diff options
| author | Johannes Ranke <jranke@uni-bremen.de> | 2020-10-08 09:31:35 +0200 |
|---|---|---|
| committer | Johannes Ranke <jranke@uni-bremen.de> | 2020-10-08 09:31:35 +0200 |
| commit | bc3825ae2d12c18ea3d3caf17eb23c93fef180b8 (patch) | |
| tree | 112e70a29db2fb35dd624af20f4d400c579b0283 /vignettes/FOCUS_L.html | |
| parent | c7635af214729d2dc15dd8fbee2ebe6bc64493a4 (diff) | |
Fix issues for release
Diffstat (limited to 'vignettes/FOCUS_L.html')
| -rw-r--r-- | vignettes/FOCUS_L.html | 154 |
1 files changed, 77 insertions, 77 deletions
diff --git a/vignettes/FOCUS_L.html b/vignettes/FOCUS_L.html index 7573ef58..c7722f37 100644 --- a/vignettes/FOCUS_L.html +++ b/vignettes/FOCUS_L.html @@ -11,7 +11,7 @@ <meta name="author" content="Johannes Ranke" /> -<meta name="date" content="2020-05-26" /> +<meta name="date" content="2020-10-08" /> <title>Example evaluation of FOCUS Laboratory Data L1 to L3</title> @@ -1518,7 +1518,7 @@ div.tocify { <h1 class="title toc-ignore">Example evaluation of FOCUS Laboratory Data L1 to L3</h1> <h4 class="author">Johannes Ranke</h4> -<h4 class="date">2020-05-26</h4> +<h4 class="date">2020-10-08</h4> </div> @@ -1538,30 +1538,30 @@ FOCUS_2006_L1_mkin <- mkin_wide_to_long(FOCUS_2006_L1)</code></pre> <pre class="r"><code>m.L1.SFO <- mkinfit("SFO", FOCUS_2006_L1_mkin, quiet = TRUE) summary(m.L1.SFO)</code></pre> <pre><code>## mkin version used for fitting: 0.9.50.3 -## R version used for fitting: 4.0.0 -## Date of fit: Tue May 26 17:01:08 2020 -## Date of summary: Tue May 26 17:01:08 2020 +## R version used for fitting: 4.0.2 +## Date of fit: Thu Oct 8 09:06:20 2020 +## Date of summary: Thu Oct 8 09:06:20 2020 ## ## Equations: -## d_parent/dt = - k_parent_sink * parent +## d_parent/dt = - k_parent * parent ## ## Model predictions using solution type analytical ## -## Fitted using 133 model solutions performed in 0.031 s +## Fitted using 133 model solutions performed in 0.032 s ## ## Error model: Constant variance ## ## Error model algorithm: OLS ## ## Starting values for parameters to be optimised: -## value type -## parent_0 89.85 state -## k_parent_sink 0.10 deparm +## value type +## parent_0 89.85 state +## k_parent 0.10 deparm ## ## Starting values for the transformed parameters actually optimised: -## value lower upper -## parent_0 89.850000 -Inf Inf -## log_k_parent_sink -2.302585 -Inf Inf +## value lower upper +## parent_0 89.850000 -Inf Inf +## log_k_parent -2.302585 -Inf Inf ## ## Fixed parameter values: ## None @@ -1572,25 +1572,25 @@ summary(m.L1.SFO)</code></pre> ## 93.88778 96.5589 -43.94389 ## ## Optimised, transformed parameters with symmetric confidence intervals: -## Estimate Std. Error Lower Upper -## parent_0 92.470 1.28200 89.740 95.200 -## log_k_parent_sink -2.347 0.03763 -2.428 -2.267 -## sigma 2.780 0.46330 1.792 3.767 +## Estimate Std. Error Lower Upper +## parent_0 92.470 1.28200 89.740 95.200 +## log_k_parent -2.347 0.03763 -2.428 -2.267 +## sigma 2.780 0.46330 1.792 3.767 ## ## Parameter correlation: -## parent_0 log_k_parent_sink sigma -## parent_0 1.000e+00 6.186e-01 -1.516e-09 -## log_k_parent_sink 6.186e-01 1.000e+00 -3.124e-09 -## sigma -1.516e-09 -3.124e-09 1.000e+00 +## parent_0 log_k_parent sigma +## parent_0 1.000e+00 6.186e-01 -1.516e-09 +## log_k_parent 6.186e-01 1.000e+00 -3.124e-09 +## sigma -1.516e-09 -3.124e-09 1.000e+00 ## ## Backtransformed parameters: ## Confidence intervals for internally transformed parameters are asymmetric. ## t-test (unrealistically) based on the assumption of normal distribution ## for estimators of untransformed parameters. -## Estimate t value Pr(>t) Lower Upper -## parent_0 92.47000 72.13 8.824e-21 89.74000 95.2000 -## k_parent_sink 0.09561 26.57 2.487e-14 0.08824 0.1036 -## sigma 2.78000 6.00 1.216e-05 1.79200 3.7670 +## Estimate t value Pr(>t) Lower Upper +## parent_0 92.47000 72.13 8.824e-21 89.74000 95.2000 +## k_parent 0.09561 26.57 2.487e-14 0.08824 0.1036 +## sigma 2.78000 6.00 1.216e-05 1.79200 3.7670 ## ## FOCUS Chi2 error levels in percent: ## err.min n.optim df @@ -1639,21 +1639,16 @@ summary(m.L1.SFO)</code></pre> <pre><code>## Warning in cov2cor(ans$covar): diag(.) had 0 or NA entries; non-finite result is ## doubtful</code></pre> <pre><code>## mkin version used for fitting: 0.9.50.3 -## R version used for fitting: 4.0.0 -## Date of fit: Tue May 26 17:01:09 2020 -## Date of summary: Tue May 26 17:01:09 2020 -## -## -## Warning: Optimisation did not converge: -## false convergence (8) -## +## R version used for fitting: 4.0.2 +## Date of fit: Thu Oct 8 09:06:21 2020 +## Date of summary: Thu Oct 8 09:06:21 2020 ## ## Equations: ## d_parent/dt = - (alpha/beta) * 1/((time/beta) + 1) * parent ## ## Model predictions using solution type analytical ## -## Fitted using 380 model solutions performed in 0.08 s +## Fitted using 380 model solutions performed in 0.088 s ## ## Error model: Constant variance ## @@ -1674,6 +1669,11 @@ summary(m.L1.SFO)</code></pre> ## Fixed parameter values: ## None ## +## +## Warning(s): +## Optimisation did not converge: +## false convergence (8) +## ## Results: ## ## AIC BIC logLik @@ -1744,16 +1744,16 @@ plot(m.L2.FOMC, show_residuals = TRUE, <p><img src="data:image/png;base64,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" /><!-- --></p> <pre class="r"><code>summary(m.L2.FOMC, data = FALSE)</code></pre> <pre><code>## mkin version used for fitting: 0.9.50.3 -## R version used for fitting: 4.0.0 -## Date of fit: Tue May 26 17:01:09 2020 -## Date of summary: Tue May 26 17:01:09 2020 +## R version used for fitting: 4.0.2 +## Date of fit: Thu Oct 8 09:06:21 2020 +## Date of summary: Thu Oct 8 09:06:21 2020 ## ## Equations: ## d_parent/dt = - (alpha/beta) * 1/((time/beta) + 1) * parent ## ## Model predictions using solution type analytical ## -## Fitted using 239 model solutions performed in 0.047 s +## Fitted using 239 model solutions performed in 0.05 s ## ## Error model: Constant variance ## @@ -1822,9 +1822,9 @@ plot(m.L2.DFOP, show_residuals = TRUE, show_errmin = TRUE, <p><img src="data:image/png;base64,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" /><!