diff options
Diffstat (limited to 'docs/dev/reference/nlme.mmkin.html')
| -rw-r--r-- | docs/dev/reference/nlme.mmkin.html | 184 |
1 files changed, 83 insertions, 101 deletions
diff --git a/docs/dev/reference/nlme.mmkin.html b/docs/dev/reference/nlme.mmkin.html index 84990521..6d9f2007 100644 --- a/docs/dev/reference/nlme.mmkin.html +++ b/docs/dev/reference/nlme.mmkin.html @@ -193,8 +193,10 @@ mmkin model are used as fixed parameters</p></td> </tr> <tr> <th>random</th> - <td><p>If not specified, all fixed effects are complemented -with uncorrelated random effects</p></td> + <td><p>If not specified, correlated random effects are set up +for all optimised degradation model parameters using the log-Cholesky +parameterization <a href='https://rdrr.io/pkg/nlme/man/pdLogChol.html'>nlme::pdLogChol</a> that is also the default of +the generic <a href='https://rdrr.io/pkg/nlme/man/nlme.html'>nlme</a> method.</p></td> </tr> <tr> <th>groups</th> @@ -274,11 +276,14 @@ 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> -<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/AIC.html'>AIC</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'>#> df AIC -#> f_nlme_sfo 5 625.0539 -#> f_nlme_dfop 9 495.1270</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'>#> <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> </div><div class='output co'>#> Kinetic nonlinear mixed-effects model fit by maximum likelihood #> #> Structural model: @@ -289,48 +294,30 @@ methods that will automatically work on 'nlme.mmkin' objects, such as #> Data: #> 90 observations of 1 variable(s) grouped in 5 datasets #> -#> Log-likelihood: -238.5635 +#> Log-likelihood: -228.5067 #> #> Fixed effects: #> list(parent_0 ~ 1, log_k1 ~ 1, log_k2 ~ 1, g_qlogis ~ 1) #> parent_0 log_k1 log_k2 g_qlogis -#> 94.1702 -1.8002 -4.1474 0.0324 +#> 94.18273 -1.82135 -4.16872 0.08949 #> #> Random effects: #> Formula: list(parent_0 ~ 1, log_k1 ~ 1, log_k2 ~ 1, g_qlogis ~ 1) #> Level: ds -#> 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> +#> 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 +#> </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 10.79857 100.7937 30.34192 4.193937 43.85442 +#> parent 10.57119 101.0652 30.42366 4.283776 44.80015 #> </div><div class='input'> -<span class='co'># \dontrun{</span> - <span class='va'>f_nlme_2</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>, start <span class='op'>=</span> <span class='fu'><a href='https://rdrr.io/r/base/c.html'>c</a></span><span class='op'>(</span>parent_0 <span class='op'>=</span> <span class='fl'>100</span>, log_k_parent <span class='op'>=</span> <span class='fl'>0.1</span><span class='op'>)</span><span class='op'>)</span> - <span class='fu'><a href='https://rdrr.io/r/stats/update.html'>update</a></span><span class='op'>(</span><span class='va'>f_nlme_2</span>, random <span class='op'>=</span> <span class='va'>parent_0</span> <span class='op'>~</span> <span class='fl'>1</span><span class='op'>)</span> -</div><div class='output co'>#> Kinetic nonlinear mixed-effects model fit by maximum likelihood -#> -#> Structural model: -#> d_parent/dt = - k_parent * parent -#> -#> Data: -#> observations of 0 variable(s) grouped in 0 datasets -#> -#> Log-likelihood: -404.3729 -#> -#> Fixed effects: -#> list(parent_0 ~ 1, log_k_parent ~ 1) -#> parent_0 log_k_parent -#> 75.933 -3.556 -#> -#> Random effects: -#> Formula: parent_0 ~ 1 | ds -#> parent_0 Residual -#> StdDev: 0.002417 21.63 -#> </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='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> <span class='va'>m_sfo_sfo</span> <span class='op'><-</span> <span class='fu'><a href='mkinmod.html'>mkinmod</a></span><span class='op'>(</span>parent <span class='op'>=</span> <span class='fu'><a href='mkinmod.html'>mkinsub</a></span><span class='op'>(</span><span class='st'>"SFO"</span>, <span class='st'>"A1"</span><span class='op'>)</span>, A1 <span class='op'>=</span> <span class='fu'><a href='mkinmod.html'>mkinsub</a></span><span class='op'>(</span><span class='st'>"SFO"</span><span class='op'>)</span>, use_of_ff <span class='op'>=</span> <span class='st'>"min"</span>, quiet <span class='op'>=</span> <span class='cn'>TRUE</span><span class='op'>)</span> @@ -345,66 +332,42 @@ 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> - <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='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> </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</span> - <span class='va'>f_nlme_sfo_sfo_ff</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-ff"</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_sfo_sfo_ff</span><span class='op'>)</span> -</div><div class='img'><img src='nlme.mmkin-3.png' alt='' width='700' height='433' /></div><div class='input'> - <span class='co'># For the following fit 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'><-</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>, verbose <span class='op'>=</span> <span class='cn'>TRUE</span><span class='op'>)</span> -</div><div