep$SFORB) != 0) object$SFORB <- ep$SFORB
+  class(object) <- c("summary.saem.mmkin")
+  return(object)
+}
+
+#' @rdname summary.saem.mmkin
+#' @export
+print.summary.saem.mmkin <- function(x, digits = max(3, getOption("digits") - 3), verbose = x$verbose, ...) {
+  cat("saemix version used for fitting:     ", x$saemixversion, "\n")
+  cat("mkin version used for pre-fitting: ", x$mkinversion, "\n")
+  cat("R version used for fitting:        ", x$Rversion, "\n")
+
+  cat("Date of fit:    ", x$date.fit, "\n")
+  cat("Date of summary:", x$date.summary, "\n")
+
+  cat("\nEquations:\n")
+  nice_diffs <- gsub("^(d.*) =", "\\1/dt =", x[["diffs"]])
+  writeLines(strwrap(nice_diffs, exdent = 11))
+
+  cat("\nData:\n")
+  cat(nrow(x$data), "observations of",
+    length(unique(x$data$name)), "variable(s) grouped in",
+    length(unique(x$data$ds)), "datasets\n")
+
+  cat("\nModel predictions using solution type", x$solution_type, "\n")
+
+  cat("\nFitted in", x$time[["elapsed"]],  "s using", paste(x$so@options$nbiter.saemix, collapse = ", "), "iterations\n")
+
+  cat("\nVariance model: ")
+  cat(switch(x$err_mod,
+    const = "Constant variance",
+    obs = "Variance unique to each observed variable",
+    tc = "Two-component variance function"), "\n")
+
+  cat("\nMean of starting values for individual parameters:\n")
+  print(x$mean_dp_start, digits = digits)
+
+  cat("\nFixed degradation parameter values:\n")
+  if(length(x$fixed$value) == 0) cat("None\n")
+  else print(x$fixed, digits = digits)
+
+  cat("\nResults:\n\n")
+  cat("Likelihood computed by importance sampling\n")
+  print(data.frame(AIC = x$AIC, BIC = x$BIC, logLik = x$logLik,
+      row.names = " "), digits = digits)
+
+  cat("\nOptimised parameters:\n")
+  print(x$confint_trans, digits = digits)
+
+  if (nrow(x$confint_trans) > 1) {
+    corr <- x$corFixed
+    class(corr) <- "correlation"
+    print(corr, title = "\nCorrelation:", ...)
+  }
+
+  cat("\nRandom effects:\n")
+  print(x$confint_ranef, digits = digits)
+
+  cat("\nVariance model:\n")
+  print(x$confint_errmod, digits = digits)
+
+  if (x$transformations == "mkin") {
+    cat("\nBacktransformed parameters:\n")
+    print(x$confint_back, digits = digits)
+  }
+
+  printSFORB <- !is.null(x$SFORB)
+  if(printSFORB){
+    cat("\nEstimated Eigenvalues of SFORB model(s):\n")
+    print(x$SFORB, digits = digits,...)
+  }
+
+  printff <- !is.null(x$ff)
+  if(printff){
+    cat("\nResulting formation fractions:\n")
+    print(data.frame(ff = x$ff), digits = digits,...)
+  }
+
+  printdistimes <- !is.null(x$distimes)
+  if(printdistimes){
+    cat("\nEstimated disappearance times:\n")
+    print(x$distimes, digits = digits,...)
+  }
+
+  if (x$print_data){
+    cat("\nData:\n")
+    print(format(x$data, digits = digits, ...), row.names = FALSE)
+  }
+
+  invisible(x)
+}