-- --></p> <pre class="r"><code>summary(m.L2.DFOP, data = FALSE)</code></pre> <pre><code>## mkin version used for fitting: 0.9.50.3 -## R version used for fitting: 4.0.0 -## Date of fit: Tue May 26 17:01:09 2020 -## Date of summary: Tue May 26 17:01:09 2020 +## R version used for fitting: 4.0.2 +## Date of fit: Thu Oct 8 09:06:21 2020 +## Date of summary: Thu Oct 8 09:06:21 2020 ## ## Equations: ## d_parent/dt = - ((k1 * g * exp(-k1 * time) + k2 * (1 - g) * exp(-k2 * @@ -1833,7 +1833,7 @@ plot(m.L2.DFOP, show_residuals = TRUE, show_errmin = TRUE, ## ## Model predictions using solution type analytical ## -## Fitted using 572 model solutions performed in 0.13 s +## Fitted using 572 model solutions performed in 0.136 s ## ## Error model: Constant variance ## @@ -1894,8 +1894,8 @@ plot(m.L2.DFOP, show_residuals = TRUE, show_errmin = TRUE, ## parent 2.53 4 2 ## ## Estimated disappearance times: -## DT50 DT90 DT50_k1 DT50_k2 -## parent 0.5335 5.311 0.03009 2.058</code></pre> +## DT50 DT90 DT50back DT50_k1 DT50_k2 +## parent 0.5335 5.311 1.599 0.03009 2.058</code></pre> <p>Here, the DFOP model is clearly the best-fit model for dataset L2 based on the chi^2 error level criterion. However, the failure to calculate the covariance matrix indicates that the parameter estimates correlate excessively. Therefore, the FOMC model may be preferred for this dataset.</p> </div> </div> @@ -1922,9 +1922,9 @@ plot(mm.L3)</code></pre> <p>We can extract the summary and plot for <em>e.g.</em> the DFOP fit, using square brackets for indexing which will result in the use of the summary and plot functions working on mkinfit objects.</p> <pre class="r"><code>summary(mm.L3[["DFOP", 1]])</code></pre> <pre><code>## mkin version used for fitting: 0.9.50.3 -## R version used for fitting: 4.0.0 -## Date of fit: Tue May 26 17:01:10 2020 -## Date of summary: Tue May 26 17:01:10 2020 +## R version used for fitting: 4.0.2 +## Date of fit: Thu Oct 8 09:06:22 2020 +## Date of summary: Thu Oct 8 09:06:22 2020 ## ## Equations: ## d_parent/dt = - ((k1 * g * exp(-k1 * time) + k2 * (1 - g) * exp(-k2 * @@ -1933,7 +1933,7 @@ plot(mm.L3)</code></pre> ## ## Model predictions using solution type analytical ## -## Fitted using 373 model solutions performed in 0.083 s +## Fitted using 373 model solutions performed in 0.085 s ## ## Error model: Constant variance ## @@ -1994,8 +1994,8 @@ plot(mm.L3)</code></pre> ## parent 2.225 4 4 ## ## Estimated disappearance times: -## DT50 DT90 DT50_k1 DT50_k2 -## parent 7.464 123 1.343 50.37 +## DT50 DT90 DT50back DT50_k1 DT50_k2 +## parent 7.464 123 37.03 1.343 50.37 ## ## Data: ## time variable observed predicted residual @@ -2030,30 +2030,30 @@ plot(mm.L4)</code></pre> <p>The <span class="math inline"><em>χ</em><sup>2</sup></span> error level of 3.3% as well as the plot suggest that the SFO model fits very well. The error level at which the <span class="math inline"><em>χ</em><sup>2</sup></span> test passes is slightly lower for the FOMC model. However, the difference appears negligible.