Prepare for v1.0.0v1.0.0

- Improve authorship and copyright information
- Prepare pkgdown config
- Remove dependence on saemix as we need the development version which
is not ready for CRAN
- Temporarily remove saemix interface to check code coverage of the rest

1 files changed, 0 insertions, 268 deletions

diff --git a/R/summary.saem.mmkin.R b/R/summary.saem.mmkin.R
deleted file mode 100644
index e92c561c..00000000
--- a/R/summary.saem.mmkin.R
+++ /dev/null
@@ -1,268 +0,0 @@
-#' 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{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
-#'   saemix authors for the parts inherited from saemix.
-#' @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 saem
-#' f_mmkin_dfop_sfo <- mmkin(list(dfop_sfo), ds_syn_dfop_sfo,
-#'   quiet = TRUE, error_model = "tc", cores = 5)
-#' f_saem_dfop_sfo <- saem(f_mmkin_dfop_sfo)
-#' summary(f_saem_dfop_sfo, 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$name
-  confint_trans <- as.matrix(conf.int[pnames, c("estimate", "lower", "upper")])
-  colnames(confint_trans)[1] <- "est."
-
-  # In case objects were produced by earlier versions of saem
-  if (is.null(object$transformations)) object$transformations <- "mkin"
-
-  if (object$transformations == "mkin") {
-    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$transform_fractions)
-        confint_back[names(bpl), "lower"] <- bpl
-        confint_back[names(bpu), "upper"] <- bpu
-      }
-    }
-  } else {
-    confint_back <- confint_trans
-  }
-
-  #  Correlation of fixed effects (inspired by summary.nlme)
-  varFix <- vcov(object$so)[1:np, 1:np]
-  stdFix <- sqrt(diag(varFix))
-  object$corFixed <- array(
-    t(varFix/stdFix)/stdFix,
-    dim(varFix),
-    list(pnames, pnames))
-
-  # Random effects
-  rnames <- paste0("SD.", pnames)
-  confint_ranef <- as.matrix(conf.int[rnames, c("estimate", "lower", "upper")])
-  colnames(confint_ranef)[1] <- "est."
-
-  # Error model
-  enames <- if (object$err_mod == "const") "a.1" else c("a.1", "b.1")
-  confint_errmod <- as.matrix(conf.int[enames, c("estimate", "lower", "upper")])
-  colnames(confint_errmod)[1] <- "est."
-
-
-  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$print_data <- data # boolean: Should we print the data?
-  so_pred <- object$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)
-}