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-rw-r--r--docs/dev/reference/nlme.mmkin.html78
1 files changed, 47 insertions, 31 deletions
diff --git a/docs/dev/reference/nlme.mmkin.html b/docs/dev/reference/nlme.mmkin.html
index a4d7070a..925cf7cf 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." />
</button>
<span class="navbar-brand">
<a class="navbar-link" href="../index.html">mkin</a>
- <span class="version label label-info" data-toggle="tooltip" data-placement="bottom" title="In-development version">0.9.50.4</span>
+ <span class="version label label-info" data-toggle="tooltip" data-placement="bottom" title="In-development version">1.0.3.9000</span>
</span>
</div>
@@ -123,7 +123,7 @@ have been obtained by fitting the same model to a list of datasets." />
<ul class="nav navbar-nav navbar-right">
<li>
<a href="https://github.com/jranke/mkin/">
- <span class="fab fa fab fa-github fa-lg"></span>
+ <span class="fab fa-github fa-lg"></span>
</a>
</li>
@@ -157,7 +157,7 @@ have been obtained by fitting the same model to a list of datasets.</p>
data <span class='op'>=</span> <span class='st'>"auto"</span>,
fixed <span class='op'>=</span> <span class='fu'><a href='https://rdrr.io/r/base/lapply.html'>lapply</a></span><span class='op'>(</span><span class='fu'><a href='https://rdrr.io/r/base/list.html'>as.list</a></span><span class='op'>(</span><span class='fu'><a href='https://rdrr.io/r/base/names.html'>names</a></span><span class='op'>(</span><span class='fu'><a href='nlme_function.html'>mean_degparms</a></span><span class='op'>(</span><span class='va'>model</span><span class='op'>)</span><span class='op'>)</span><span class='op'>)</span>, <span class='kw'>function</span><span class='op'>(</span><span class='va'>el</span><span class='op'>)</span> <span class='fu'><a href='https://rdrr.io/r/base/eval.html'>eval</a></span><span class='op'>(</span><span class='fu'><a href='https://rdrr.io/r/base/parse.html'>parse</a></span><span class='op'>(</span>text <span class='op'>=</span>
<span class='fu'><a href='https://rdrr.io/r/base/paste.html'>paste</a></span><span class='op'>(</span><span class='va'>el</span>, <span class='fl'>1</span>, sep <span class='op'>=</span> <span class='st'>"~"</span><span class='op'>)</span><span class='op'>)</span><span class='op'>)</span><span class='op'>)</span>,
- random <span class='op'>=</span> <span class='fu'>pdDiag</span><span class='op'>(</span><span class='va'>fixed</span><span class='op'>)</span>,
+ random <span class='op'>=</span> <span class='fu'><a href='https://rdrr.io/pkg/nlme/man/pdDiag.html'>pdDiag</a></span><span class='op'>(</span><span class='va'>fixed</span><span class='op'>)</span>,
<span class='va'>groups</span>,
start <span class='op'>=</span> <span class='fu'><a href='nlme_function.html'>mean_degparms</a></span><span class='op'>(</span><span class='va'>model</span>, random <span class='op'>=</span> <span class='cn'>TRUE</span><span class='op'>)</span>,
correlation <span class='op'>=</span> <span class='cn'>NULL</span>,
@@ -262,6 +262,12 @@ parameters taken from the mmkin object are used</p></td>
<p>Upon success, a fitted 'nlme.mmkin' object, which is an nlme object
with additional elements. It also inherits from 'mixed.mmkin'.</p>
+ <h2 class="hasAnchor" id="details"><a class="anchor" href="#details"></a>Details</h2>
+
+ <p>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.</p>
<h2 class="hasAnchor" id="note"><a class="anchor" href="#note"></a>Note</h2>
<p>As the object inherits from <a href='https://rdrr.io/pkg/nlme/man/nlme.html'>nlme::nlme</a>, there is a wealth of
@@ -284,7 +290,7 @@ methods that will automatically work on 'nlme.mmkin' objects, such as
