From 48c463680b51fa767b4cd7bd62865f192d0354ac Mon Sep 17 00:00:00 2001
From: Johannes Ranke
Date: Sat, 6 Feb 2021 18:30:32 +0100
Subject: Reintroduce interface to saemix
Also after the upgrade from buster to bullseye of my local system, some
test results for saemix have changed.
---
docs/dev/reference/nlme.mmkin.html | 76 +++++++++++++++++++++++---------------
1 file changed, 46 insertions(+), 30 deletions(-)
(limited to 'docs/dev/reference/nlme.mmkin.html')
diff --git a/docs/dev/reference/nlme.mmkin.html b/docs/dev/reference/nlme.mmkin.html
index a4d7070a..2649c111 100644
--- a/docs/dev/reference/nlme.mmkin.html
+++ b/docs/dev/reference/nlme.mmkin.html
@@ -74,7 +74,7 @@ have been obtained by fitting the same model to a list of datasets." />
mkin
- 0.9.50.4
+ 1.0.1.9000
@@ -123,7 +123,7 @@ have been obtained by fitting the same model to a list of datasets." />
-
-
+
@@ -262,6 +262,12 @@ parameters taken from the mmkin object are used
Upon success, a fitted 'nlme.mmkin' object, which is an nlme object
with additional elements. It also inherits from 'mixed.mmkin'.
+ Details
+
+ Note that the convergence of the nlme algorithms depends on the quality
+of the data. In degradation kinetics, we often only have few datasets
+(e.g. data for few soils) and complicated degradation models, which may
+make it impossible to obtain convergence with nlme.
Note
As the object inherits from nlme::nlme, there is a wealth of
@@ -284,7 +290,7 @@ methods that will automatically work on 'nlme.mmkin' objects, such as
anova(f_nlme_sfo, f_nlme_dfop)
#> Model df AIC BIC logLik Test L.Ratio p-value
#> f_nlme_sfo 1 5 625.0539 637.5529 -307.5269
-#> f_nlme_dfop 2 9 495.1270 517.6253 -238.5635 1 vs 2 137.9268 <.0001
#> Kinetic nonlinear mixed-effects model fit by maximum likelihood
#>
#> Structural model:
@@ -312,7 +318,7 @@ methods that will automatically work on 'nlme.mmkin' objects, such as
#> $distimes
#> DT50 DT90 DT50back DT50_k1 DT50_k2
-#> parent 10.79857 100.7937 30.34192 4.193937 43.85442
+#> parent 10.79857 100.7937 30.34193 4.193938 43.85443
#>
#> Error in nlme.formula(model = value ~ (mkin::get_deg_func())(name, time, parent_0, log_k_A1, f_parent_qlogis, log_k1, log_k2, g_qlogis), data = structure(list(ds = structure(c(1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 5L, 5L, 5L, 5L, 5L, 5L, 5L, 5L, 5L, 5L, 5L, 5L, 5L, 5L, 5L, 5L, 5L, 5L, 5L, 5L, 5L, 5L, 5L, 5L, 5L, 5L, 5L, 5L, 5L, 5L ), .Label = c("1", "2", "3", "4", "5"), class = c("ordered", "factor")), name = c("parent", "parent", "parent", "parent", "parent", "parent", "parent", "parent", "parent", "parent", "parent", "parent", "parent", "parent", "parent", "parent", "parent", "parent", "parent", "parent", "A1", "A1", "A1", "A1", "A1", "A1", "A1", "A1", "A1", "A1", "A1", "A1", "A1", "A1", "A1", "A1", "A1", "A1", "parent", "parent", "parent", "parent", "parent", "parent", "parent", "parent", "parent", "parent", "parent", "parent", "parent", "parent", "parent", "parent", "parent", "parent", "A1", "A1", "A1", "A1", "A1", "A1", "A1", "A1", "A1", "A1", "A1", "A1", "A1", "A1", "A1", "A1", "parent", "parent", "parent", "parent", "parent", "parent", "parent", "parent", "parent", "parent", "parent", "parent", "parent", "parent", "parent", "parent", "A1", "A1", "A1", "A1", "A1", "A1", "A1", "A1", "A1", "A1", "A1", "A1", "A1", "A1", "parent", "parent", "parent", "parent", "parent", "parent", "parent", "parent", "parent", "parent", "parent", "parent", "parent", "parent", "parent", "parent", "parent", "parent", "parent", "parent", "A1", "A1", "A1", "A1", "A1", "A1", "A1", "A1", "A1", "A1", "A1", "A1", "A1", "A1", "A1", "A1", "A1", "A1", "parent", "parent", "parent", "parent", "parent", "parent", "parent", "parent", "parent", "parent", "parent", "parent", "parent", "parent", "parent", "parent", "A1", "A1", "A1", "A1", "A1", "A1", "A1", "A1", "A1", "A1", "A1", "A1", "A1", "A1"), time = c(0, 0, 3, 3, 6, 6, 10, 10, 20, 20, 34, 34, 55, 55, 90, 90, 112, 112, 132, 132, 3, 3, 6, 6, 10, 10, 20, 20, 34, 34, 55, 55, 90, 90, 112, 112, 132, 132, 0, 0, 3, 3, 7, 7, 14, 14, 30, 30, 60, 60, 90, 90, 120, 120, 180, 180, 3, 3, 7, 7, 14, 14, 30, 30, 60, 60, 90, 90, 120, 120, 180, 180, 0, 0, 1, 1, 3, 3, 8, 8, 14, 14, 27, 27, 48, 48, 70, 70, 1, 1, 3, 3, 8, 8, 14, 14, 27, 27, 48, 48, 70, 70, 0, 0, 1, 1, 3, 3, 8, 8, 14, 14, 27, 27, 48, 48, 70, 70, 91, 91, 120, 120, 1, 1, 3, 3, 8, 8, 14, 14, 27, 27, 48, 48, 70, 70, 91, 91, 120, 120, 0, 0, 8, 8, 14, 14, 21, 21, 41, 41, 63, 63, 91, 91, 120, 120, 8, 8, 14, 14, 21, 21, 41, 41, 63, 63, 91, 91, 120, 120), value = c(97.2, 96.4, 71.1, 69.2, 58.1, 56.6, 44.4, 43.4, 33.3, 29.2, 17.6, 18, 10.5, 9.3, 4.5, 4.7, 3, 3.4, 2.3, 2.7, 4.3, 4.6, 7, 7.2, 8.2, 8, 11, 13.7, 11.5, 12.7, 14.9, 14.5, 12.1, 12.3, 9.9, 10.2, 8.8, 7.8, 93.6, 92.3, 87, 82.2, 74, 73.9, 64.2, 69.5, 54, 54.6, 41.1, 38.4, 32.5, 35.5, 28.1, 29, 26.5, 27.6, 3.9, 3.1, 6.9, 6.6, 10.4, 8.3, 14.4, 13.7, 22.1, 22.3, 27.5, 25.4, 28, 26.6, 25.8, 25.3, 91.9, 90.8, 64.9, 66.2, 43.5, 44.1, 18.3, 18.1, 10.2, 10.8, 4.9, 3.3, 1.6, 1.5, 1.1, 0.9, 9.6, 7.7, 15, 15.1, 21.2, 21.1, 19.7, 18.9, 17.5, 15.9, 9.5, 9.8, 6.2, 6.1, 99.8, 98.3, 77.1, 77.2, 59, 58.1, 27.4, 29.2, 19.1, 29.6, 10.1, 18.2, 4.5, 9.1, 2.3, 2.9, 2, 1.8, 2, 2.2, 4.2, 3.9, 7.4, 7.9, 14.5, 13.7, 14.2, 12.2, 13.7, 13.2, 13.6, 15.4, 10.4, 11.6, 10, 9.5, 9.1, 9, 96.1, 94.3, 73.9, 73.9, 69.4, 73.1, 65.6, 65.3, 55.9, 54.4, 47, 49.3, 44.7, 46.7, 42.1, 41.3, 3.3, 3.4, 3.9, 2.9, 6.4, 7.2, 9.1, 8.5, 11.7, 12, 13.3, 13.2, 14.3, 12.1)), row.names = c(NA, -170L), class = c("nfnGroupedData", "nfGroupedData", "groupedData", "data.frame"), formula = value ~ time | ds, FUN = function (x) max(x, na.rm = TRUE), order.groups = FALSE), start = list( fixed = c(parent_0 = 93.8101519326534, log_k_A1 = -9.76474551635931, f_parent_qlogis = -0.971114801595408, log_k1 = -1.87993711571859, log_k2 = -4.27081421366622, g_qlogis = 0.135644115277507 ), random = list(ds = structure(c(2.56569977430371, -3.49441920289139, -3.32614443321494, 4.35347873814922, -0.0986148763466161, 4.65850590018027, 1.8618544764481, 6.12693257601545, 4.91792724701579, -17.5652201996596, -0.466203822618637, 0.746660653597927, 0.282193987271096, -0.42053488943072, -0.142115928819667, 0.369240076779088, -1.38985563501659, 1.02592753494098, 0.73090914081534, -0.736221117518819, 0.768170629350299, -1.89347658079869, 1.72168783460352, 0.844607177798114, -1.44098906095325, -0.377731855445672, 0.168180098477565, 0.469683412912104, 0.500717664434525, -0.760849320378522), .Dim = 5:6, .Dimnames = list(c("1", "2", "3", "4", "5"), c("parent_0", "log_k_A1", "f_parent_qlogis", "log_k1", "log_k2", "g_qlogis"))))), fixed = list(parent_0 ~ 1, log_k_A1 ~ 1, f_parent_qlogis ~ 1, log_k1 ~ 1, log_k2 ~ 1, g_qlogis ~ 1), random = structure(numeric(0), class = c("pdDiag", "pdMat"), formula = structure(list(parent_0 ~ 1, log_k_A1 ~ 1, f_parent_qlogis ~ 1, log_k1 ~ 1, log_k2 ~ 1, g_qlogis ~ 1), class = "listForm"), Dimnames = list(NULL, NULL))): maximum number of iterations (maxIter = 50) reached without convergence
#> Timing stopped at: 49.95 16.5 44.08
#> Error in plot(f_nlme_dfop_sfo): object 'f_nlme_dfop_sfo' not found
+
#> Error in anova(f_nlme_dfop_sfo, f_nlme_sfo_sfo): object 'f_nlme_dfop_sfo' not found
+
#> Model df AIC BIC logLik Test L.Ratio p-value
+#> f_nlme_dfop_sfo 1 13 843.8548 884.6201 -408.9274
+#> f_nlme_sfo_sfo 2 9 1085.1821 1113.4043 -533.5910 1 vs 2 249.3273 <.0001
#> $ff
#> parent_sink parent_A1 A1_sink
@@ -355,7 +362,15 @@ methods that will automatically work on 'nlme.mmkin' objects, such as
#> parent 19.13518 63.5657
#> A1 66.02155 219.3189
#>
#> Error in endpoints(f_nlme_dfop_sfo): object 'f_nlme_dfop_sfo' not found
+
#> $ff
+#> parent_A1 parent_sink
+#> 0.2768575 0.7231425
+#>
+#> $distimes
+#> DT50 DT90 DT50back DT50_k1 DT50_k2
+#> parent 11.07091 104.6320 31.49737 4.462384 46.20825
+#> A1 162.30492 539.1653 NA NA NA
+#>
#> Kinetic nonlinear mixed-effects model fit by maximum likelihood
@@ -429,18 +442,21 @@ methods that will automatically work on 'nlme.mmkin' objects, such as
#> Formula: ~1 | name
#> Parameter estimates:
#> parent A1
-#> 1.0000000 0.2050003
#> Error in nlme.formula(model = value ~ (mkin::get_deg_func())(name, time, parent_0, log_k_A1, f_parent_qlogis, log_k1, log_k2, g_qlogis), data = structure(list(ds = structure(c(1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 5L, 5L, 5L, 5L, 5L, 5L, 5L, 5L, 5L, 5L, 5L, 5L, 5L, 5L, 5L, 5L, 5L, 5L, 5L, 5L, 5L, 5L, 5L, 5L, 5L, 5L, 5L, 5L, 5L, 5L ), .Label = c("1", "2", "3", "4", "5"), class = c("ordered", "factor")), name = c("parent", "parent", "parent", "parent", "parent", "parent", "parent", "parent", "parent", "parent", "parent", "parent", "parent", "parent", "parent", "parent", "parent", "parent", "parent", "parent", "A1", "A1", "A1", "A1", "A1", "A1", "A1", "A1", "A1", "A1", "A1", "A1", "A1", "A1", "A1", "A1", "A1", "A1", "parent", "parent", "parent", "parent", "parent", "parent", "parent", "parent", "parent", "parent", "parent", "parent", "parent", "parent", "parent", "parent", "parent", "parent", "A1", "A1", "A1", "A1", "A1", "A1", "A1", "A1", "A1", "A1", "A1", "A1", "A1", "A1", "A1", "A1", "parent", "parent", "parent", "parent", "parent", "parent", "parent", "parent", "parent", "parent", "parent", "parent", "parent", "parent", "parent", "parent", "A1", "A1", "A1", "A1", "A1", "A1", "A1", "A1", "A1", "A1", "A1", "A1", "A1", "A1", "parent", "parent", "parent", "parent", "parent", "parent", "parent", "parent", "parent", "parent", "parent", "parent", "parent", "parent", "parent", "parent", "parent", "parent", "parent", "parent", "A1", "A1", "A1", "A1", "A1", "A1", "A1", "A1", "A1", "A1", "A1", "A1", "A1", "A1", "A1", "A1", "A1", "A1", "parent", "parent", "parent", "parent", "parent", "parent", "parent", "parent", "parent", "parent", "parent", "parent", "parent", "parent", "parent", "parent", "A1", "A1", "A1", "A1", "A1", "A1", "A1", "A1", "A1", "A1", "A1", "A1", "A1", "A1"), time = c(0, 0, 3, 3, 6, 6, 10, 10, 20, 20, 34, 34, 55, 55, 90, 90, 112, 112, 132, 132, 3, 3, 6, 6, 10, 10, 20, 20, 34, 34, 55, 55, 90, 90, 112, 112, 132, 132, 0, 0, 3, 3, 7, 7, 14, 14, 30, 30, 60, 60, 90, 90, 120, 120, 180, 180, 3, 3, 7, 7, 14, 14, 30, 30, 60, 60, 90, 90, 120, 120, 180, 180, 0, 0, 1, 1, 3, 3, 8, 8, 14, 14, 27, 27, 48, 48, 70, 70, 1, 1, 3, 3, 8, 8, 14, 14, 27, 27, 48, 48, 70, 70, 0, 0, 1, 1, 3, 3, 8, 8, 14, 14, 27, 27, 48, 48, 70, 70, 91, 91, 120, 120, 1, 1, 3, 3, 8, 8, 14, 14, 27, 27, 48, 48, 70, 70, 91, 91, 120, 120, 0, 0, 8, 8, 14, 14, 21, 21, 41, 41, 63, 63, 91, 91, 120, 120, 8, 8, 14, 14, 21, 21, 41, 41, 63, 63, 91, 91, 120, 120), value = c(97.2, 96.4, 71.1, 69.2, 58.1, 56.6, 44.4, 43.4, 33.3, 29.2, 17.6, 18, 10.5, 9.3, 4.5, 4.7, 3, 3.4, 2.3, 2.7, 4.3, 4.6, 7, 7.2, 8.2, 8, 11, 13.7, 11.5, 12.7, 14.9, 14.5, 12.1, 12.3, 9.9, 10.2, 8.8, 7.8, 93.6, 92.3, 87, 82.2, 74, 73.9, 64.2, 69.5, 54, 54.6, 41.1, 38.4, 32.5, 35.5, 28.1, 29, 26.5, 27.6, 3.9, 3.1, 6.9, 6.6, 10.4, 8.3, 14.4, 13.7, 22.1, 22.3, 27.5, 25.4, 28, 26.6, 25.8, 25.3, 91.9, 90.8, 64.9, 66.2, 43.5, 44.1, 18.3, 18.1, 10.2, 10.8, 4.9, 3.3, 1.6, 1.5, 1.1, 0.9, 9.6, 7.7, 15, 15.1, 21.2, 21.1, 19.7, 18.9, 17.5, 15.9, 9.5, 9.8, 6.2, 6.1, 99.8, 98.3, 77.1, 77.2, 59, 58.1, 27.4, 29.2, 19.1, 29.6, 10.1, 18.2, 4.5, 9.1, 2.3, 2.9, 2, 1.8, 2, 2.2, 4.2, 3.9, 7.4, 7.9, 14.5, 13.7, 14.2, 12.2, 13.7, 13.2, 13.6, 15.4, 10.4, 11.6, 10, 9.5, 9.1, 9, 96.1, 94.3, 73.9, 73.9, 69.4, 73.1, 65.6, 65.3, 55.9, 54.4, 47, 49.3, 44.7, 46.7, 42.1, 41.3, 3.3, 3.4, 3.9, 2.9, 6.4, 7.2, 9.1, 8.5, 11.7, 12, 13.3, 13.2, 14.3, 12.1)), row.names = c(NA, -170L), class = c("nfnGroupedData", "nfGroupedData", "groupedData", "data.frame"), formula = value ~ time | ds, FUN = function (x) max(x, na.rm = TRUE), order.groups = FALSE), start = list( fixed = c(parent_0 = 93.4272167134207, log_k_A1 = -9.71590717106959, f_parent_qlogis = -0.953712099744438, log_k1 = -1.95256957646888, log_k2 = -4.42919226610318, g_qlogis = 0.193023137298073 ), random = list(ds = structure(c(2.85557330683041, -3.87630303729395, -2.78062140212751, 4.82042042600536, -1.01906929341432, 4.613992019697, 2.05871276943309, 6.0766404049189, 4.86471337131288, -17.6140585653619, -0.480721175257541, 0.773079218835614, 0.260464433006093, -0.440615012802434, -0.112207463781733, 0.445812953745225, -1.49588630006094, 1.13602040717272, 0.801850880762046, -0.887797941619048, 0.936480292463262, -2.43093808171905, 1.91256225793793, 0.984827519864443, -1.40293198854659, -0.455176326336681, 0.376355651864385, 0.343919720700401, 0.46329187713133, -0.728390923359434 ), .Dim = 5:6, .Dimnames = list(c("1", "2", "3", "4", "5"), c("parent_0", "log_k_A1", "f_parent_qlogis", "log_k1", "log_k2", "g_qlogis"))))), fixed = list(parent_0 ~ 1, log_k_A1 ~ 1, f_parent_qlogis ~ 1, log_k1 ~ 1, log_k2 ~ 1, g_qlogis ~ 1), random = structure(numeric(0), class = c("pdDiag", "pdMat"), formula = structure(list(parent_0 ~ 1, log_k_A1 ~ 1, f_parent_qlogis ~ 1, log_k1 ~ 1, log_k2 ~ 1, g_qlogis ~ 1), class = "listForm"), Dimnames = list(NULL, NULL)), weights = structure(numeric(0), formula = ~1 | name, class = c("varIdent", "varFunc"))): maximum number of iterations (maxIter = 50) reached without convergence
#> Timing stopped at: 59.38 16.5 53.5
#> Warning: longer object length is not a multiple of shorter object length
#> Warning: longer object length is not a multiple of shorter object length
#> Warning: longer object length is not a multiple of shorter object length
#> Warning: longer object length is not a multiple of shorter object length
#> Warning: longer object length is not a multiple of shorter object length
#> Warning: longer object length is not a multiple of shorter object length
#> Warning: longer object length is not a multiple of shorter object length
#> Warning: longer object length is not a multiple of shorter object length
#> Warning: longer object length is not a multiple of shorter object length
#> Warning: longer object length is not a multiple of shorter object length
#> Warning: longer object length is not a multiple of shorter object length
#> Warning: longer object length is not a multiple of shorter object length
#> Warning: longer object length is not a multiple of shorter object length
#> Warning: longer object length is not a multiple of shorter object length
#> Error in X[, fmap[[nm]]] <- gradnm: number of items to replace is not a multiple of replacement length
#> Timing stopped at: 6.363 2.688 5.469
#> Error in anova(f_nlme_dfop_sfo, f_nlme_dfop_sfo_obs, f_nlme_dfop_sfo_tc): object 'f_nlme_dfop_sfo' not found
+#> 1.0000000 0.2049995
#> Model df AIC BIC logLik Test L.Ratio
+#> f_nlme_dfop_sfo 1 13 843.8548 884.6201 -408.9274
+#> f_nlme_dfop_sfo_obs 2 14 817.5338 861.4350 -394.7669 1 vs 2 28.32093
+#> p-value
+#> f_nlme_dfop_sfo
+#> f_nlme_dfop_sfo_obs <.0001
# }
--
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