diff options
Diffstat (limited to 'docs/dev/reference/nlme.mmkin.html')
| -rw-r--r-- | docs/dev/reference/nlme.mmkin.html | 106 |
1 files changed, 41 insertions, 65 deletions
diff --git a/docs/dev/reference/nlme.mmkin.html b/docs/dev/reference/nlme.mmkin.html index 6d9f2007..a4d7070a 100644 --- a/docs/dev/reference/nlme.mmkin.html +++ b/docs/dev/reference/nlme.mmkin.html @@ -154,11 +154,12 @@ have been obtained by fitting the same model to a list of datasets.</p> <pre class="usage"><span class='co'># S3 method for mmkin</span> <span class='fu'><a href='https://rdrr.io/pkg/nlme/man/nlme.html'>nlme</a></span><span class='op'>(</span> <span class='va'>model</span>, - data <span class='op'>=</span> <span class='fu'><a href='https://rdrr.io/r/base/sys.parent.html'>sys.frame</a></span><span class='op'>(</span><span class='fu'><a href='https://rdrr.io/r/base/sys.parent.html'>sys.parent</a></span><span class='op'>(</span><span class='op'>)</span><span class='op'>)</span>, - <span class='va'>fixed</span>, - random <span class='op'>=</span> <span class='va'>fixed</span>, + 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>, <span class='va'>groups</span>, - <span class='va'>start</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>, weights <span class='op'>=</span> <span class='cn'>NULL</span>, <span class='va'>subset</span>, @@ -276,14 +277,14 @@ methods that will automatically work on 'nlme.mmkin' objects, such as <span class='va'>f</span> <span class='op'><-</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/c.html'>c</a></span><span class='op'>(</span><span class='st'>"SFO"</span>, <span class='st'>"DFOP"</span><span class='op'>)</span>, <span class='va'>ds</span>, quiet <span class='op'>=</span> <span class='cn'>TRUE</span>, cores <span class='op'>=</span> <span class='fl'>1</span><span class='op'>)</span> <span class='kw'><a href='https://rdrr.io/r/base/library.html'>library</a></span><span class='op'>(</span><span class='va'><a href='https://svn.r-project.org/R-packages/trunk/nlme/'>nlme</a></span><span class='op'>)</span> <span class='va'>f_nlme_sfo</span> <span class='op'><-</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</span><span class='op'>[</span><span class='st'>"SFO"</span>, <span class='op'>]</span><span class='op'>)</span> -</div><div class='output co'>#> <span class='warning'>Warning: Iteration 1, LME step: nlminb() did not converge (code = 1). Do increase 'msMaxIter'!</span></div><div class='input'> + <span class='co'># \dontrun{</span> <span class='va'>f_nlme_dfop</span> <span class='op'><-</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</span><span class='op'>[</span><span class='st'>"DFOP"</span>, <span class='op'>]</span><span class='op'>)</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_sfo</span>, <span class='va'>f_nlme_dfop</span><span class='op'>)</span> </div><div class='output co'>#> Model df AIC BIC logLik Test L.Ratio p-value -#> f_nlme_sfo 1 6 622.0677 637.0666 -305.0338 -#> f_nlme_dfop 2 15 487.0134 524.5105 -228.5067 1 vs 2 153.0543 <.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> +#> 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</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'>#> Kinetic nonlinear mixed-effects model fit by maximum likelihood #> #> Structural model: @@ -294,28 +295,24 @@ methods that will automatically work on 'nlme.mmkin' objects, such as #> Data: #> 90 observations of 1 variable(s) grouped in 5 datasets #> -#> Log-likelihood: -228.5067 +#> Log-likelihood: -238.6 #> #> Fixed effects: #> list(parent_0 ~ 1, log_k1 ~ 1, log_k2 ~ 1, g_qlogis ~ 1) #> parent_0 log_k1 log_k2 g_qlogis -#> 94.18273 -1.82135 -4.16872 0.08949 +#> 94.1702 -1.8002 -4.1474 0.0324 #> #> Random effects: #> Formula: list(parent_0 ~ 1, log_k1 ~ 1, log_k2 ~ 1, g_qlogis ~ 1) #> Level: ds -#> Structure: General positive-definite, Log-Cholesky parametrization -#> StdDev Corr -#> parent_0 2.4656397 prnt_0 log_k1 log_k2 -#> log_k1 0.7950788 0.240 -#> log_k2 1.2605419 0.150 0.984 -#> g_qlogis 0.5013272 -0.075 0.843 0.834 -#> Residual 2.3308100 +#> Structure: Diagonal +#> parent_0 log_k1 log_k2 g_qlogis Residual +#> StdDev: 2.488 0.8447 1.33 0.4652 2.321 #> </div><div