From 68f5f5c17e3e1c3f9272b9b663a4d7380433b530 Mon Sep 17 00:00:00 2001 From: Johannes Ranke Date: Sat, 4 Apr 2020 16:46:37 +0200 Subject: Add three functions to facilitate the use of nlme --- R/memkin.R | 170 ------------------------------------------------ R/mkinsub.R | 11 ++-- R/nlme.R | 213 ++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++ 3 files changed, 219 insertions(+), 175 deletions(-) delete mode 100644 R/memkin.R create mode 100644 R/nlme.R (limited to 'R') diff --git a/R/memkin.R b/R/memkin.R deleted file mode 100644 index 8a71484e..00000000 --- a/R/memkin.R +++ /dev/null @@ -1,170 +0,0 @@ -#' 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) -} diff --git a/R/mkinsub.R b/R/mkinsub.R index db91ca00..f87c230a 100644 --- a/R/mkinsub.R +++ b/R/mkinsub.R @@ -27,11 +27,12 @@ #' parent = mkinsub("SFO", "m1"), #' m1 = mkinsub("SFO")) #' -#' # Now supplying full names -#' SFO_SFO.2 <- mkinmod( -#' parent = mkinsub("SFO", "m1", full_name = "Test compound"), -#' m1 = mkinsub("SFO", full_name = "Metabolite M1")) -#' +#' \dontrun{ +#' # Now supplying full names +#' SFO_SFO.2 <- mkinmod( +#' parent = mkinsub("SFO", "m1", full_name = "Test compound"), +#' m1 = mkinsub("SFO", full_name = "Metabolite M1")) +#' } #' @export mkinsub <- function(submodel, to = NULL, sink = TRUE, full_name = NA) { diff --git a/R/nlme.R b/R/nlme.R new file mode 100644 index 00000000..b17fe15a --- /dev/null +++ b/R/nlme.R @@ -0,0 +1,213 @@ +#' 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. An mmkin row object 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 +#' @import nlme +#' @importFrom purrr map_dfr +#' @return A named vector containing mean values of the fitted degradation model parameters +#' @rdname nlme +#' @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, cores = 1, quiet = TRUE) +#' mean_dp <- mean_degparms(f) +#' grouped_data <- nlme_data(f) +#' nlme_f <- nlme_function(f) +#' +#' library(nlme) +#' m_nlme <- nlme(value ~ nlme_f(name, time, parent_0, log_k_parent_sink), +#' data = grouped_data, +#' fixed = parent_0 + log_k_parent_sink ~ 1, +#' random = pdDiag(parent_0 + log_k_parent_sink ~ 1), +#' start = mean_dp) +#' summary(m_nlme) +#' +#' \dontrun{ +#' Test on some real data +#' 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) +#' +#' grouped_data_2 <- nlme_data(f_2["SFO-SFO", ]) +#' +#' mean_dp_sfo_sfo <- mean_degparms(f_2["SFO-SFO", ]) +#' mean_dp_sfo_sfo_ff <- mean_degparms(f_2["SFO-SFO-ff", ]) +#' mean_dp_fomc_sfo <- mean_degparms(f_2["FOMC-SFO", ]) +#' mean_dp_dfop_sfo <- mean_degparms(f_2["DFOP-SFO", ]) +#' mean_dp_sforb_sfo <- mean_degparms(f_2["SFORB-SFO", ]) +#' +#' nlme_f_sfo_sfo <- nlme_function(f_2["SFO-SFO", ]) +#' nlme_f_sfo_sfo_ff <- nlme_function(f_2["SFO-SFO-ff", ]) +#' nlme_f_fomc_sfo <- nlme_function(f_2["FOMC-SFO", ]) +#' +#' # Allowing for correlations between random effects leads to non-convergence +#' f_nlme_sfo_sfo <- nlme(value ~ nlme_f_sfo_sfo(name, time, +#' parent_0, log_k_parent_sink, log_k_parent_A1, log_k_A1_sink), +#' data = grouped_data_2, +#' fixed = parent_0 + log_k_parent_sink + log_k_parent_A1 + log_k_A1_sink ~ 1, +#' random = pdDiag(parent_0 + log_k_parent_sink + log_k_parent_A1 + log_k_A1_sink ~ 1), +#' start = mean_dp_sfo_sfo) +#' +#' # The same model fitted with transformed formation fractions does not converge +#' f_nlme_sfo_sfo_ff <- nlme(value ~ nlme_f_sfo_sfo_ff(name, time, +#' parent_0, log_k_parent, log_k_A1, f_parent_ilr_1), +#' data = grouped_data_2, +#' fixed = parent_0 + log_k_parent + log_k_A1 + f_parent_ilr_1 ~ 1, +#' random = pdDiag(parent_0 + log_k_parent + log_k_A1 + f_parent_ilr_1 ~ 1), +#' start = mean_dp_sfo_sfo_ff) +#' +#' # It does converge with this version of reduced random effects +#' f_nlme_sfo_sfo_ff <- nlme(value ~ nlme_f_sfo_sfo_ff(name, time, +#' parent_0, log_k_parent, log_k_A1, f_parent_ilr_1), +#' data = grouped_data_2, +#' fixed = parent_0 + log_k_parent + log_k_A1 + f_parent_ilr_1 ~ 1, +#' random = pdDiag(parent_0 + log_k_parent ~ 1), +#' start = mean_dp_sfo_sfo_ff) +#' +#' f_nlme_fomc_sfo <- nlme(value ~ nlme_f_fomc_sfo(name, time, +#' parent_0, log_alpha, log_beta, log_k_A1, f_parent_ilr_1), +#' data = grouped_data_2, +#' fixed = parent_0 + log_alpha + log_beta + log_k_A1 + f_parent_ilr_1 ~ 1, +#' random = pdDiag(parent_0 + log_alpha + log_beta + log_k_A1 + f_parent_ilr_1 ~ 1), +#' start = mean_dp_fomc_sfo) +#' +#' # DFOP-SFO and SFORB-SFO did not converge with full random effects +#' +#' anova(f_nlme_fomc_sfo, f_nlme_sfo_sfo) +#' } +#' @export +mean_degparms <- function(object) { + if (nrow(object) > 1) stop("Only row objects allowed") + p_mat_start_trans <- sapply(object, parms, transformed = TRUE) + mean_degparm_names <- setdiff(rownames(p_mat_start_trans), names(object[[1]]$errparms)) + res <- apply(p_mat_start_trans[mean_degparm_names, ], 1, mean) + return(res) +} + +#' @rdname nlme +#' @importFrom purrr map_dfr +#' @return A groupedData data object +#' @export +nlme_data <- function(object) { + if (nrow(object) > 1) stop("Only row objects allowed") + ds_names <- colnames(object) + + ds_list <- lapply(object, function(x) x$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_renamed <- data.frame(ds = ds_nlme$ds, name = ds_nlme$variable, + time = ds_nlme$time, value = ds_nlme$observed, + stringsAsFactors = FALSE) + ds_nlme_grouped <- groupedData(value ~ time | ds, ds_nlme_renamed) + return(ds_nlme_grouped) +} + +#' @rdname nlme +#' @return A function that can be used with nlme +#' @export +nlme_function <- function(object) { + if (nrow(object) > 1) stop("Only row objects allowed") + + mkin_model <- object[[1]]$mkinmod + + degparm_names <- names(mean_degparms(object)) + + # Inspired by https://stackoverflow.com/a/12983961/3805440 + # and https://stackoverflow.com/a/26280789/3805440 + model_function_alist <- replicate(length(degparm_names) + 2, substitute()) + names(model_function_alist) <- c("name", "time", degparm_names) + + model_function_body <- quote({ + arg_frame <- as.data.frame(as.list((environment())), stringsAsFactors = FALSE) + res_frame <- arg_frame[1:2] + parm_frame <- arg_frame[-(1:2)] + parms_unique <- unique(parm_frame) + + n_unique <- nrow(parms_unique) + + times_ds <- list() + names_ds <- list() + for (i in 1:n_unique) { + times_ds[[i]] <- + arg_frame[which(arg_frame[[3]] == parms_unique[i, 1]), "time"] + names_ds[[i]] <- + arg_frame[which(arg_frame[[3]] == parms_unique[i, 1]), "name"] + } + + res_list <- lapply(1:n_unique, function(x) { + transparms_optim <- unlist(parms_unique[x, , drop = TRUE]) + parms_fixed <- object[[1]]$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)) + return(model_function) +} -- cgit v1.2.1