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Diffstat (limited to 'R/summary.nlmixr.mmkin.R')
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diff --git a/R/summary.nlmixr.mmkin.R b/R/summary.nlmixr.mmkin.R deleted file mode 100644 index 94d4ed93..00000000 --- a/R/summary.nlmixr.mmkin.R +++ /dev/null @@ -1,250 +0,0 @@ -#' 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) -} |