class='input'> <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</span><span class='op'>)</span> </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'>#> $distimes #> DT50 DT90 DT50back DT50_k1 DT50_k2 -#> parent 10.57119 101.0652 30.42366 4.283776 44.80015 +#> parent 10.79857 100.7937 30.34192 4.193937 43.85442 #> </div><div class='input'> <span class='va'>ds_2</span> <span class='op'><-</span> <span class='fu'><a href='https://rdrr.io/r/base/lapply.html'>lapply</a></span><span class='op'>(</span><span class='va'>experimental_data_for_UBA_2019</span><span class='op'>[</span><span class='fl'>6</span><span class='op'>:</span><span class='fl'>10</span><span class='op'>]</span>, <span class='kw'>function</span><span class='op'>(</span><span class='va'>x</span><span class='op'>)</span> <span class='va'>x</span><span class='op'>$</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> @@ -332,7 +329,7 @@ methods that will automatically work on 'nlme.mmkin' objects, such as <span class='va'>ds_2</span>, quiet <span class='op'>=</span> <span class='cn'>TRUE</span><span class='op'>)</span> <span class='va'>f_nlme_sfo_sfo</span> <span class='op'><-</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'>"SFO-SFO"</span>, <span class='op'>]</span><span class='op'>)</span> -</div><div class='output co'>#> <span class='warning'>Warning: Iteration 1, LME step: nlminb() did not converge (code = 1). Do increase 'msMaxIter'!</span></div><div class='output co'>#> <span class='warning'>Warning: Iteration 2, LME step: nlminb() did not converge (code = 1). Do increase 'msMaxIter'!</span></div><div class='input'> <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_sfo_sfo</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_sfo_sfo</span><span class='op'>)</span> </div><div class='img'><img src='nlme.mmkin-2.png' alt='' width='700' height='433' /></div><div class='input'> <span class='co'># With formation fractions this does not coverge with defaults</span> <span class='co'># f_nlme_sfo_sfo_ff <- nlme(f_2["SFO-SFO-ff", ])</span> @@ -343,32 +340,22 @@ methods that will automatically work on 'nlme.mmkin' objects, such as <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'><-</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'>#> <span class='warning'>Warning: Iteration 1, LME step: nlminb() did not converge (code = 1). Do increase 'msMaxIter'!</span></div><div class='output co'>#> <span class='warning'>Warning: Iteration 2, LME step: nlminb() did not converge (code = 1). Do increase 'msMaxIter'!</span></div><div class='output co'>#> <span class='warning'>Warning: Iteration 3, LME step: nlminb() did not converge (code = 1). Do increase 'msMaxIter'!</span></div><div class='output co'>#> <span class='warning'>Warning: Iteration 4, LME step: nlminb() did not converge (code = 1). Do increase 'msMaxIter'!</span></div><div class='input'> +</div><div class='output co'>#> <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'>#> <span class='message'>Timing stopped at: 49.95 16.5 44.08</span></div><div class='input'> <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='img'><img src='nlme.mmkin-3.png' alt='' width='700' height='433' /></div><div class='input'> +</div><div class='output co'>#> <span class='error'>Error in plot(f_nlme_dfop_sfo): object 'f_nlme_dfop_sfo' not found</span></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'>#> Model df AIC BIC logLik Test L.Ratio p-value -#> f_nlme_dfop_sfo 1 28 811.7199 899.5222 -377.8599 -#> f_nlme_sfo_sfo 2 15 1075.1934 1122.2304 -522.5967 1 vs 2 289.4736 <.0001</div><div class='input'> +</div><div class='output co'>#> <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'> <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'>#> $ff #> parent_sink parent_A1 A1_sink -#> 0.6512742 0.3487258 1.0000000 +#> 0.5912432 0.4087568 1.0000000 #> #> $distimes -#> DT50 DT90 -#> parent 18.03144 59.89916 -#> A1 102.72949 341.25997 +#> DT50 DT90 +#> parent 19.13518 63.5657 +#> A1 66.02155 219.3189 #> </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'>#> $ff -#> parent_A1 parent_sink -#> 0.2762167 0.7237833 -#> -#> $distimes -#> DT50 DT90 DT50back DT50_k1 DT50_k2 -#> parent 11.15024 133.9652 40.32755 4.688015 62.16017 -#> A1 235.83191 783.4167 NA NA NA -#> </div><div