<span class='fu'><a href='https://rdrr.io/r/stats/anova.html'>anova</a></span><span class='op'>(</span><span class='va'>f_nlme_sfo</span>, <span class='va'>f_nlme_dfop</span><span class='op'>)</span>
</div><div class='output co'>#&gt; Model df AIC BIC logLik Test L.Ratio p-value
#&gt; f_nlme_sfo 1 5 625.0539 637.5529 -307.5269
-#&gt; f_nlme_dfop 2 9 495.1270 517.6253 -238.5635 1 vs 2 137.9268 &lt;.0001</div><div class='input'> <span class='fu'><a href='https://rdrr.io/r/base/print.html'>print</a></span><span class='op'>(</span><span class='va'>f_nlme_dfop</span><span class='op'>)</span>
+#&gt; f_nlme_dfop 2 9 495.1270 517.6253 -238.5635 1 vs 2 137.9269 &lt;.0001</div><div class='input'> <span class='fu'><a href='https://rdrr.io/r/base/print.html'>print</a></span><span class='op'>(</span><span class='va'>f_nlme_dfop</span><span class='op'>)</span>
</div><div class='output co'>#&gt; Kinetic nonlinear mixed-effects model fit by maximum likelihood
#&gt;
#&gt; Structural model:
@@ -312,7 +318,7 @@ methods that will automatically work on 'nlme.mmkin' objects, such as
</div><div class='img'><img src='nlme.mmkin-1.png' alt='' width='700' height='433' /></div><div class='input'> <span class='fu'><a href='endpoints.html'>endpoints</a></span><span class='op'>(</span><span class='va'>f_nlme_dfop</span><span class='op'>)</span>
</div><div class='output co'>#&gt; $distimes
#&gt; DT50 DT90 DT50back DT50_k1 DT50_k2
-#&gt; parent 10.79857 100.7937 30.34192 4.193937 43.85442
+#&gt; parent 10.79857 100.7937 30.34193 4.193938 43.85443
#&gt; </div><div class='input'>
<span class='va'>ds_2</span> <span class='op'>&lt;-</span> <span class='fu'><a href='https://rdrr.io/r/base/lapply.html'>lapply</a></span><span class='op'>(</span><span class='va'>experimental_data_for_UBA_2019</span><span class='op'>[</span><span class='fl'>6</span><span class='op'>:</span><span class='fl'>10</span><span class='op'>]</span>,
<span class='kw'>function</span><span class='op'>(</span><span class='va'>x</span><span class='op'>)</span> <span class='va'>x</span><span class='op'>$</span><span class='va'>data</span><span class='op'>[</span><span class='fu'><a href='https://rdrr.io/r/base/c.html'>c</a></span><span class='op'>(</span><span class='st'>"name"</span>, <span class='st'>"time"</span>, <span class='st'>"value"</span><span class='op'>)</span><span class='op'>]</span><span class='op'>)</span>
@@ -335,16 +341,17 @@ methods that will automatically work on 'nlme.mmkin' objects, such as
<span class='co'># f_nlme_sfo_sfo_ff &lt;- nlme(f_2["SFO-SFO-ff", ])</span>
<span class='co'>#plot(f_nlme_sfo_sfo_ff)</span>
- <span class='co'># With the log-Cholesky parameterization, this converges in 11</span>
- <span class='co'># iterations and around 100 seconds, but without tweaking control</span>
- <span class='co'># parameters (with pdDiag, increasing the tolerance and pnlsMaxIter was</span>
- <span class='co'># necessary)</span>
- <span class='va'>f_nlme_dfop_sfo</span> <span class='op'>&lt;-</span> <span class='fu'><a href='https://rdrr.io/pkg/nlme/man/nlme.html'>nlme</a></span><span class='op'>(</span><span class='va'>f_2</span><span class='op'>[</span><span class='st'>"DFOP-SFO"</span>, <span class='op'>]</span><span class='op'>)</span>
-</div><div class='output co'>#&gt; <span class='error'>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</span></div><div class='output co'>#&gt; <span class='message'>Timing stopped at: 49.95 16.5 44.08</span></div><div class='input'>
+ <span class='co'># For the following, we need to increase pnlsMaxIter and the tolerance</span>
+ <span class='co'># to get convergence</span>
