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authorJohannes Ranke <jranke@uni-bremen.de>2020-03-30 14:03:51 +0200
committerJohannes Ranke <jranke@uni-bremen.de>2020-03-30 14:03:51 +0200
commit405cde11f9f26fcab0742e84c110cf3dcb2a4c1f (patch)
treec00c880d74676581fcbaa2d9aa7fb4c739f79b18 /R/memkin.R
parent6263a53ef24ff0c06e5f4a869a987f41f361bc58 (diff)
First nlme fits for models with a metabolite
Diffstat (limited to 'R/memkin.R')
-rw-r--r--R/memkin.R118
1 files changed, 78 insertions, 40 deletions
diff --git a/R/memkin.R b/R/memkin.R
index 68837d86..5cc00345 100644
--- a/R/memkin.R
+++ b/R/memkin.R
@@ -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))

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