diff options
Diffstat (limited to 'R')
| -rw-r--r-- | R/mhmkin.R | 91 |
1 files changed, 71 insertions, 20 deletions
@@ -14,11 +14,12 @@ #' supported #' @param no_random_effect Default is NULL and will be passed to [saem]. If a #' character vector is supplied, it will be passed to all calls to [saem], -#' regardless if the corresponding parameter is in the model. Alternatively, -#' an object of class [illparms.mhmkin] can be specified. This has to have -#' the same dimensions as the return object of the current call. In this way, -#' ill-defined parameters found in corresponding simpler versions of the -#' degradation model can be specified. +#' which will exclude random effects for all matching parameters. Alternatively, +#' a list of character vectors or an object of class [illparms.mhmkin] can be +#' specified. They have to have the same dimensions that the return object of +#' the current call will have, i.e. the number of rows must match the number +#' of degradation models in the mmkin object(s), and the number of columns must +#' match the number of error models used in the mmkin object(s). #' @param algorithm The algorithm to be used for fitting (currently not used) #' @param \dots Further arguments that will be passed to the nonlinear mixed-effects #' model fitting function. @@ -50,6 +51,42 @@ mhmkin.mmkin <- function(objects, ...) { #' @export #' @rdname mhmkin +#' @examples +#' \dontrun{ +#' # We start with separate evaluations of all the first six datasets with two +#' # degradation models and two error models +#' f_sep_const <- mmkin(c("SFO", "FOMC"), ds_fomc[1:6], cores = 2, quiet = TRUE) +#' f_sep_tc <- update(f_sep_const, error_model = "tc") +#' # The mhmkin function sets up hierarchical degradation models aka +#' # nonlinear mixed-effects models for all four combinations, specifying +#' # uncorrelated random effects for all degradation parameters +#' f_saem_1 <- mhmkin(list(f_sep_const, f_sep_tc), cores = 2) +#' status(f_saem_1) +#' # The 'illparms' function shows that in all hierarchical fits, at least +#' # one random effect is ill-defined (the confidence interval for the +#' # random effect expressed as standard deviation includes zero) +#' illparms(f_saem_1) +#' # Therefore we repeat the fits, excluding the ill-defined random effects +#' f_saem_2 <- update(f_saem_1, no_random_effect = illparms(f_saem_1)) +#' status(f_saem_2) +#' illparms(f_saem_2) +#' # Model comparisons show that FOMC with two-component error is preferable, +#' # and confirms our reduction of the default parameter model +#' anova(f_saem_1) +#' anova(f_saem_2) +#' # The convergence plot for the selected model looks fine +#' saemix::plot(f_saem_2[["FOMC", "tc"]]$so, plot.type = "convergence") +#' # The plot of predictions versus data shows that we have a pretty data-rich +#' # situation with homogeneous distribution of residuals, because we used the +#' # same degradation model, error model and parameter distribution model that +#' # was used in the data generation. +#' plot(f_saem_2[["FOMC", "tc"]]) +#' # We can specify the same parameter model reductions manually +#' no_ranef <- list("parent_0", "log_beta", "parent_0", c("parent_0", "log_beta")) +#' dim(no_ranef) <- c(2, 2) +#' f_saem_2m <- update(f_saem_1, no_random_effect = no_ranef) +#' anova(f_saem_2m) +#' } mhmkin.list <- function(objects, backend = "saemix", algorithm = "saem", no_random_effect = NULL, ..., @@ -97,25 +134,38 @@ mhmkin.list <- function(objects, backend = "saemix", algorithm = "saem", dimnames(fit_indices) <- list(degradation = names(deg_models), error = error_models) - fit_function <- function(fit_index) { - w <- which(fit_indices == fit_index, arr.ind = TRUE) - deg_index <- w[1] - error_index <- w[2] - mmkin_row <- objects[[error_index]][deg_index, ] + if (is.null(no_random_effect) || length(dim(no_random_effect)) == 1) { + no_ranef <- rep(list(no_random_effect), n.fits) + dim(no_ranef) <- dim(fit_indices) + } else { + if (!identical(dim(no_random_effect), dim(fit_indices))) { + stop("Dimensions of argument 'no_random_effect' are not suitable") + } if (is(no_random_effect, "illparms.mhmkin")) { - if (identical(dim(no_random_effect), dim(fit_indices))) { - no_ranef_split <- strsplit(no_random_effect[[fit_index]], ", ") - no_ranef <- sapply(no_ranef_split, function(x) { - gsub("sd\\((.*)\\)", "\\1", x) + no_ranef_dim <- dim(no_random_effect) + no_ranef <- lapply(no_random_effect, function(x) { + no_ranef_split <- strsplit(x, ", ") + ret <- sapply(no_ranef_split, function(y) { + gsub("sd\\((.*)\\)", "\\1", y) }) - } else { - stop("Dimensions of illparms.mhmkin object given as 'no_random_effect' are not suitable") - } + return(ret) + }) + dim(no_ranef) <- no_ranef_dim } else { no_ranef <- no_random_effect } + } + + fit_function <- function(fit_index) { + w <- which(fit_indices == fit_index, arr.ind = TRUE) + deg_index <- w[1] + error_index <- w[2] + mmkin_row <- objects[[error_index]][deg_index, ] res <- try(do.call(backend_function, - args = c(list(mmkin_row), dot_args, list(no_random_effect = no_ranef)))) + args = c( + list(mmkin_row), + dot_args, + list(no_random_effect = no_ranef[[deg_index, error_index]])))) return(res) } @@ -145,15 +195,16 @@ mhmkin.list <- function(objects, backend = "saemix", algorithm = "saem", #' @param j Column index selecting the fits to specific datasets #' @param drop If FALSE, the method always returns an mhmkin object, otherwise #' either a list of fit objects or a single fit object. -#' @return An object of class \code{\link{mhmkin}}. +#' @return An object inheriting from \code{\link{mhmkin}}. #' @rdname mhmkin #' @export `[.mhmkin` <- function(x, i, j, ..., drop = FALSE) { + original_class <- class(x) class(x) <- NULL x_sub <- x[i, j, drop = drop] if (!drop) { - class(x_sub) <- "mhmkin" + class(x_sub) <- original_class } return(x_sub) } |