</p> <pre class="r"><code>summary(mm.L4[["SFO", 1]], data = FALSE)</code></pre> <pre><code>## mkin version used for fitting: 0.9.50.3 -## R version used for fitting: 4.0.0 -## Date of fit: Tue May 26 17:01:10 2020 -## Date of summary: Tue May 26 17:01:10 2020 +## R version used for fitting: 4.0.2 +## Date of fit: Thu Oct 8 09:06:22 2020 +## Date of summary: Thu Oct 8 09:06:22 2020 ## ## Equations: -## d_parent/dt = - k_parent_sink * parent +## d_parent/dt = - k_parent * parent ## ## Model predictions using solution type analytical ## -## Fitted using 142 model solutions performed in 0.029 s +## Fitted using 142 model solutions performed in 0.03 s ## ## Error model: Constant variance ## ## Error model algorithm: OLS ## ## Starting values for parameters to be optimised: -## value type -## parent_0 96.6 state -## k_parent_sink 0.1 deparm +## value type +## parent_0 96.6 state +## k_parent 0.1 deparm ## ## Starting values for the transformed parameters actually optimised: -## value lower upper -## parent_0 96.600000 -Inf Inf -## log_k_parent_sink -2.302585 -Inf Inf +## value lower upper +## parent_0 96.600000 -Inf Inf +## log_k_parent -2.302585 -Inf Inf ## ## Fixed parameter values: ## None @@ -2064,25 +2064,25 @@ plot(mm.L4)</code></pre> ## 47.12133 47.35966 -20.56067 ## ## Optimised, transformed parameters with symmetric confidence intervals: -## Estimate Std. Error Lower Upper -## parent_0 96.440 1.69900 92.070 100.800 -## log_k_parent_sink -5.030 0.07059 -5.211 -4.848 -## sigma 3.162 0.79050 1.130 5.194 +## Estimate Std. Error Lower Upper +## parent_0 96.440 1.69900 92.070 100.800 +## log_k_parent -5.030 0.07059 -5.211 -4.848 +## sigma 3.162 0.79050 1.130 5.194 ## ## Parameter correlation: -## parent_0 log_k_parent_sink sigma -## parent_0 1.000e+00 5.938e-01 3.387e-07 -## log_k_parent_sink 5.938e-01 1.000e+00 5.830e-07 -## sigma 3.387e-07 5.830e-07 1.000e+00 +## parent_0 log_k_parent sigma +## parent_0 1.000e+00 5.938e-01 3.387e-07 +## log_k_parent 5.938e-01 1.000e+00 5.830e-07 +## sigma 3.387e-07 5.830e-07 1.000e+00 ## ## Backtransformed parameters: ## Confidence intervals for internally transformed parameters are asymmetric. ## t-test (unrealistically) based on the assumption of normal distribution ## for estimators of untransformed parameters. -## Estimate t value Pr(>t) Lower Upper -## parent_0 96.440000 56.77 1.604e-08 92.070000 1.008e+02 -## k_parent_sink 0.006541 14.17 1.578e-05 0.005455 7.842e-03 -## sigma 3.162000 4.00 5.162e-03 1.130000 5.194e+00 +## Estimate t value Pr(>t) Lower Upper +## parent_0 96.440000 56.77 1.604e-08 92.070000 1.008e+02 +## k_parent 0.006541 14.17 1.578e-05 0.005455 7.842e-03 +## sigma 3.162000 4.00 5.162e-03 1.130000 5.194e+00 ## ## FOCUS Chi2 error levels in percent: ## err.min n.optim df @@ -2094,16 +2094,16 @@ plot(mm.L4)</code></pre> ## parent 106 352</code></pre> <pre class="r"><code>summary(mm.L4[["FOMC", 1]], data = FALSE)</code></pre> <pre><code>## mkin version used for fitting: 0.9.50.3 -## R version used for fitting: 4.0.0 -## Date of fit: Tue May 26 17:01:10 2020 -## Date of summary: Tue May 26 17:01:10 2020 +## R version used for fitting: 4.0.2 +## Date of fit: Thu Oct 8 09:06:22 2020 +## Date of summary: Thu Oct 8 09:06:22 2020 ## ## Equations: ## d_parent/dt = - (alpha/beta) * 1/((time/beta) + 1) * parent ## ## Model predictions using solution type analytical ## -## Fitted using 224 model solutions performed in 0.044 s +## Fitted using 224 model solutions performed in 0.046 s ## ## Error model: Constant variance ## |