class='output co'>#> -#> **Iteration 1 -#> LME step: Loglik: -404.9583, nlminb iterations: 1 -#> reStruct parameters: -#> ds1 ds2 ds3 ds4 ds5 ds6 -#> -0.4114356 0.9798646 1.3524300 0.7293315 0.3354323 1.3647313 -#> Beginning PNLS step: .. completed fit_nlme() step. -#> PNLS step: RSS = 630.3633 -#> fixed effects: 93.82269 -5.455993 -0.9601037 -1.862196 -4.199671 0.07824609 -#> iterations: 120 -#> Convergence crit. (must all become <= tolerance = 0.0005): -#> fixed reStruct -#> 0.7897284 0.5822782 -#> -#> **Iteration 2 -#> LME step: Loglik: -407.7755, nlminb iterations: 11 -#> reStruct parameters: -#> ds1 ds2 ds3 ds4 ds5 ds6 -#> -0.37122411 0.00305562 1.44336560 0.72467122 0.30160310 1.40762692 -#> Beginning PNLS step: .. completed fit_nlme() step. -#> PNLS step: RSS = 630.3637 -#> fixed effects: 93.82269 -5.455992 -0.9601036 -1.862196 -4.199671 0.0782462 -#> iterations: 120 -#> Convergence crit. (must all become <= tolerance = 0.0005): -#> fixed reStruct -#> 1.375673e-06 9.758294e-06 </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> + <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'><-</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'> <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-4.png' alt='' width='700' height='433' /></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'>#> Model df AIC BIC logLik Test L.Ratio p-value -#> f_nlme_dfop_sfo 1 13 843.8547 884.6201 -408.9274 -#> f_nlme_sfo_sfo 2 9 1085.1821 1113.4043 -533.5910 1 vs 2 249.3274 <.0001</div><div class='input'> +#> 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'> <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 -#> 0.5912432 0.4087568 1.0000000 +#> 0.6512742 0.3487258 1.0000000 #> #> $distimes -#> DT50 DT90 -#> parent 19.13518 63.5657 -#> A1 66.02155 219.3189 +#> DT50 DT90 +#> parent 18.03144 59.89916 +#> A1 102.72949 341.25997 #> </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 -#> 0.2768574 0.7231426 +#> 0.2762167 0.7237833 #> #> $distimes #> DT50 DT90 DT50back DT50_k1 DT50_k2 -#> parent 11.07091 104.6320 31.49738 4.462384 46.20825 -#> A1 162.30523 539.1663 NA NA NA +#> parent 11.15024 133.9652 40.32755 4.688015 62.16017 +#> A1 235.83191 783.4167 NA NA NA #> </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> @@ -416,7 +379,7 @@ 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'>#> Kinetic nonlinear mixed-effects model fit by maximum likelihood +</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 #> #> Structural model: #> d_parent/dt = - ((k1 * g * exp(-k1 * time) + k2 * (1 - g) * exp(-k2 * @@ -426,31 +389,35 @@ methods that will automatically work on 'nlme.mmkin' objects, such as #> Data: #> 90 observations of 1 variable(s) grouped in 5 datasets #> -#> Log-likelihood: -238.4298 +#> Log-likelihood: -228.3575 #> #> Fixed effects: #> list(parent_0 ~ 1, log_k1 ~ 1, log_k2 ~ 1, g_qlogis ~ 1) #> parent_0 log_k1 log_k2 g_qlogis -#> 94.04775 -1.82340 -4.16715 0.05685 +#> 93.6695 -1.9187 -4.4253 0.2215 #> #> Random effects: #> Formula: list(parent_0 ~ 1, log_k1 ~ 1, log_k2 ~ 1, g_qlogis ~ 1) #> Level: ds -#> Structure: Diagonal -#> parent_0 log_k1 log_k2 g_qlogis Residual -#> StdDev: 2.474 0.85 1.337 0.4659 1 +#> 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 #> #> Variance function: #> Structure: Constant plus proportion of variance covariate #> Formula: ~fitted(.) #> Parameter estimates: -#> const prop -#> 2.23224114 0.01262341 </div><div class='input'> +#> const prop +#> 2.0376990 0.0221686 </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> - <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'>#> <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> </div><div class='output co'>#> Kinetic nonlinear mixed-effects model fit by maximum likelihood #> #> Structural model: @@ -460,29 +427,44 @@ methods that will automatically work on 'nlme.mmkin' objects, such as #> Data: #> 170 observations of 2 variable(s) grouped in 5 datasets #> -#> Log-likelihood: -472.976 +#> Log-likelihood: -462.2203 #> #> 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 -#> 87.976 -3.670 -4.164 -4.645 +#> 88.682 -3.664 -4.164 -4.665 #> #> 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: 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 +#> 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 #> #> Variance function: #> Structure: Different standard deviations per stratum #> Formula: ~1 | name #> Parameter estimates: #> parent A1 -#> 1.0000000 0.2050003 </div><div class='input'> <span class='co'># The same with DFOP-SFO does not converge, apparently the variances of</span> - <span class='co'># parent and A1 are too similar in this case, so that the model is</span> - <span class='co'># overparameterised</span> - <span class='co'>#f_nlme_dfop_sfo_obs <- nlme(f_2_obs["DFOP-SFO", ], control = list(maxIter = 100))</span> +#> 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'> + <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> + <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'> <span class='co'># }</span> </div></pre> </div> |