First nlme fits for models with a metabolite

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))