class='input'> +</div><div class='output co'>#> <span class='error'>Error in endpoints(f_nlme_dfop_sfo): object 'f_nlme_dfop_sfo' not found</span></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'>></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> @@ -379,7 +366,7 @@ methods that will automatically work on 'nlme.mmkin' objects, such as <span class='fu'><a href='https://rdrr.io/r/stats/AIC.html'>AIC</a></span><span class='op'>(</span><span class='va'>f_nlme_sfo</span>, <span class='va'>f_nlme_sfo_tc</span>, <span class='va'>f_nlme_dfop</span>, <span class='va'>f_nlme_dfop_tc</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_dfop_tc</span><span class='op'>)</span> <span class='op'>}</span> -</div><div class='output co'>#> <span class='warning'>Warning: Iteration 1, LME step: nlminb() did not converge (code = 1). Do increase 'msMaxIter'!</span></div><div class='output co'>#> <span class='warning'>Warning: Iteration 14, LME step: nlminb() did not converge (code = 1). PORT message: false convergence (8)</span></div><div class='output co'>#> <span class='warning'>Warning: Iteration 1, LME step: nlminb() did not converge (code = 1). Do increase 'msMaxIter'!</span></div><div class='output co'>#> <span class='warning'>Warning: Iteration 2, LME step: nlminb() did not converge (code = 1). Do increase 'msMaxIter'!</span></div><div class='output co'>#> <span class='warning'>Warning: Iteration 4, LME step: nlminb() did not converge (code = 1). PORT message: false convergence (8)</span></div><div class='output co'>#> <span class='warning'>Warning: Iteration 5, LME step: nlminb() did not converge (code = 1). PORT message: false convergence (8)</span></div><div class='output co'>#> <span class='warning'>Warning: Iteration 6, LME step: nlminb() did not converge (code = 1). PORT message: false convergence (8)</span></div><div class='output co'>#> <span class='warning'>Warning: Iteration 7, LME step: nlminb() did not converge (code = 1). PORT message: false convergence (8)</span></div><div class='output co'>#> <span class='warning'>Warning: Iteration 8, LME step: nlminb() did not converge (code = 1). PORT message: false convergence (8)</span></div><div class='output co'>#> <span class='warning'>Warning: Iteration 9, LME step: nlminb() did not converge (code = 1). PORT message: false convergence (8)</span></div><div class='output co'>#> <span class='warning'>Warning: Iteration 10, LME step: nlminb() did not converge (code = 1). PORT message: false convergence (8)</span></div><div class='output co'>#> <span class='warning'>Warning: Iteration 11, LME step: nlminb() did not converge (code = 1). PORT message: false convergence (8)</span></div><div class='output co'>#> <span class='warning'>Warning: Iteration 12, LME step: nlminb() did not converge (code = 1). PORT message: false convergence (8)</span></div><div class='output co'>#> <span class='warning'>Warning: Iteration 14, LME step: nlminb() did not converge (code = 1). PORT message: false convergence (8)</span></div><div class='output co'>#> <span class='warning'>Warning: Iteration 15, LME step: nlminb() did not converge (code = 1). PORT message: false convergence (8)</span></div><div class='output co'>#> <span class='warning'>Warning: Iteration 16, LME step: nlminb() did not converge (code = 1). PORT message: false convergence (8)</span></div><div class='output co'>#> <span class='warning'>Warning: Iteration 17, LME step: nlminb() did not converge (code = 1). PORT message: false convergence (8)</span></div><div class='output co'>#> <span class='warning'>Warning: Iteration 18, LME step: nlminb() did not converge (code = 1). PORT message: false convergence (8)</span></div><div class='output co'>#> Kinetic nonlinear mixed-effects model fit by maximum likelihood +</div><div class='output co'>#> Kinetic nonlinear mixed-effects model fit by maximum likelihood #> #> Structural model: #> d_parent/dt = - ((k1 * g * exp(-k1 * time) + k2 * (1 - g) * exp(-k2 * @@ -389,35 +376,31 @@ methods that will automatically work on 'nlme.mmkin' objects, such as #> Data: #> 90 observations of 1 variable(s) grouped in 5 datasets #> -#> Log-likelihood: -228.3575 +#> Log-likelihood: -238.4 #> #> Fixed effects: #> list(parent_0 ~ 1, log_k1 ~ 1, log_k2 ~ 1, g_qlogis ~ 1) #> parent_0 log_k1 log_k2 g_qlogis -#> 93.6695 -1.9187 -4.4253 0.2215 +#> 94.04775 -1.82340 -4.16715 0.05685 #> #> Random effects: #> Formula: list(parent_0 ~ 1, log_k1 ~ 1, log_k2 ~ 1, g_qlogis ~ 1) #> Level: ds -#> Structure: General positive-definite, Log-Cholesky parametrization -#> StdDev Corr -#> parent_0 2.8574651 prnt_0 log_k1 log_k2 -#> log_k1 0.9689083 0.506 -#> log_k2 1.5798002 0.446 0.997 -#> g_qlogis 0.5761569 -0.457 0.247 0.263 -#> Residual 1.0000000 +#> Structure: Diagonal +#> parent_0 log_k1 log_k2 g_qlogis Residual +#> StdDev: 2.474 0.85 1.337 0.4659 1 #> #> Variance function: #> Structure: Constant plus proportion of variance covariate #> Formula: ~fitted(.) #> Parameter estimates: -#> const prop -#> 2.0376990 0.0221686 </div><div class='input'> +#> const prop +#> 2.23224114 0.01262341 </div><div class='input'> <span class='va'>f_2_obs</span> <span class='op'><-</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> <span class='va'>f_nlme_sfo_sfo_obs</span> <span class='op'><-</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> -</div><div class='output co'>#> <span class='warning'>Warning: Iteration 1, LME step: nlminb() did not converge (code = 1). Do increase 'msMaxIter'!</span></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_sfo_sfo_obs</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'>#> Kinetic nonlinear mixed-effects model fit by maximum likelihood #> #> Structural model: @@ -427,44 +410,37 @@ methods that will automatically work on 'nlme.mmkin' objects, such as #> Data: #> 170 observations of 2 variable(s) grouped in 5 datasets #> -#> Log-likelihood: -462.2203 +#> Log-likelihood: -473 #> #> Fixed effects: #> list(parent_0 ~ 1, log_k_parent_sink ~ 1, log_k_parent_A1 ~ 1, log_k_A1_sink ~ 1) #> parent_0 log_k_parent_sink log_k_parent_A1 log_k_A1_sink -#> 88.682 -3.664 -4.164 -4.665 +#> 87.976 -3.670 -4.164 -4.645 #> #> Random effects: #> Formula: list(parent_0 ~ 1, log_k_parent_sink ~ 1, log_k_parent_A1 ~ 1, log_k_A1_sink ~ 1) #> Level: ds -#> Structure: General positive-definite, Log-Cholesky parametrization -#> StdDev Corr -#> parent_0 4.9153305 prnt_0 lg_k__ l___A1 -#> log_k_parent_sink 1.8158570 0.956 -#> log_k_parent_A1 1.0514548 0.821 0.907 -#> log_k_A1_sink 0.4924122 0.035 0.315 0.533 -#> Residual 6.3987599 +#> Structure: Diagonal +#> parent_0 log_k_parent_sink log_k_parent_A1 log_k_A1_sink Residual +#> StdDev: 3.992 1.777 1.055 0.4821 6.483 #> #> Variance function: #> Structure: Different standard deviations per stratum #> Formula: ~1 | name #> Parameter estimates: #> parent A1 -#> 1.0000000 0.2040647 </div><div class='input'> <span class='va'>f_nlme_dfop_sfo_obs</span> <span class='op'><-</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'>#> <span class='warning'>Warning: Iteration 1, LME step: nlminb() did not converge (code = 1). Do increase 'msMaxIter'!</span></div><div class='output co'>#> <span class='warning'>Warning: Iteration 2, LME step: nlminb() did not converge (code = 1). Do increase 'msMaxIter'!</span></div><div class='output co'>#> <span class='warning'>Warning: Iteration 3, LME step: nlminb() did not converge (code = 1). Do increase 'msMaxIter'!</span></div><div class='output co'>#> <span class='warning'>Warning: Iteration 4, LME step: nlminb() did not converge (code = 1). Do increase 'msMaxIter'!</span></div><div class='input'> +#> 1.0000000 0.2050003 </div><div class='input'> <span class='va'>f_nlme_dfop_sfo_obs</span> <span class='op'><-</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'>#> <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'>#> <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'><-</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 <- nlme(f_2_tc["SFO-SFO", ]) # stops with error message</span> <span class='va'>f_nlme_dfop_sfo_tc</span> <span class='op'><-</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'>#> <span class='warning'>Warning: Iteration 1, LME step: nlminb() did not converge (code = 1). Do increase 'msMaxIter'!</span></div><div class='output co'>#> <span class='warning'>Warning: Iteration 2, LME step: nlminb() did not converge (code = 1). Do increase 'msMaxIter'!