+ <span class='va'>f_nlme_dfop_sfo</span> <span class='op'>&lt;-</span> <span class='fu'><a href='https://rdrr.io/pkg/nlme/man/nlme.html'>nlme</a></span><span class='op'>(</span><span class='va'>f_2</span><span class='op'>[</span><span class='st'>"DFOP-SFO"</span>, <span class='op'>]</span>,
+ control <span class='op'>=</span> <span class='fu'><a href='https://rdrr.io/r/base/list.html'>list</a></span><span class='op'>(</span>pnlsMaxIter <span class='op'>=</span> <span class='fl'>120</span>, tolerance <span class='op'>=</span> <span class='fl'>5e-4</span><span class='op'>)</span><span class='op'>)</span>
+
<span class='fu'><a href='https://rdrr.io/r/graphics/plot.default.html'>plot</a></span><span class='op'>(</span><span class='va'>f_nlme_dfop_sfo</span><span class='op'>)</span>
-</div><div class='output co'>#&gt; <span class='error'>Error in plot(f_nlme_dfop_sfo): object 'f_nlme_dfop_sfo' not found</span></div><div class='input'>
+</div><div class='img'><img src='nlme.mmkin-3.png' alt='' width='700' height='433' /></div><div class='input'>
<span class='fu'><a href='https://rdrr.io/r/stats/anova.html'>anova</a></span><span class='op'>(</span><span class='va'>f_nlme_dfop_sfo</span>, <span class='va'>f_nlme_sfo_sfo</span><span class='op'>)</span>
-</div><div class='output co'>#&gt; <span class='error'>Error in anova(f_nlme_dfop_sfo, f_nlme_sfo_sfo): object 'f_nlme_dfop_sfo' not found</span></div><div class='input'>
+</div><div class='output co'>#&gt; Model df AIC BIC logLik Test L.Ratio p-value
+#&gt; f_nlme_dfop_sfo 1 13 843.8548 884.6201 -408.9274
+#&gt; f_nlme_sfo_sfo 2 9 1085.1821 1113.4043 -533.5910 1 vs 2 249.3273 &lt;.0001</div><div class='input'>
<span class='fu'><a href='endpoints.html'>endpoints</a></span><span class='op'>(</span><span class='va'>f_nlme_sfo_sfo</span><span class='op'>)</span>
</div><div class='output co'>#&gt; $ff
#&gt; parent_sink parent_A1 A1_sink
@@ -355,7 +362,15 @@ methods that will automatically work on 'nlme.mmkin' objects, such as
#&gt; parent 19.13518 63.5657
#&gt; A1 66.02155 219.3189
#&gt; </div><div class='input'> <span class='fu'><a href='endpoints.html'>endpoints</a></span><span class='op'>(</span><span class='va'>f_nlme_dfop_sfo</span><span class='op'>)</span>
-</div><div class='output co'>#&gt; <span class='error'>Error in endpoints(f_nlme_dfop_sfo): object 'f_nlme_dfop_sfo' not found</span></div><div class='input'>
+</div><div class='output co'>#&gt; $ff
+#&gt; parent_A1 parent_sink
+#&gt; 0.2768575 0.7231425
+#&gt;
+#&gt; $distimes
+#&gt; DT50 DT90 DT50back DT50_k1 DT50_k2
+#&gt; parent 11.07091 104.6320 31.49737 4.462384 46.20825
+#&gt; A1 162.30492 539.1653 NA NA NA
+#&gt; </div><div class='input'>
<span class='kw'>if</span> <span class='op'>(</span><span class='fu'><a href='https://rdrr.io/r/base/length.html'>length</a></span><span class='op'>(</span><span class='fu'>findFunction</span><span class='op'>(</span><span class='st'>"varConstProp"</span><span class='op'>)</span><span class='op'>)</span> <span class='op'>&gt;</span> <span class='fl'>0</span><span class='op'>)</span> <span class='op'>{</span> <span class='co'># tc error model for nlme available</span>
<span class='co'># Attempts to fit metabolite kinetics with the tc error model are possible,</span>
<span class='co'># but need tweeking of control values and sometimes do not converge</span>
@@ -381,7 +396,7 @@ methods that will automatically work on 'nlme.mmkin' objects, such as
#&gt; Fixed effects:
#&gt; list(parent_0 ~ 1, log_k1 ~ 1, log_k2 ~ 1, g_qlogis ~ 1)
#&gt; parent_0 log_k1 log_k2 g_qlogis
-#&gt; 94.04775 -1.82340 -4.16715 0.05685
+#&gt; 94.04774 -1.82340 -4.16716 0.05686
#&gt;
#&gt; Random effects:
#&gt; Formula: list(parent_0 ~ 1, log_k1 ~ 1, log_k2 ~ 1, g_qlogis ~ 1)
@@ -395,10 +410,8 @@ methods that will automatically work on 'nlme.mmkin' objects, such as
#&gt; Formula: ~fitted(.)
