Improve saem method, add summary

Also make the endpoints function work for saem objects.

1 files changed, 250 insertions, 0 deletions

diff --git a/R/summary.saem.mmkin.R b/R/summary.saem.mmkin.R
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+#' Summary method for class "saem.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 [saem.mmkin]
+#' @param x an object of class [summary.saem.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 based on the [saemix::SaemixObject]
+#'   obtained in the fit, with at least the following additional components
+#'   \item{saemixversion, mkinversion, Rversion}{The saemix, 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{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
+#'   saemix authors for the parts inherited from saemix.
+#' @examples
+#' # Generate five datasets following SFO kinetics
+#' sampling_times = c(0, 1, 3, 7, 14, 28, 60, 90, 120)
+#' dt50_sfo_in_pop <- 50
+#' k_in_pop <- log(2) / dt50_sfo_in_pop
+#' set.seed(1234)
+#' k_in <- rlnorm(5, log(k_in_pop), 0.5)
+#' SFO <- mkinmod(parent = mkinsub("SFO"))
+#'
+#' pred_sfo <- function(k) {
+#'   mkinpredict(SFO,
+#'     c(k_parent = k),
+#'     c(parent = 100),
+#'     sampling_times)
+#' }
+#'
+#' ds_sfo_mean <- lapply(k_in, pred_sfo)
+#' names(ds_sfo_mean) <- paste("ds", 1:5)
+#'
+#' ds_sfo_syn <- lapply(ds_sfo_mean, function(ds) {
+#'   add_err(ds,
+#'     sdfunc = function(value) sqrt(1^2 + value^2 * 0.07^2),
+#'     n = 1)[[1]]
+#' })
+#'
+#' \dontrun{
+#' # Evaluate using mmkin and saem
+#' f_mmkin <- mmkin("SFO", ds_sfo_syn, quiet = TRUE, error_model = "tc", cores = 1)
+#' f_saem <- saem(f_mmkin)
+#' summary(f_saem, data = TRUE)
+#' }
+#'
+#' @export
+summary.saem.mmkin <- function(object, data = FALSE, verbose = FALSE, distimes = TRUE, ...) {
+
+  mod_vars <- names(object$mkinmod$diffs)
+
+  pnames <- names(object$mean_dp_start)
+  np <- length(pnames)
+
+  conf.int <- object$so@results@conf.int
+  rownames(conf.int) <- conf.int$name
+  confint_trans <- as.matrix(conf.int[pnames, c("estimate", "lower", "upper")])
+  colnames(confint_trans)[1] <- "est."
+
+  bp <- backtransform_odeparms(confint_trans[, "est."], object$mkinmod,
+    object$transform_rates, object$transform_fractions)
+  bpnames <- names(bp)
+
+  #  Correlation of fixed effects (inspired by summary.nlme)
+  varFix <- vcov(object$so)[1:np, 1:np]
+  stdFix <- sqrt(diag(varFix))
+  object$corFixed <- array(
+    t(varFix/stdFix)/stdFix,
+    dim(varFix),
+    list(pnames, pnames))
+
+  # Random effects
+  rnames <- paste0("SD.", pnames)
+  confint_ranef <- as.matrix(conf.int[rnames, c("estimate", "lower", "upper")])
+  colnames(confint_ranef)[1] <- "est."
+
+  # Error model
+  enames <- object$so@results@name.sigma
+  confint_errmod <- as.matrix(conf.int[enames, c("estimate", "lower", "upper")])
+  colnames(confint_errmod)[1] <- "est."
+
+  # Transform boundaries of CI for one parameter at a time,
+  # with the exception of sets of formation fractions (single fractions are OK).
+  f_names_skip <- character(0)
+  for (box in mod_vars) { # Figure out sets of fractions to skip
+    f_names <- grep(paste("^f", box, sep = "_"), pnames, value = TRUE)
+    n_paths <- length(f_names)
+    if (n_paths > 1) f_names_skip <- c(f_names_skip, f_names)
+  }
+
+  confint_back <- matrix(NA, nrow = length(bp), ncol = 3,
+    dimnames = list(bpnames, colnames(confint_trans)))
+  confint_back[, "est."] <- bp
+
+  for (pname in pnames) {
+    if (!pname %in% f_names_skip) {
+      par.lower <- confint_trans[pname, "lower"]
+      par.upper <- confint_trans[pname, "upper"]
+      names(par.lower) <- names(par.upper) <- pname
+      bpl <- backtransform_odeparms(par.lower, object$mkinmod,
+                                            object$transform_rates,
+                                            object$transform_fractions)
+      bpu <- backtransform_odeparms(par.upper, object$mkinmod,
+                                            object$transform_rates,
+                                            object$transform_fractions)
+      confint_back[names(bpl), "lower"] <- bpl
+      confint_back[names(bpu), "upper"] <- bpu
+    }
+  }
+
+  object$confint_trans <- confint_trans
+  object$confint_ranef <- confint_ranef
+  object$confint_errmod <- confint_errmod
+  object$confint_back <- confint_back
+
+  object$date.summary = date()
+  object$use_of_ff = object$mkinmod$use_of_ff
+  object$error_model_algorithm = object$mmkin_orig[[1]]$error_model_algorithm
+  err_mod = object$mmkin_orig[[1]]$err_mod
+
+  object$diffs <- object$mkinmod$diffs
+  object$print_data <- data
+  so_pred <- object$so@results@predictions
+
+  object$data[["observed"]] <- object$data[["value"]]
+  object$data[["value"]] <- NULL
+  object$data[["predicted"]] <- so_pred$ipred
+  object$data[["residual"]] <- so_pred$ires
+  object$data[["standardized"]] <- so_pred$iwres
+  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$mean_dp_start)
+
+  cat("\nFixed degradation parameter values:\n")
+  if(length(x$fixed$value) == 0) cat("None\n")
+  else print(x$fixed)
+
+  cat("\nResults:\n\n")
+  cat("Likelihood computed by importance sampling\n")
+  print(data.frame(AIC = x$AIC, BIC = x$BIC, logLik = x$logLik,
+      row.names = " "))
+
+  cat("\nOptimised, transformed parameters with symmetric confidence intervals:\n")
+  print(x$confint_trans)
+
+  if (nrow(x$confint_trans) > 1) {
+    corr <- x$corFixed
+    class(corr) <- "correlation"
+    print(corr, title = "\nCorrelation:", ...)
+  }
+
+  cat("\nRandom effects:\n")
+  print(x$confint_ranef)
+
+  cat("\nVariance model:\n")
+  print(x$confint_errmod)
+
+  cat("\nBacktransformed parameters with asymmetric confidence intervals:\n")
+  print(x$confint_back)
+
+  printSFORB <- !is.null(x$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)
+}