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Diffstat (limited to 'R')
| -rw-r--r-- | R/memkin.R | 170 |
1 files changed, 170 insertions, 0 deletions
diff --git a/R/memkin.R b/R/memkin.R new file mode 100644 index 00000000..8a71484e --- /dev/null +++ b/R/memkin.R @@ -0,0 +1,170 @@ +#' Estimation of parameter distributions from mmkin row objects +#' +#' This function sets up and attempts to fit a mixed effects model to +#' an mmkin row object which is essentially a list of mkinfit objects +#' that have been obtained by fitting the same model to a list of +#' 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}} +#' @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")) +#' d_SFO_1 <- mkinpredict(m_SFO, +#' c(k_parent_sink = 0.1), +#' c(parent = 98), sampling_times) +#' d_SFO_1_long <- mkin_wide_to_long(d_SFO_1, time = "time") +#' d_SFO_2 <- mkinpredict(m_SFO, +#' c(k_parent_sink = 0.05), +#' c(parent = 102), sampling_times) +#' d_SFO_2_long <- mkin_wide_to_long(d_SFO_2, time = "time") +#' d_SFO_3 <- mkinpredict(m_SFO, +#' c(k_parent_sink = 0.02), +#' c(parent = 103), sampling_times) +#' d_SFO_3_long <- mkin_wide_to_long(d_SFO_3, time = "time") +#' +#' d1 <- add_err(d_SFO_1, function(value) 3, n = 1) +#' d2 <- add_err(d_SFO_2, function(value) 2, n = 1) +#' d3 <- add_err(d_SFO_3, function(value) 4, n = 1) +#' ds <- c(d1 = d1, d2 = d2, d3 = d3) +#' +#' f <- mmkin("SFO", ds) +#' x <- memkin(f) +#' summary(x) +#' +#' ds_2 <- lapply(experimental_data_for_UBA_2019[6:10], +#' function(x) x$data[c("name", "time", "value")]) +#' m_sfo_sfo <- mkinmod(parent = mkinsub("SFO", "A1"), +#' A1 = mkinsub("SFO"), use_of_ff = "min") +#' m_sfo_sfo_ff <- mkinmod(parent = mkinsub("SFO", "A1"), +#' A1 = mkinsub("SFO"), use_of_ff = "max") +#' m_fomc_sfo <- mkinmod(parent = mkinsub("FOMC", "A1"), +#' A1 = mkinsub("SFO")) +#' m_dfop_sfo <- mkinmod(parent = mkinsub("DFOP", "A1"), +#' A1 = mkinsub("SFO")) +#' m_sforb_sfo <- mkinmod(parent = mkinsub("SFORB", "A1"), +#' A1 = mkinsub("SFO")) +#' +#' f_2 <- mmkin(list("SFO-SFO" = m_sfo_sfo, +#' "SFO-SFO-ff" = m_sfo_sfo_ff, +#' "FOMC-SFO" = m_fomc_sfo, +#' "DFOP-SFO" = m_dfop_sfo, +#' "SFORB-SFO" = m_sforb_sfo), +#' ds_2) +#' +#' f_nlme_sfo_sfo <- memkin(f_2[1, ]) +#' f_nlme_sfo_sfo_2 <- memkin(f_2[1, ], "pdDiag(parent_0 + log_k_parent_sink + log_k_parent_A1 + log_k_A1_sink ~ 1)") # explicit +#' f_nlme_sfo_sfo_3 <- memkin(f_2[1, ], "pdDiag(parent_0 + log_k_parent_sink + log_k_parent_A1 ~ 1)") # reduced +#' f_nlme_sfo_sfo_4 <- memkin(f_2[1, ], "pdDiag(parent_0 + log_k_parent_sink ~ 1)") # further reduced +#' \dontrun{ +#' f_nlme_sfo_sfo_ff <- memkin(f_2[2, ]) # does not converge with maxIter = 50 +#' } +#' f_nlme_fomc_sfo <- memkin(f_2[3, ]) +#' \dontrun{ +#' f_nlme_dfop_sfo <- memkin(f_2[4, ]) # apparently underdetermined +#' f_nlme_sforb_sfo <- memkin(f_2[5, ]) # also does not converge +#' } +#' anova(f_nlme_fomc_sfo, f_nlme_sfo_sfo, f_nlme_sfo_sfo_4) +#' @export +memkin <- function(object, random_spec = "auto", ...) { + if (nrow(object) > 1) stop("Only row objects allowed") + ds_names <- colnames(object) + + p_mat_start_trans <- sapply(object, parms, transformed = TRUE) + colnames(p_mat_start_trans) <- ds_names + + p_names_mean_function <- setdiff(rownames(p_mat_start_trans), names(object[[1]]$errparms)) + p_start_mean_function <- apply(p_mat_start_trans[p_names_mean_function, ], 1, mean) + + 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 + + # Inspired by https://stackoverflow.com/a/12983961/3805440 + # and https://stackoverflow.com/a/26280789/3805440 + 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())), stringsAsFactors = FALSE) + res_frame <- arg_frame[1:2] + parm_frame <- arg_frame[-(1:2)] + parms_unique <- unique(parm_frame) + + n_unique <- nrow(parms_unique) + + times_ds <- list() + names_ds <- list() + for (i in 1:n_unique) { + times_ds[[i]] <- + arg_frame[which(arg_frame[[3]] == parms_unique[i, 1]), "time"] + names_ds[[i]] <- + arg_frame[which(arg_frame[[3]] == parms_unique[i, 1]), "name"] + } + + res_list <- lapply(1:n_unique, function(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 <- 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) + return(res) + }) + model_function <- as.function(c(model_function_alist, model_function_body)) + # 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 = ", random_spec, "\n", + " start = p_start_mean_function)\n") + + f_nlme <- eval(parse(text = nlme_call_text)) + + return(f_nlme) +} |