#&gt; Parameter estimates:
#&gt; const prop
-#&gt; 2.23224114 0.01262341 </div><div class='input'>
- <span class='va'>f_2_obs</span> <span class='op'>&lt;-</span> <span class='fu'><a href='mmkin.html'>mmkin</a></span><span class='op'>(</span><span class='fu'><a href='https://rdrr.io/r/base/list.html'>list</a></span><span class='op'>(</span><span class='st'>"SFO-SFO"</span> <span class='op'>=</span> <span class='va'>m_sfo_sfo</span>,
- <span class='st'>"DFOP-SFO"</span> <span class='op'>=</span> <span class='va'>m_dfop_sfo</span><span class='op'>)</span>,
- <span class='va'>ds_2</span>, quiet <span class='op'>=</span> <span class='cn'>TRUE</span>, error_model <span class='op'>=</span> <span class='st'>"obs"</span><span class='op'>)</span>
+#&gt; 2.23223147 0.01262395 </div><div class='input'>
+ <span class='va'>f_2_obs</span> <span class='op'>&lt;-</span> <span class='fu'><a href='https://rdrr.io/r/stats/update.html'>update</a></span><span class='op'>(</span><span class='va'>f_2</span>, error_model <span class='op'>=</span> <span class='st'>"obs"</span><span class='op'>)</span>
<span class='va'>f_nlme_sfo_sfo_obs</span> <span class='op'>&lt;-</span> <span class='fu'><a href='https://rdrr.io/pkg/nlme/man/nlme.html'>nlme</a></span><span class='op'>(</span><span class='va'>f_2_obs</span><span class='op'>[</span><span class='st'>"SFO-SFO"</span>, <span class='op'>]</span><span class='op'>)</span>
<span class='fu'><a href='https://rdrr.io/r/base/print.html'>print</a></span><span class='op'>(</span><span class='va'>f_nlme_sfo_sfo_obs</span><span class='op'>)</span>
</div><div class='output co'>#&gt; Kinetic nonlinear mixed-effects model fit by maximum likelihood
@@ -429,18 +442,21 @@ methods that will automatically work on 'nlme.mmkin' objects, such as
#&gt; Formula: ~1 | name
#&gt; Parameter estimates:
#&gt; parent A1
-#&gt; 1.0000000 0.2050003 </div><div class='input'> <span class='va'>f_nlme_dfop_sfo_obs</span> <span class='op'>&lt;-</span> <span class='fu'><a href='https://rdrr.io/pkg/nlme/man/nlme.html'>nlme</a></span><span class='op'>(</span><span class='va'>f_2_obs</span><span class='op'>[</span><span class='st'>"DFOP-SFO"</span>, <span class='op'>]</span><span class='op'>)</span>
-</div><div class='output co'>#&gt; <span class='error'>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</span></div><div class='output co'>#&gt; <span class='message'>Timing stopped at: 59.38 16.5 53.5</span></div><div class='input'>
- <span class='va'>f_2_tc</span> <span class='op'>&lt;-</span> <span class='fu'><a href='mmkin.html'>mmkin</a></span><span class='op'>(</span><span class='fu'><a href='https://rdrr.io/r/base/list.html'>list</a></span><span class='op'>(</span><span class='st'>"SFO-SFO"</span> <span class='op'>=</span> <span class='va'>m_sfo_sfo</span>,
- <span class='st'>"DFOP-SFO"</span> <span class='op'>=</span> <span class='va'>m_dfop_sfo</span><span class='op'>)</span>,
- <span class='va'>ds_2</span>, quiet <span class='op'>=</span> <span class='cn'>TRUE</span>, error_model <span class='op'>=</span> <span class='st'>"tc"</span><span class='op'>)</span>
- <span class='co'># f_nlme_sfo_sfo_tc &lt;- nlme(f_2_tc["SFO-SFO", ]) # stops with error message</span>
- <span class='va'>f_nlme_dfop_sfo_tc</span> <span class='op'>&lt;-</span> <span class='fu'><a href='https://rdrr.io/pkg/nlme/man/nlme.html'>nlme</a></span><span class='op'>(</span><span class='va'>f_2_tc</span><span class='op'>[</span><span class='st'>"DFOP-SFO"</span>, <span class='op'>]</span><span class='op'>)</span>
-</div><div class='output co'>#&gt; <span class='warning'>Warning: longer object length is not a multiple of shorter object length</span></div><div class='output co'>#&gt; <span class='warning'>Warning: longer object length is not a multiple of shorter object length</span></div><div class='output co'>#&gt; <span class='warning'>Warning: longer object length is not a multiple of shorter object length</span></div><div class='output co'>#&gt; <span class='warning'>Warning: longer object length is not a multiple of shorter object length</span></div><div class='output co'>#&gt; <span class='warning'>Warning: longer object length is not