</span></div><div class='output co'>#> <span class='warning'>Warning: Iteration 3, LME step: nlminb() did not converge (code = 1). Do increase 'msMaxIter'!</span></div><div class='output co'>#> <span class='warning'>Warning: Iteration 4, LME step: nlminb() did not converge (code = 1). Do increase 'msMaxIter'!</span></div><div class='output co'>#> <span class='warning'>Warning: Iteration 6, LME step: nlminb() did not converge (code = 1). PORT message: false convergence (8)</span></div><div class='output co'>#> <span class='warning'>Warning: Iteration 7, LME step: nlminb() did not converge (code = 1). PORT message: false convergence (8)</span></div><div class='output co'>#> <span class='warning'>Warning: Iteration 8, LME step: nlminb() did not converge (code = 1). PORT message: false convergence (8)</span></div><div class='output co'>#> <span class='warning'>Warning: Iteration 9, LME step: nlminb() did not converge (code = 1). PORT message: false convergence (8)</span></div><div class='output co'>#> <span class='warning'>Warning: Iteration 11, LME step: nlminb() did not converge (code = 1). PORT message: false convergence (8)</span></div><div class='output co'>#> <span class='warning'>Warning: Iteration 12, LME step: nlminb() did not converge (code = 1). PORT message: false convergence (8)</span></div><div class='output co'>#> <span class='warning'>Warning: Iteration 15, LME step: nlminb() did not converge (code = 1). PORT message: false convergence (8)</span></div><div class='output co'>#> <span class='warning'>Warning: Iteration 25, LME step: nlminb() did not converge (code = 1). PORT message: false convergence (8)</span></div><div class='input'> <span class='co'># We get warnings about false convergence in the LME step in several iterations</span> +</div><div class='output co'>#> <span class='warning'>Warning: longer object length is not a multiple of shorter object length</span></div><div class='output co'>#> <span class='warning'>Warning: longer object length is not a multiple of shorter object length</span></div><div class='output co'>#> <span class='warning'>Warning: longer object length is not a multiple of shorter object length</span></div><div class='output co'>#> <span class='warning'>Warning: longer object length is not a multiple of shorter object length</span></div><div class='output co'>#> <span class='warning'>Warning: longer object length is not a multiple of shorter object length</span></div><div class='output co'>#> <span class='warning'>Warning: longer object length is not a multiple of shorter object length</span></div><div class='output co'>#> <span class='warning'>Warning: longer object length is not a multiple of shorter object length</span></div><div class='output co'>#> <span class='warning'>Warning: longer object length is not a multiple of shorter object length</span></div><div class='output co'>#> <span class='warning'>Warning: longer object length is not a multiple of shorter object length</span></div><div class='output co'>#> <span class='warning'>Warning: longer object length is not a multiple of shorter object length</span></div><div class='output co'>#> <span class='warning'>Warning: longer object length is not a multiple of shorter object length</span></div><div class='output co'>#> <span class='warning'>Warning: longer object length is not a multiple of shorter object length</span></div><div class='output co'>#> <span class='warning'>Warning: longer object length is not a multiple of shorter object length</span></div><div class='output co'>#> <span class='warning'>Warning: longer object length is not a multiple of shorter object length</span></div><div class='output co'>#> <span class='error'>Error in X[, fmap[[nm]]] <- gradnm: number of items to replace is not a multiple of replacement length</span></div><div class='output co'>#> <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'>#> Model df AIC BIC logLik Test L.Ratio p-value -#> f_nlme_dfop_sfo 1 28 811.7199 899.5222 -377.8599 -#> f_nlme_dfop_sfo_obs 2 29 784.1304 875.0685 -363.0652 1 vs 2 29.5895 <.0001 -#> f_nlme_dfop_sfo_tc 3 29 791.9981 882.9362 -366.9990 </div><div class='input'> +</div><div class='output co'>#> <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'> <span class='co'># }</span> </div></pre> </div> |