diff options
Diffstat (limited to 'R')
| -rw-r--r-- | R/memkin.R | 118 |
1 files changed, 78 insertions, 40 deletions
@@ -6,9 +6,12 @@ #' datasets. #' #' @param object An mmkin row object containing several fits of the same model to different datasets +#' @param random_spec Either "auto" or a specification of random effects for \code{\link{nlme}} +#' given as a character vector #' @param ... Additional arguments passed to \code{\link{nlme}} -#' @importFrom nlme nlme -#' @return A fitted object of class 'memkin' +#' @import nlme +#' @importFrom purrr map_dfr +#' @return An nlme object #' @examples #' sampling_times = c(0, 1, 3, 7, 14, 28, 60, 90, 120) #' m_SFO <- mkinmod(parent = mkinsub("SFO")) @@ -32,9 +35,44 @@ #' #' f <- mmkin("SFO", ds) #' x <- memkin(f) +#' summary(x) #' +#' ds_2 <- lapply(experimental_data_for_UBA_2019[6:10], +#' function(x) x$data[c("name", "time", "value")]) +#' m_sfo_sfo <- mkinmod(parent = mkinsub("SFO", "A1"), +#' A1 = mkinsub("SFO"), use_of_ff = "min") +#' m_sfo_sfo_ff <- mkinmod(parent = mkinsub("SFO", "A1"), +#' A1 = mkinsub("SFO"), use_of_ff = "max") +#' m_fomc_sfo <- mkinmod(parent = mkinsub("FOMC", "A1"), +#' A1 = mkinsub("SFO")) +#' m_dfop_sfo <- mkinmod(parent = mkinsub("DFOP", "A1"), +#' A1 = mkinsub("SFO")) +#' m_sforb_sfo <- mkinmod(parent = mkinsub("SFORB", "A1"), +#' A1 = mkinsub("SFO")) +#' +#' f_2 <- mmkin(list("SFO-SFO" = m_sfo_sfo, +#' "SFO-SFO-ff" = m_sfo_sfo_ff, +#' "FOMC-SFO" = m_fomc_sfo, +#' "DFOP-SFO" = m_dfop_sfo, +#' "SFORB-SFO" = m_sforb_sfo), +#' ds_2) +#' +#' f_nlme_sfo_sfo <- memkin(f_2[1, ]) +#' f_nlme_sfo_sfo_2 <- memkin(f_2[1, ], "pdDiag(parent_0 + log_k_parent_sink + log_k_parent_A1 + log_k_A1_sink ~ 1)") # explicit +#' f_nlme_sfo_sfo_3 <- memkin(f_2[1, ], "pdDiag(parent_0 + log_k_parent_sink + log_k_parent_A1 ~ 1)") # reduced +#' f_nlme_sfo_sfo_4 <- memkin(f_2[1, ], "pdDiag(parent_0 + log_k_parent_sink ~ 1)") # further reduced +#' \dontrun{ +#' f_nlme_sfo_sfo_ff <- memkin(f_2[2, ]) # does not converge with maxIter = 50 +#' } +#' f_nlme_fomc_sfo <- memkin(f_2[3, ]) +#' \dontrun{ +#' f_nlme_dfop_sfo <- memkin(f_2[4, ]) # apparently underdetermined} +#' f_nlme_sforb_sfo <- memkin(f_2[5, ]) # also does not converge +#' } +#' anova(f_nlme_sfo_sfo, f_nlme_fomc_sfo) +#' # The FOMC variant has a lower AIC and has significantly higher likelihood #' @export -memkin <- function(object, ...) { +memkin <- function(object, random_spec = "auto", ...) { if (nrow(object) > 1) stop("Only row objects allowed") ds_names <- colnames(object) @@ -47,6 +85,7 @@ memkin <- function(object, ...) { ds_list <- lapply(object, function(x) x$data[c("time", "variable", "observed")]) names(ds_list) <- ds_names ds_nlme <- purrr::map_dfr(ds_list, function(x) x, .id = "ds") + ds_nlme$variable <- as.character(ds_nlme$variable) ds_nlme_grouped <- groupedData(observed ~ time | ds, ds_nlme) mkin_model <- object[[1]]$mkinmod @@ -56,22 +95,9 @@ memkin <- function(object, ...) { model_function_alist <- replicate(length(p_names_mean_function) + 2, substitute()) names(model_function_alist) <- c("name", "time", p_names_mean_function) - - - model_function_body <- quote({ - arg_frame <- as.data.frame(as.list((environment()))) - res <- parent_0 * exp( - exp(log_k_parent_sink) * time) - dump(c("arg_frame", "res"), file = "out_1.txt", append = TRUE) - return(res) - }) - model_function <- as.function(c(model_function_alist, model_function_body)) - f_nlme <- eval(parse(text = nlme_call_text)) - model_function_body <- quote({ - arg_frame <- as.data.frame(as.list((environment()))) - + arg_frame <- as.data.frame(as.list((environment())), stringsAsFactors = FALSE) res_frame <- arg_frame[1:2] - parm_frame <- arg_frame[-(1:2)] parms_unique <- unique(parm_frame) @@ -87,45 +113,57 @@ memkin <- function(object, ...) { } res_list <- lapply(1:n_unique, function(x) { - parms <- unlist(parms_unique[x, , drop = TRUE]) - odeini_parm_names <- grep('_0$', names(parms), value = TRUE) - odeparm_names <- setdiff(names(parms), odeini_parm_names) - odeini <- parms[odeini_parm_names] - names(odeini) <- gsub('_0$', '', odeini_parm_names) - odeparms <- backtransform_odeparms(parms[odeparm_names], mkin_model) # TBD rates/fractions - out_wide <- mkinpredict(mkin_model, odeparms = odeparms, - solution_type = "analytical", - odeini = odeini, outtimes = unique(times_ds[[x]])) + transparms_optim <- unlist(parms_unique[x, , drop = TRUE]) + parms_fixed <- object[[1]]$bparms.fixed + + odeini_optim_parm_names <- grep('_0$', names(transparms_optim), value = TRUE) + odeini_optim <- transparms_optim[odeini_optim_parm_names] + names(odeini_optim) <- gsub('_0$', '', odeini_optim_parm_names) + odeini_fixed_parm_names <- grep('_0$', names(parms_fixed), value = TRUE) + odeini_fixed <- parms_fixed[odeini_fixed_parm_names] + names(odeini_fixed) <- gsub('_0$', '', odeini_fixed_parm_names) + odeini <- c(odeini_optim, odeini_fixed)[names(mkin_model$diffs)] + + ode_transparms_optim_names <- setdiff(names(transparms_optim), odeini_optim_parm_names) + odeparms_optim <- backtransform_odeparms(transparms_optim[ode_transparms_optim_names], mkin_model, + transform_rates = object[[1]]$transform_rates, + transform_fractions = object[[1]]$transform_fractions) + odeparms_fixed_names <- setdiff(names(parms_fixed), odeini_fixed_parm_names) + odeparms_fixed <- parms_fixed[odeparms_fixed_names] + odeparms <- c(odeparms_optim, odeparms_fixed) + + out_wide <- mkinpredict(mkin_model, + odeparms = odeparms, odeini = odeini, + solution_type = object[[1]]$solution_type, + outtimes = sort(unique(times_ds[[x]]))) out_array <- out_wide[, -1, drop = FALSE] rownames(out_array) <- as.character(unique(times_ds[[x]])) out_times <- as.character(times_ds[[x]]) - out_names <- names_ds[[x]] + out_names <- as.character(names_ds[[x]]) out_values <- mapply(function(times, names) out_array[times, names], out_times, out_names) return(as.numeric(out_values)) }) res <- unlist(res_list) - #dump(c("arg_frame", "res"), file = "out_2.txt", append = TRUE) return(res) }) model_function <- as.function(c(model_function_alist, model_function_body)) - debug(model_function) - f_nlme <- eval(parse(text = nlme_call_text)) - - undebug(model_function) - - model_function(c(0, 0, 100), parent_0 = 100, log_k_parent_sink = log(0.1)) - + # For some reason, using envir = parent.frame() here is not enough, + # we need to use assign + assign("model_function", model_function, envir = parent.frame()) + + random_spec <- if (random_spec[1] == "auto") { + paste0("pdDiag(", paste(p_names_mean_function, collapse = " + "), " ~ 1),\n") + } else { + paste0(random_spec, ",\n") + } nlme_call_text <- paste0( "nlme(observed ~ model_function(variable, time, ", paste(p_names_mean_function, collapse = ", "), "),\n", " data = ds_nlme_grouped,\n", " fixed = ", paste(p_names_mean_function, collapse = " + "), " ~ 1,\n", - " random = pdDiag(", paste(p_names_mean_function, collapse = " + "), " ~ 1),\n", - #" start = c(parent_0 = 100, log_k_parent_sink = log(0.1)), verbose = TRUE)\n") - #" start = p_start_mean_function)\n") - " start = p_start_mean_function, verbose = TRUE)\n") - cat(nlme_call_text) + " random = ", random_spec, "\n", + " start = p_start_mean_function)\n") f_nlme <- eval(parse(text = nlme_call_text)) |