a multiple of shorter object length</span></div><div class='output co'>#&gt; <span class='warning'>Warning: longer object length is not a multiple of shorter object length</span></div><div class='output co'>#&gt; <span class='warning'>Warning: longer object length is not a multiple of shorter object length</span></div><div class='output co'>#&gt; <span class='warning'>Warning: longer object length is not a multiple of shorter object length</span></div><div class='output co'>#&gt; <span class='warning'>Warning: longer object length is not a multiple of shorter object length</span></div><div class='output co'>#&gt; <span class='warning'>Warning: longer object length is not a multiple of shorter object length</span></div><div class='output co'>#&gt; <span class='warning'>Warning: longer object length is not a multiple of shorter object length</span></div><div class='output co'>#&gt; <span class='warning'>Warning: longer object length is not a multiple of shorter object length</span></div><div class='output co'>#&gt; <span class='warning'>Warning: longer object length is not a multiple of shorter object length</span></div><div class='output co'>#&gt; <span class='warning'>Warning: longer object length is not a multiple of shorter object length</span></div><div class='output co'>#&gt; <span class='error'>Error in X[, fmap[[nm]]] &lt;- gradnm: number of items to replace is not a multiple of replacement length</span></div><div class='output co'>#&gt; <span class='message'>Timing stopped at: 6.363 2.688 5.469</span></div><div class='input'> <span class='co'># We get warnings about false convergence in the LME step in several iterations</span>
- <span class='co'># but as the last such warning occurs in iteration 25 and we have 28 iterations</span>
- <span class='co'># we can ignore these</span>
- <span class='fu'><a href='https://rdrr.io/r/stats/anova.html'>anova</a></span><span class='op'>(</span><span class='va'>f_nlme_dfop_sfo</span>, <span class='va'>f_nlme_dfop_sfo_obs</span>, <span class='va'>f_nlme_dfop_sfo_tc</span><span class='op'>)</span>
-</div><div class='output co'>#&gt; <span class='error'>Error in anova(f_nlme_dfop_sfo, f_nlme_dfop_sfo_obs, f_nlme_dfop_sfo_tc): object 'f_nlme_dfop_sfo' not found</span></div><div class='input'>
+#&gt; 1.0000000 0.2049995 </div><div class='input'> <span class='va'>f_nlme_dfop_sfo_obs</span> <span class='op'>&lt;-</span> <span class='fu'><a href='https://rdrr.io/pkg/nlme/man/nlme.html'>nlme</a></span><span class='op'>(</span><span class='va'>f_2_obs</span><span class='op'>[</span><span class='st'>"DFOP-SFO"</span>, <span class='op'>]</span>,
+ control <span class='op'>=</span> <span class='fu'><a href='https://rdrr.io/r/base/list.html'>list</a></span><span class='op'>(</span>pnlsMaxIter <span class='op'>=</span> <span class='fl'>120</span>, tolerance <span class='op'>=</span> <span class='fl'>5e-4</span><span class='op'>)</span><span class='op'>)</span>
+
+ <span class='va'>f_2_tc</span> <span class='op'>&lt;-</span> <span class='fu'><a href='https://rdrr.io/r/stats/update.html'>update</a></span><span class='op'>(</span><span class='va'>f_2</span>, error_model <span class='op'>=</span> <span class='st'>"tc"</span><span class='op'>)</span>
+ <span class='co'># f_nlme_sfo_sfo_tc &lt;- nlme(f_2_tc["SFO-SFO", ]) # No convergence with 50 iterations</span>
+ <span class='co'># f_nlme_dfop_sfo_tc &lt;- nlme(f_2_tc["DFOP-SFO", ],</span>
+ <span class='co'># control = list(pnlsMaxIter = 120, tolerance = 5e-4)) # Error in X[, fmap[[nm]]] &lt;- gradnm</span>
+
+ <span class='fu'><a href='https://rdrr.io/r/stats/anova.html'>anova</a></span><span class='op'>(</span><span class='va'>f_nlme_dfop_sfo</span>, <span class='va'>f_nlme_dfop_sfo_obs</span><span class='op'>)</span>
+</div><div class='output co'>#&gt; Model df AIC BIC logLik Test L.Ratio
+#&gt; f_nlme_dfop_sfo 1 13 843.8548 884.6201 -408.9274
+#&gt; f_nlme_dfop_sfo_obs 2 14 817.5338 861.4350 -394.7669 1 vs 2 28.32093
+#&gt; p-value
+#&gt; f_nlme_dfop_sfo
+#&gt; f_nlme_dfop_sfo_obs &lt;.0001</div><div class='input'>
<span class='co'># }</span>
</div></pre>
</div>

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