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-rw-r--r--R/nlme.mmkin.R37
1 files changed, 8 insertions, 29 deletions
diff --git a/R/nlme.mmkin.R b/R/nlme.mmkin.R
index d4720e79..22a70f18 100644
--- a/R/nlme.mmkin.R
+++ b/R/nlme.mmkin.R
@@ -53,6 +53,7 @@ get_deg_func <- function() {
#' f_nlme_dfop <- nlme(f["DFOP", ])
#' AIC(f_nlme_sfo, f_nlme_dfop)
#' print(f_nlme_dfop)
+#' plot(f_nlme_dfop)
#' endpoints(f_nlme_dfop)
#' \dontrun{
#' f_nlme_2 <- nlme(f["SFO", ], start = c(parent_0 = 100, log_k_parent = 0.1))
@@ -63,58 +64,36 @@ get_deg_func <- function() {
#' A1 = mkinsub("SFO"), use_of_ff = "min", quiet = TRUE)
#' m_sfo_sfo_ff <- mkinmod(parent = mkinsub("SFO", "A1"),
#' A1 = mkinsub("SFO"), use_of_ff = "max", quiet = TRUE)
-#' m_fomc_sfo <- mkinmod(parent = mkinsub("FOMC", "A1"),
-#' A1 = mkinsub("SFO"), quiet = TRUE)
#' m_dfop_sfo <- mkinmod(parent = mkinsub("DFOP", "A1"),
#' A1 = mkinsub("SFO"), quiet = TRUE)
#'
#' 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),
#' ds_2, quiet = TRUE)
-#' plot(f_2["SFO-SFO", 3:4]) # Separate fits for datasets 3 and 4
#'
#' f_nlme_sfo_sfo <- nlme(f_2["SFO-SFO", ])
-#' # plot(f_nlme_sfo_sfo) # not feasible with pkgdown figures
-#' plot(f_nlme_sfo_sfo, 3:4) # Global mixed model: Fits for datasets 3 and 4
+#' plot(f_nlme_sfo_sfo)
#'
#' # With formation fractions
#' f_nlme_sfo_sfo_ff <- nlme(f_2["SFO-SFO-ff", ])
-#' plot(f_nlme_sfo_sfo_ff, 3:4) # chi2 different due to different df attribution
+#' plot(f_nlme_sfo_sfo_ff)
#'
-#' # For more parameters, we need to increase pnlsMaxIter and the tolerance
+#' # For the following fit we need to increase pnlsMaxIter and the tolerance
#' # to get convergence
-#' f_nlme_fomc_sfo <- nlme(f_2["FOMC-SFO", ],
-#' control = list(pnlsMaxIter = 100, tolerance = 1e-4), verbose = TRUE)
#' f_nlme_dfop_sfo <- nlme(f_2["DFOP-SFO", ],
#' control = list(pnlsMaxIter = 120, tolerance = 5e-4), verbose = TRUE)
-#' plot(f_2["FOMC-SFO", 3:4])
-#' plot(f_nlme_fomc_sfo, 3:4)
#'
-#' plot(f_2["DFOP-SFO", 3:4])
-#' plot(f_nlme_dfop_sfo, 3:4)
+#' plot(f_nlme_dfop_sfo)
#'
-#' anova(f_nlme_dfop_sfo, f_nlme_fomc_sfo, f_nlme_sfo_sfo)
-#' anova(f_nlme_dfop_sfo, f_nlme_sfo_sfo) # if we ignore FOMC
+#' anova(f_nlme_dfop_sfo, f_nlme_sfo_sfo)
#'
#' endpoints(f_nlme_sfo_sfo)
#' endpoints(f_nlme_dfop_sfo)
#'
#' if (length(findFunction("varConstProp")) > 0) { # tc error model for nlme available
-#' # Attempts to fit metabolite kinetics with the tc error model
-#' #f_2_tc <- mmkin(list("SFO-SFO" = m_sfo_sfo,
-#' # "SFO-SFO-ff" = m_sfo_sfo_ff,
-#' # "FOMC-SFO" = m_fomc_sfo,
-#' # "DFOP-SFO" = m_dfop_sfo),
-#' # ds_2, quiet = TRUE,
-#' # error_model = "tc")
-#' #f_nlme_sfo_sfo_tc <- nlme(f_2_tc["SFO-SFO", ], control = list(maxIter = 100))
-#' #f_nlme_dfop_sfo_tc <- nlme(f_2_tc["DFOP-SFO", ])
-#' #f_nlme_dfop_sfo_tc <- update(f_nlme_dfop_sfo, weights = varConstProp(),
-#' # control = list(sigma = 1, msMaxIter = 100, pnlsMaxIter = 15))
-#' # Fitting metabolite kinetics with nlme.mmkin and the two-component
-#' # error model currently does not work, at least not with these data.
+#' # Attempts to fit metabolite kinetics with the tc error model are possible,
+#' # but need tweeking of control values and sometimes do not converge
#'
#' f_tc <- mmkin(c("SFO", "DFOP"), ds, quiet = TRUE, error_model = "tc")
#' f_nlme_sfo_tc <- nlme(f_tc["SFO", ])

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