Improve interface to experimental version of nlme

The experimental nlme version in my drat repository contains the
variance function structure varConstProp which makes it possible to use
the two-component error model in generalized nonlinear models using
nlme::gnls() and in mixed effects models using nlme::nlme().

3 files changed, 66 insertions, 8 deletions

diff --git a/man/add_err.Rd b/man/add_err.Rd
index 9527d508..fe7002ee 100644
--- a/man/add_err.Rd
+++ b/man/add_err.Rd
@@ -8,7 +8,7 @@ add_err(
   prediction,
   sdfunc,
   secondary = c("M1", "M2"),
-  n = 1000,
+  n = 10,
   LOD = 0.1,
   reps = 2,
   digits = 1,
diff --git a/man/nlme.mmkin.Rd b/man/nlme.mmkin.Rd
index 10c3ec78..0af670a0 100644
--- a/man/nlme.mmkin.Rd
+++ b/man/nlme.mmkin.Rd
@@ -77,16 +77,16 @@ have been obtained by fitting the same model to a list of datasets.
 \examples{
 ds <- lapply(experimental_data_for_UBA_2019[6:10],
  function(x) subset(x$data[c("name", "time", "value")], name == "parent"))
-f <- mmkin("SFO", ds, quiet = TRUE, cores = 1)
+f <- mmkin(c("SFO", "DFOP"), ds, quiet = TRUE, cores = 1)
 library(nlme)
-endpoints(f[[1]])
-f_nlme <- nlme(f)
-print(f_nlme)
-endpoints(f_nlme)
+f_nlme_sfo <- nlme(f["SFO", ])
+f_nlme_dfop <- nlme(f["DFOP", ])
+AIC(f_nlme_sfo, f_nlme_dfop)
+print(f_nlme_dfop)
+endpoints(f_nlme_dfop)
 \dontrun{
-  f_nlme_2 <- nlme(f, start = c(parent_0 = 100, log_k_parent_sink = 0.1))
+  f_nlme_2 <- nlme(f["SFO", ], start = c(parent_0 = 100, log_k_parent = 0.1))
   update(f_nlme_2, random = parent_0 ~ 1)
-  # Test on some real data
   ds_2 <- lapply(experimental_data_for_UBA_2019[6:10],
    function(x) x$data[c("name", "time", "value")])
   m_sfo_sfo <- mkinmod(parent = mkinsub("SFO", "A1"),
@@ -130,6 +130,36 @@ endpoints(f_nlme)
 
   endpoints(f_nlme_sfo_sfo)
   endpoints(f_nlme_dfop_sfo)
+
+  if (findFunction("varConstProp")) { # 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.
+
+    f_tc <- mmkin(c("SFO", "DFOP"), ds, quiet = TRUE, error_model = "tc")
+    f_nlme_sfo_tc <- nlme(f_tc["SFO", ])
+    f_nlme_dfop_tc <- nlme(f_tc["DFOP", ])
+    AIC(f_nlme_sfo, f_nlme_sfo_tc, f_nlme_dfop, f_nlme_dfop_tc)
+    print(f_nlme_dfop_tc)
+  }
+  f_2_obs <- mmkin(list("SFO-SFO" = m_sfo_sfo,
+   "DFOP-SFO" = m_dfop_sfo),
+    ds_2, quiet = TRUE, error_model = "obs")
+  f_nlme_sfo_sfo_obs <- nlme(f_2_obs["SFO-SFO", ])
+  # 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))
 }
 }
 \seealso{
diff --git a/man/sigma_twocomp.Rd b/man/sigma_twocomp.Rd
index 4e1f7c38..ed79d493 100644
--- a/man/sigma_twocomp.Rd
+++ b/man/sigma_twocomp.Rd
@@ -31,6 +31,34 @@ proposed by Rocke and Lorenzato (1995) can be written in this form as well,
 but assumes approximate lognormal distribution of errors for high values of
 y.
 }
+\examples{
+times <- c(0, 1, 3, 7, 14, 28, 60, 90, 120)
+d_pred <- data.frame(time = times, parent = 100 * exp(- 0.03 * times))
+set.seed(123456)
+d_syn <- add_err(d_pred, function(y) sigma_twocomp(y, 1, 0.07),
+  reps = 2, n = 1)[[1]]
+f_nls <- nls(value ~ SSasymp(time, 0, parent_0, lrc), data = d_syn,
+ start = list(parent_0 = 100, lrc = -3))
+library(nlme)
+f_gnls <- gnls(value ~ SSasymp(time, 0, parent_0, lrc),
+  data = d_syn, na.action = na.omit,
+  start = list(parent_0 = 100, lrc = -3))
+if (length(findFunction("varConstProp")) > 0) {
+  f_gnls_tc <- gnls(value ~ SSasymp(time, 0, parent_0, lrc),
+    data = d_syn, na.action = na.omit,
+    start = list(parent_0 = 100, lrc = -3),
+    weights = varConstProp())
+  f_gnls_tc_sf <- gnls(value ~ SSasymp(time, 0, parent_0, lrc),
+    data = d_syn, na.action = na.omit,
+    start = list(parent_0 = 100, lrc = -3),
+    control = list(sigma = 1),
+    weights = varConstProp())
+}
+f_mkin <- mkinfit("SFO", d_syn, error_model = "const", quiet = TRUE)
+f_mkin_tc <- mkinfit("SFO", d_syn, error_model = "tc", quiet = TRUE)
+plot_res(f_mkin_tc, standardized = TRUE)
+AIC(f_nls, f_gnls, f_gnls_tc, f_gnls_tc_sf, f_mkin, f_mkin_tc)
+}
 \references{
 Werner, Mario, Brooks, Samuel H., and Knott, Lancaster B. (1978)
 Additive, Multiplicative, and Mixed Analytical Errors. Clinical Chemistry