diff options
Diffstat (limited to 'R')
| -rw-r--r-- | R/mkinerrmin.R | 2 | ||||
| -rw-r--r-- | R/mkinfit.R | 18 | ||||
| -rw-r--r-- | R/mmkin.R | 32 | ||||
| -rw-r--r-- | R/saemix.R | 213 | ||||
| -rw-r--r-- | R/transform_odeparms.R | 61 |
5 files changed, 215 insertions, 111 deletions
diff --git a/R/mkinerrmin.R b/R/mkinerrmin.R index 1994aed0..f52692ba 100644 --- a/R/mkinerrmin.R +++ b/R/mkinerrmin.R @@ -107,7 +107,7 @@ mkinerrmin <- function(fit, alpha = 0.05) if (obs_var == fit$obs_vars[[1]]) { special_parms = c("alpha", "log_alpha", "beta", "log_beta", "k1", "log_k1", "k2", "log_k2", - "g", "g_ilr", "tb", "log_tb") + "g", "g_ilr", "g_qlogis", "tb", "log_tb") n.optim <- n.optim + length(intersect(special_parms, names(parms.optim))) } diff --git a/R/mkinfit.R b/R/mkinfit.R index 1b1bb73d..7fa1c56e 100644 --- a/R/mkinfit.R +++ b/R/mkinfit.R @@ -89,12 +89,11 @@ if(getRversion() >= '2.15.1') utils::globalVariables(c("name", "time", "value")) #' models and the break point tb of the HS model. If FALSE, zero is used as #' a lower bound for the rates in the optimisation. #' @param transform_fractions Boolean specifying if formation fractions -#' constants should be transformed in the model specification used in the -#' fitting for better compliance with the assumption of normal distribution -#' of the estimator. The default (TRUE) is to do transformations. If TRUE, -#' the g parameter of the DFOP and HS models are also transformed, as they -#' can also be seen as compositional data. The transformation used for these -#' transformations is the [ilr()] transformation. +#' should be transformed in the model specification used in the fitting for +#' better compliance with the assumption of normal distribution of the +#' estimator. The default (TRUE) is to do transformations. If TRUE, +#' the g parameter of the DFOP model is also transformed. Transformations +#' are described in [transform_odeparms]. #' @param quiet Suppress printing out the current value of the negative #' log-likelihood after each improvement? #' @param atol Absolute error tolerance, passed to [deSolve::ode()]. Default @@ -187,15 +186,14 @@ if(getRversion() >= '2.15.1') utils::globalVariables(c("name", "time", "value")) #' #' # Fit the model quietly to the FOCUS example dataset D using defaults #' fit <- mkinfit(SFO_SFO, FOCUS_D, quiet = TRUE) -#' # Since mkin 0.9.50.3, we get a warning about non-normality of residuals, -#' # so we try an alternative error model +#' plot_sep(fit) +#' # As lower parent values appear to have lower variance, we try an alternative error model #' fit.tc <- mkinfit(SFO_SFO, FOCUS_D, quiet = TRUE, error_model = "tc") #' # This avoids the warning, and the likelihood ratio test confirms it is preferable #' lrtest(fit.tc, fit) #' # We can also allow for different variances of parent and metabolite as error model #' fit.obs <- mkinfit(SFO_SFO, FOCUS_D, quiet = TRUE, error_model = "obs") -#' # This also avoids the warning about non-normality, but the two-component error model -#' # has significantly higher likelihood +#' # The two-component error model has significantly higher likelihood #' lrtest(fit.obs, fit.tc) #' parms(fit.tc) #' endpoints(fit.tc) @@ -64,8 +64,9 @@ #' #' @export mmkin mmkin <- function(models = c("SFO", "FOMC", "DFOP"), datasets, - cores = detectCores(), cluster = NULL, ...) + cores = parallel::detectCores(), cluster = NULL, ...) { + call <- match.call() parent_models_available = c("SFO", "FOMC", "DFOP", "HS", "SFORB", "IORE", "logistic") n.m <- length(models) n.d <- length(datasets) @@ -100,12 +101,14 @@ mmkin <- function(models = c("SFO", "FOMC", "DFOP"), datasets, } if (is.null(cluster)) { - results <- parallel::mclapply(as.list(1:n.fits), fit_function, mc.cores = cores) + results <- parallel::mclapply(as.list(1:n.fits), fit_function, + mc.cores = cores, mc.preschedule = FALSE) } else { results <- parallel::parLapply(cluster, as.list(1:n.fits), fit_function) } attributes(results) <- attributes(fit_indices) + attr(results, "call") <- call class(results) <- "mmkin" return(results) } @@ -187,3 +190,28 @@ print.mmkin <- function(x, ...) { } } + +#' @export +update.mmkin <- function(object, ..., evaluate = TRUE) +{ + call <- attr(object, "call") + + update_arguments <- match.call(expand.dots = FALSE)$... + + if (length(update_arguments) > 0) { + update_arguments_in_call <- !is.na(match(names(update_arguments), names(call))) + } + + for (a in names(update_arguments)[update_arguments_in_call]) { + call[[a]] <- update_arguments[[a]] + } + + update_arguments_not_in_call <- !update_arguments_in_call + if(any(update_arguments_not_in_call)) { + call <- c(as.list(call), update_arguments[update_arguments_not_in_call]) + call <- as.call(call) + } + + if(evaluate) eval(call, parent.frame()) + else call +} @@ -20,47 +20,40 @@ #' @importFrom saemix saemixData saemixModel #' @importFrom stats var #' @examples +#' \dontrun{ +#' library(saemix) #' ds <- lapply(experimental_data_for_UBA_2019[6:10], #' function(x) subset(x$data[c("name", "time", "value")])) #' names(ds) <- paste("Dataset", 6:10) -#' sfo_sfo <- mkinmod(parent = mkinsub("SFO", "A1"), +#' f_mmkin_parent_p0_fixed <- mmkin("FOMC", ds, cores = 1, +#' state.ini = c(parent = 100), fixed_initials = "parent", quiet = TRUE) +#' m_saemix_p0_fixed <- saemix_model(f_mmkin_parent_p0_fixed["FOMC", ]) +#' d_saemix_parent <- saemix_data(f_mmkin_parent_p0_fixed) +#' saemix_options <- list(seed = 123456, displayProgress = FALSE, +#' save = FALSE, save.graphs = FALSE, nbiter.saemix = c(200, 80)) +#' f_saemix_p0_fixed <- saemix(m_saemix_p0_fixed, d_saemix_parent, saemix_options) +#' +#' f_mmkin_parent <- mmkin(c("SFO", "FOMC", "DFOP"), ds, quiet = TRUE) +#' m_saemix_sfo <- saemix_model(f_mmkin_parent["SFO", ]) +#' m_saemix_fomc <- saemix_model(f_mmkin_parent["FOMC", ]) +#' m_saemix_dfop <- saemix_model(f_mmkin_parent["DFOP", ]) +#' d_saemix_parent <- saemix_data(f_mmkin_parent["SFO", ]) +#' f_saemix_sfo <- saemix(m_saemix_sfo, d_saemix_parent, saemix_options) +#' f_saemix_fomc <- saemix(m_saemix_fomc, d_saemix_parent, saemix_options) +#' f_saemix_dfop <- saemix(m_saemix_dfop, d_saemix_parent, saemix_options) +#' compare.saemix(list(f_saemix_sfo, f_saemix_fomc, f_saemix_dfop)) +#' f_mmkin_parent_tc <- update(f_mmkin_parent, error_model = "tc") +#' m_saemix_fomc_tc <- saemix_model(f_mmkin_parent_tc["FOMC", ]) +#' f_saemix_fomc_tc <- saemix(m_saemix_fomc_tc, d_saemix_parent, saemix_options) +#' compare.saemix(list(f_saemix_fomc, f_saemix_fomc_tc)) +#' +#' dfop_sfo <- mkinmod(parent = mkinsub("DFOP", "A1"), #' A1 = mkinsub("SFO")) -#' \dontrun{ -#' f_mmkin <- mmkin(list("SFO-SFO" = sfo_sfo), ds, quiet = TRUE) -#' library(saemix) -#' m_saemix <- saemix_model(f_mmkin, cores = 1) +#' f_mmkin <- mmkin(list("DFOP-SFO" = dfop_sfo), ds, quiet = TRUE) +#' m_saemix <- saemix_model(f_mmkin) #' d_saemix <- saemix_data(f_mmkin) -#' saemix_options <- list(seed = 123456, -#' save = FALSE, save.graphs = FALSE, displayProgress = FALSE, -#' nbiter.saemix = c(200, 80)) #' f_saemix <- saemix(m_saemix, d_saemix, saemix_options) -#' plot(f_saemix, plot.type = "convergence") -#' } -#' # Synthetic data with two-component error -#' sampling_times = c(0, 1, 3, 7, 14, 28, 60, 90, 120) -#' dt50_sfo_in <- c(80, 90, 100, 111.111, 125) -#' k_in <- log(2) / dt50_sfo_in #' -#' SFO <- mkinmod(parent = mkinsub("SFO")) -#' -#' pred_sfo <- function(k) { -#' mkinpredict(SFO, c(k_parent = k), -#' c(parent = 100), sampling_times) -#' } -#' -#' ds_sfo_mean <- lapply(k_in, pred_sfo) -#' set.seed(123456L) -#' ds_sfo_syn <- lapply(ds_sfo_mean, function(ds) { -#' add_err(ds, sdfunc = function(value) sqrt(1^2 + value^2 * 0.07^2), -#' n = 1)[[1]] -#' }) -#' \dontrun{ -#' f_mmkin_syn <- mmkin("SFO", ds_sfo_syn, error_model = "tc", quiet = TRUE) -#' # plot(f_mmkin_syn) -#' m_saemix_tc <- saemix_model(f_mmkin_syn, cores = 1) -#' d_saemix_tc <- saemix_data(f_mmkin_syn) -#' f_saemix_tc <- saemix(m_saemix_tc, d_saemix_tc, saemix_options) -#' plot(f_saemix_tc, plot.type = "convergence") #' } #' @return An [saemix::SaemixModel] object. #' @export @@ -68,14 +61,14 @@ saemix_model <- function(object, cores = 1) { if (nrow(object) > 1) stop("Only row objects allowed") mkin_model <- object[[1]]$mkinmod - analytical <- is.function(mkin_model$deg_func) + solution_type <- object[[1]]$solution_type degparms_optim <- mean_degparms(object) - psi0 <- matrix(degparms_optim, nrow = 1) - colnames(psi0) <- names(degparms_optim) - degparms_fixed <- object[[1]]$bparms.fixed + # Transformations are done in the degradation function + transform.par = rep(0, length(degparms_optim)) + odeini_optim_parm_names <- grep('_0$', names(degparms_optim), value = TRUE) odeini_fixed_parm_names <- grep('_0$', names(degparms_fixed), value = TRUE) @@ -85,50 +78,114 @@ saemix_model <- function(object, cores = 1) { odeini_fixed <- degparms_fixed[odeini_fixed_parm_names] names(odeini_fixed) <- gsub('_0$', '', odeini_fixed_parm_names) - model_function <- function(psi, id, xidep) { + model_function <- FALSE + + if (length(mkin_model$spec) == 1 & mkin_model$use_of_ff == "max") { + parent_type <- mkin_model$spec[[1]]$type + if (length(odeini_fixed) == 1) { + if (parent_type == "SFO") { + stop("saemix needs at least two parameters to work on.") + } + if (parent_type == "FOMC") { + model_function <- function(psi, id, xidep) { + odeini_fixed / (xidep[, "time"]/exp(psi[id, 2]) + 1)^exp(psi[id, 1]) + } + } + if (parent_type == "DFOP") { + model_function <- function(psi, id, xidep) { + g <- plogis(psi[id, 3]) + t = xidep[, "time"] + odeini_fixed * (g * exp(- exp(psi[id, 1]) * t) + + (1 - g) * exp(- exp(psi[id, 2]) * t)) + } + } + if (parent_type == "HS") { + model_function <- function(psi, id, xidep) { + tb <- exp(psi[id, 3]) + t = xidep[, "time"] + k1 = exp(psi[id, 1]) + odeini_fixed * ifelse(t <= tb, + exp(- k1 * t), + exp(- k1 * t) * exp(- exp(psi[id, 2]) * (t - tb))) + } + } + } else { + if (length(odeparms_fixed) == 0) { + if (parent_type == "SFO") { + model_function <- function(psi, id, xidep) { + psi[id, 1] * exp( - exp(psi[id, 2]) * xidep[, "time"]) + } + } + if (parent_type == "FOMC") { + model_function <- function(psi, id, xidep) { + psi[id, 1] / (xidep[, "time"]/exp(psi[id, 3]) + 1)^exp(psi[id, 2]) + } + } + if (parent_type == "DFOP") { + model_function <- function(psi, id, xidep) { + g <- plogis(psi[id, 4]) + t = xidep[, "time"] + psi[id, 1] * (g * exp(- exp(psi[id, 2]) * t) + + (1 - g) * exp(- exp(psi[id, 3]) * t)) + } + } + if (parent_type == "HS") { + model_function <- function(psi, id, xidep) { + tb <- exp(psi[id, 4]) + t = xidep[, "time"] + k1 = exp(psi[id, 2]) + psi[id, 1] * ifelse(t <= tb, + exp(- k1 * t), + exp(- k1 * t) * exp(- exp(psi[id, 3]) * (t - tb))) + } + } + } + } + } - uid <- unique(id) + if (!is.function(model_function)) { + model_function <- function(psi, id, xidep) { - res_list <- parallel::mclapply(uid, function(i) { - transparms_optim <- psi[i, ] - names(transparms_optim) <- names(degparms_optim) + uid <- unique(id) - odeini_optim <- transparms_optim[odeini_optim_parm_names] - names(odeini_optim) <- gsub('_0$', '', odeini_optim_parm_names) + res_list <- parallel::mclapply(uid, function(i) { + transparms_optim <- psi[i, ] + names(transparms_optim) <- names(degparms_optim) - odeini <- c(odeini_optim, odeini_fixed)[names(mkin_model$diffs)] + odeini_optim <- transparms_optim[odeini_optim_parm_names] + names(odeini_optim) <- gsub('_0$', '', odeini_optim_parm_names) - ode_transparms_optim_names <- setdiff(names(transparms_optim), odeini_optim_parm_names) - odeparms_optim <- backtransform_odeparms(transparms_optim[ode_transparms_optim_names], mkin_model, - transform_rates = object[[1]]$transform_rates, - transform_fractions = object[[1]]$transform_fractions) - odeparms <- c(odeparms_optim, odeparms_fixed) + odeini <- c(odeini_optim, odeini_fixed)[names(mkin_model$diffs)] - xidep_i <- subset(xidep, id == i) + ode_transparms_optim_names <- setdiff(names(transparms_optim), odeini_optim_parm_names) + odeparms_optim <- backtransform_odeparms(transparms_optim[ode_transparms_optim_names], mkin_model, + transform_rates = object[[1]]$transform_rates, + transform_fractions = object[[1]]$transform_fractions) + odeparms <- c(odeparms_optim, odeparms_fixed) - if (analytical) { - out_values <- mkin_model$deg_func(xidep_i, odeini, odeparms) - } else { + xidep_i <- subset(xidep, id == i) - i_time <- xidep_i$time - i_name <- xidep_i$name + if (solution_type == "analytical") { + out_values <- mkin_model$deg_func(xidep_i, odeini, odeparms) + } else { - out_wide <- mkinpredict(mkin_model, - odeparms = odeparms, odeini = odeini, - solution_type = object[[1]]$solution_type, - outtimes = sort(unique(i_time))) + i_time <- xidep_i$time + i_name <- xidep_i$name - out_index <- cbind(as.character(i_time), as.character(i_name)) - out_values <- out_wide[out_index] - } - return(out_values) - }, mc.cores = cores) - res <- unlist(res_list) - return(res) - } + out_wide <- mkinpredict(mkin_model, + odeparms = odeparms, odeini = odeini, + solution_type = solution_type, + outtimes = sort(unique(i_time))) - raneff_0 <- mean_degparms(object, random = TRUE)$random$ds - var_raneff_0 <- apply(raneff_0, 2, var) + out_index <- cbind(as.character(i_time), as.character(i_name)) + out_values <- out_wide[out_index] + } + return(out_values) + }, mc.cores = cores) + res <- unlist(res_list) + return(res) + } + } error.model <- switch(object[[1]]$err_mod, const = "constant", @@ -136,7 +193,7 @@ saemix_model <- function(object, cores = 1) { obs = "constant") if (object[[1]]$err_mod == "obs") { - warning("The error model 'obs' (variance by variable) was not transferred to the saemix model") + warning("The error model 'obs' (variance by variable) can currently not be transferred to an saemix model") } error.init <- switch(object[[1]]$err_mod, @@ -145,11 +202,15 @@ saemix_model <- function(object, cores = 1) { b = mean(sapply(object, function(x) x$errparms[2]))), obs = c(a = mean(sapply(object, function(x) x$errparms)), b = 1)) - res <- saemixModel(model_function, psi0, + psi0_matrix <- matrix(degparms_optim, nrow = 1) + colnames(psi0_matrix) <- names(degparms_optim) + + res <- saemixModel(model_function, + psi0 = psi0_matrix, "Mixed model generated from mmkin object", - transform.par = rep(0, length(degparms_optim)), - error.model = error.model, error.init = error.init, - omega.init = diag(var_raneff_0) + transform.par = transform.par, + error.model = error.model, + error.init = error.init ) return(res) } diff --git a/R/transform_odeparms.R b/R/transform_odeparms.R index 0a25ee8c..f21d31fc 100644 --- a/R/transform_odeparms.R +++ b/R/transform_odeparms.R @@ -5,19 +5,19 @@ #' constants and other parameters that can only take on positive values, a #' simple log transformation is used. For compositional parameters, such as the #' formations fractions that should always sum up to 1 and can not be negative, -#' the \code{\link{ilr}} transformation is used. +#' the [ilr] transformation is used. #' #' The transformation of sets of formation fractions is fragile, as it supposes #' the same ordering of the components in forward and backward transformation. -#' This is no problem for the internal use in \code{\link{mkinfit}}. +#' This is no problem for the internal use in [mkinfit]. #' #' @param parms Parameters of kinetic models as used in the differential #' equations. #' @param transparms Transformed parameters of kinetic models as used in the #' fitting procedure. -#' @param mkinmod The kinetic model of class \code{\link{mkinmod}}, containing +#' @param mkinmod The kinetic model of class [mkinmod], containing #' the names of the model variables that are needed for grouping the -#' formation fractions before \code{\link{ilr}} transformation, the parameter +#' formation fractions before [ilr] transformation, the parameter #' names and the information if the pathway to sink is included in the model. #' @param transform_rates Boolean specifying if kinetic rate constants should #' be transformed in the model specification used in the fitting for better @@ -28,11 +28,15 @@ #' @param transform_fractions Boolean specifying if formation fractions #' constants should be transformed in the model specification used in the #' fitting for better compliance with the assumption of normal distribution -#' of the estimator. The default (TRUE) is to do transformations. The g -#' parameter of the DFOP and HS models are also transformed, as they can also -#' be seen as compositional data. The transformation used for these -#' transformations is the \code{\link{ilr}} transformation. +#' of the estimator. The default (TRUE) is to do transformations. +#' The g parameter of the DFOP model is also seen as a fraction. +#' If a single fraction is transformed (g parameter of DFOP or only a single +#' target variable e.g. a single metabolite plus a pathway to sink), a +#' logistic transformation is used [stats::qlogis()]. In other cases, i.e. if +#' two or more formation fractions need to be transformed whose sum cannot +#' exceed one, the [ilr] transformation is used. #' @return A vector of transformed or backtransformed parameters +#' @importFrom stats plogis qlogis #' @author Johannes Ranke #' @examples #' @@ -91,8 +95,7 @@ #' #' @export transform_odeparms transform_odeparms <- function(parms, mkinmod, - transform_rates = TRUE, - transform_fractions = TRUE) + transform_rates = TRUE, transform_fractions = TRUE) { # We need the model specification for the names of the model # variables and the information on the sink @@ -119,8 +122,7 @@ transform_odeparms <- function(parms, mkinmod, N <- parms[grep("^N", names(parms))] transparms[names(N)] <- N - # Go through state variables and apply isometric logratio transformation to - # formation fractions if requested + # Go through state variables and transform formation fractions if requested mod_vars = names(spec) for (box in mod_vars) { f <- parms[grep(paste("^f", box, sep = "_"), names(parms))] @@ -128,9 +130,14 @@ transform_odeparms <- function(parms, mkinmod, if (length(f) > 0) { if(transform_fractions) { if (spec[[box]]$sink) { - trans_f <- ilr(c(f, 1 - sum(f))) - trans_f_names <- paste("f", box, "ilr", 1:length(trans_f), sep = "_") - transparms[trans_f_names] <- trans_f + if (length(f) == 1) { + trans_f_name <- paste("f", box, "qlogis", sep = "_") + transparms[trans_f_name] <- qlogis(f) + } else { + trans_f <- ilr(c(f, 1 - sum(f))) + trans_f_names <- paste("f", box, "ilr", 1:length(trans_f), sep = "_") + transparms[trans_f_names] <- trans_f + } } else { if (length(f) > 1) { trans_f <- ilr(f) @@ -161,7 +168,7 @@ transform_odeparms <- function(parms, mkinmod, if (!is.na(parms["g"])) { g <- parms["g"] if (transform_fractions) { - transparms["g_ilr"] <- ilr(c(g, 1 - g)) + transparms["g_qlogis"] <- qlogis(g) } else { transparms["g"] <- g } @@ -207,20 +214,25 @@ backtransform_odeparms <- function(transparms, mkinmod, N <- transparms[grep("^N", names(transparms))] parms[names(N)] <- N - # Go through state variables and apply inverse isometric logratio transformation + # Go through state variables and apply inverse transformations to formation fractions mod_vars = names(spec) for (box in mod_vars) { # Get the names as used in the model f_names = grep(paste("^f", box, sep = "_"), mkinmod$parms, value = TRUE) + # Get the formation fraction parameters trans_f = transparms[grep(paste("^f", box, sep = "_"), names(transparms))] if (length(trans_f) > 0) { if(transform_fractions) { - f <- invilr(trans_f) - if (spec[[box]]$sink) { - parms[f_names] <- f[1:length(f)-1] + if (any(grepl("qlogis", names(trans_f)))) { + parms[f_names] <- plogis(trans_f) } else { - parms[f_names] <- f + f <- invilr(trans_f) + if (spec[[box]]$sink) { + parms[f_names] <- f[1:length(f)-1] + } else { + parms[f_names] <- f + } } } else { parms[names(trans_f)] <- trans_f @@ -242,7 +254,12 @@ backtransform_odeparms <- function(transparms, mkinmod, } } - # DFOP parameter g is treated as a fraction + # DFOP parameter g is now transformed using qlogis + if (!is.na(transparms["g_qlogis"])) { + g_qlogis <- transparms["g_qlogis"] + parms["g"] <- plogis(g_qlogis) + } + # In earlier times we used ilr for g, so we keep this around if (!is.na(transparms["g_ilr"])) { g_ilr <- transparms["g_ilr"] parms["g"] <- invilr(g_ilr)[1] |