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authorJohannes Ranke <jranke@uni-bremen.de>2020-03-29 22:02:34 +0200
committerJohannes Ranke <jranke@uni-bremen.de>2020-03-29 22:02:34 +0200
commit6263a53ef24ff0c06e5f4a869a987f41f361bc58 (patch)
tree7ea91eff19047165c95f15a49a23a264f9d90d53 /R/memkin.R
parent20ece4e0bcbeceb90a940e04a858f4ffb6d6b5e4 (diff)
First automatic generation of an nlme model
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+#' 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 ... Additional arguments passed to \code{\link{nlme}}
+#' @importFrom nlme nlme
+#' @return A fitted object of class 'memkin'
+#' @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)
+#'
+#' @export
+memkin <- function(object, ...) {
+ 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_grouped <- groupedData(observed ~ time | ds, ds_nlme)
+
+ mkin_model <- object[[1]]$mkinmod
+
+ # Inspired by https://stackoverflow.com/a/12983961/3805440
+ # and https://stackoverflow.com/a/26280789/3805440
+ model_function_alist <- replicate(length(p_names_mean_function) + 2, substitute())
+ names(model_function_alist) <- c("name", "time", p_names_mean_function)
+
+
+
+ model_function_body <- quote({
+ arg_frame <- as.data.frame(as.list((environment())))
+ res <- parent_0 * exp( - exp(log_k_parent_sink) * time)
+ dump(c("arg_frame", "res"), file = "out_1.txt", append = TRUE)
+ return(res)
+ })
+ model_function <- as.function(c(model_function_alist, model_function_body))
+ f_nlme <- eval(parse(text = nlme_call_text))
+
+ model_function_body <- quote({
+ arg_frame <- as.data.frame(as.list((environment())))
+
+ res_frame <- arg_frame[1:2]
+
+ parm_frame <- arg_frame[-(1:2)]
+ parms_unique <- unique(parm_frame)
+
+ n_unique <- nrow(parms_unique)
+
+ times_ds <- list()
+ names_ds <- list()
+ for (i in 1:n_unique) {
+ times_ds[[i]] <-
+ arg_frame[which(arg_frame[[3]] == parms_unique[i, 1]), "time"]
+ names_ds[[i]] <-
+ arg_frame[which(arg_frame[[3]] == parms_unique[i, 1]), "name"]
+ }
+
+ res_list <- lapply(1:n_unique, function(x) {
+ parms <- unlist(parms_unique[x, , drop = TRUE])
+ odeini_parm_names <- grep('_0$', 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]]))
+ 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_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))
+
+ 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)
+
+ f_nlme <- eval(parse(text = nlme_call_text))
+
+ return(f_nlme)
+}

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