Log-Cholesky parameterisation as default in nlme.mmkin

1 files changed, 27 insertions, 18 deletions

diff --git a/man/nlme.mmkin.Rd b/man/nlme.mmkin.Rd
index abcd0e81..0a9f6913 100644
--- a/man/nlme.mmkin.Rd
+++ b/man/nlme.mmkin.Rd
@@ -87,15 +87,15 @@ ds <- lapply(experimental_data_for_UBA_2019[6:10],
 f <- mmkin(c("SFO", "DFOP"), ds, quiet = TRUE, cores = 1)
 library(nlme)
 f_nlme_sfo <- nlme(f["SFO", ])
-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))
-  update(f_nlme_2, random = parent_0 ~ 1)
+
+  f_nlme_dfop <- nlme(f["DFOP", ])
+  anova(f_nlme_sfo, f_nlme_dfop)
+  print(f_nlme_dfop)
+  plot(f_nlme_dfop)
+  endpoints(f_nlme_dfop)
+
   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"),
@@ -113,14 +113,15 @@ endpoints(f_nlme_dfop)
   f_nlme_sfo_sfo <- nlme(f_2["SFO-SFO", ])
   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)
+  # With formation fractions this does not coverge with defaults
+  # f_nlme_sfo_sfo_ff <- nlme(f_2["SFO-SFO-ff", ])
+  #plot(f_nlme_sfo_sfo_ff)
 
-  # For the following fit we need to increase pnlsMaxIter and the tolerance
-  # to get convergence
-  f_nlme_dfop_sfo <- nlme(f_2["DFOP-SFO", ],
-    control = list(pnlsMaxIter = 120, tolerance = 5e-4), verbose = TRUE)
+  # With the log-Cholesky parameterization, this converges in 11
+  # iterations and around 100 seconds, but without tweaking control
+  # parameters (with pdDiag, increasing the tolerance and pnlsMaxIter was
+  # necessary)
+  f_nlme_dfop_sfo <- nlme(f_2["DFOP-SFO", ])
 
   plot(f_nlme_dfop_sfo)
 
@@ -145,10 +146,18 @@ endpoints(f_nlme_dfop)
     ds_2, quiet = TRUE, error_model = "obs")
   f_nlme_sfo_sfo_obs <- nlme(f_2_obs["SFO-SFO", ])
   print(f_nlme_sfo_sfo_obs)
-  # The same with DFOP-SFO does not converge, apparently the variances of
-  # parent and A1 are too similar in this case, so that the model is
-  # overparameterised
-  #f_nlme_dfop_sfo_obs <- nlme(f_2_obs["DFOP-SFO", ], control = list(maxIter = 100))
+  f_nlme_dfop_sfo_obs <- nlme(f_2_obs["DFOP-SFO", ])
+
+  f_2_tc <- mmkin(list("SFO-SFO" = m_sfo_sfo,
+   "DFOP-SFO" = m_dfop_sfo),
+    ds_2, quiet = TRUE, error_model = "tc")
+  # f_nlme_sfo_sfo_tc <- nlme(f_2_tc["SFO-SFO", ]) # stops with error message
+  f_nlme_dfop_sfo_tc <- nlme(f_2_tc["DFOP-SFO", ])
+  # We get warnings about false convergence in the LME step in several iterations
+  # but as the last such warning occurs in iteration 25 and we have 28 iterations
+  # we can ignore these
+  anova(f_nlme_dfop_sfo, f_nlme_dfop_sfo_obs, f_nlme_dfop_sfo_tc)
+
 }
 }
 \seealso{