#' 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)
}
