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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.
-#'
-#' @importFrom stats confint sd
-#' @param object an object of class [nlmixr.mmkin]
-#' @param x an object of class [summary.nlmixr.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_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
-#' 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_sfo, data = TRUE)
-#' }
-#'
-#' @export
-summary.nlmixr.mmkin <- function(object, data = FALSE, verbose = FALSE, distimes = TRUE, ...) {
-
- mod_vars <- names(object$mkinmod$diffs)
-
- conf.int <- confint(object$nm)
- dpnames <- setdiff(rownames(conf.int), names(object$mean_ep_start))
- ndp <- length(dpnames)
-
- confint_trans <- as.matrix(conf.int[dpnames, c(1, 3, 4)])
- colnames(confint_trans) <- c("est.", "lower", "upper")
-
- bp <- backtransform_odeparms(confint_trans[, "est."], object$mkinmod,
- object$transform_rates, object$transform_fractions)
- bpnames <- names(bp)
-
- # Transform boundaries of CI for one parameter at a time,
- # with the exception of sets of formation fractions (single fractions are OK).
- f_names_skip <- character(0)
- for (box in mod_vars) { # Figure out sets of fractions to skip
- f_names <- grep(paste("^f", box, sep = "_"), dpnames, value = TRUE)
- n_paths <- length(f_names)
- if (n_paths > 1) f_names_skip <- c(f_names_skip, f_names)
- }
-
- confint_back <- matrix(NA, nrow = length(bp), ncol = 3,
- dimnames = list(bpnames, colnames(confint_trans)))
- confint_back[, "est."] <- bp
-
- for (pname in dpnames) {
- if (!pname %in% f_names_skip) {
- par.lower <- confint_trans[pname, "lower"]
- par.upper <- confint_trans[pname, "upper"]
- names(par.lower) <- names(par.upper) <- pname
- bpl <- backtransform_odeparms(par.lower, object$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$corFixed <- array(
- t(varFix/stdFix)/stdFix,
- dim(varFix),
- list(dpnames, dpnames))
-
- object$confint_trans <- confint_trans
- object$confint_back <- confint_back
-
- object$date.summary = date()
- object$use_of_ff = object$mkinmod$use_of_ff
-
- object$diffs <- object$mkinmod$diffs
- object$print_data <- data # boolean: Should we print the data?
-
- names(object$data)[4] <- "observed" # rename value to observed
-
- object$verbose <- verbose
-
- object$fixed <- object$mmkin_orig[[1]]$fixed
- object$AIC = AIC(object$nm)
- object$BIC = BIC(object$nm)
- object$logLik = logLik(object$nm)
-
- 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.nlmixr.mmkin")
- return(object)
-}
-
-#' @rdname summary.nlmixr.mmkin
-#' @export
-print.summary.nlmixr.mmkin <- function(x, digits = max(3, getOption("digits") - 3), verbose = x$verbose, ...) {
- cat("nlmixr version used for fitting: ", x$nlmixrversion, "\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$ds)), "datasets\n")
-
- cat("\nDegradation model predictions using RxODE\n")
-
- cat("\nFitted in", x$time[["elapsed"]], "s\n")
-
- cat("\nVariance model: ")
- cat(switch(x$err_mod,
- const = "Constant variance",
- obs = "Variance unique to each observed variable",
- tc = "Two-component variance function",
- obs_tc = "Two-component variance unique to each observed variable"), "\n")
-
- cat("\nMean of starting values for individual parameters:\n")
- print(x$mean_dp_start, digits = digits)
-
- cat("\nMean of starting values for error model parameters:\n")
- print(x$mean_ep_start, digits = digits)
-
- cat("\nFixed degradation parameter values:\n")
- if(length(x$fixed$value) == 0) cat("None\n")
- else print(x$fixed, digits = digits)
-
- cat("\nResults:\n\n")
- cat("Likelihood calculated by", nlmixr::getOfvType(x$nm), " \n")
- print(data.frame(AIC = x$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$corFixed
- class(corr) <- "correlation"
- print(corr, title = "\nCorrelation:", rdig = digits, ...)
- }
-
- cat("\nRandom effects (omega):\n")
- print(x$nm$omega, digits = digits)
-
- cat("\nVariance model:\n")
- print(x$nm$sigma, digits = digits)
-
- cat("\nBacktransformed parameters:\n")
- print(x$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)
-}

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