From 70591022c07f0e8fb4dd67789b7c8d78af8ebc18 Mon Sep 17 00:00:00 2001 From: Johannes Ranke Date: Thu, 2 May 2019 13:17:05 +0200 Subject: Better initials for error model parameters - Also make it possible to specify initial values for error model parameters. - Run tests - Rebuild docs --- docs/articles/FOCUS_L.html | 154 ++++++++++++++++++++++----------------------- 1 file changed, 77 insertions(+), 77 deletions(-) (limited to 'docs/articles/FOCUS_L.html') diff --git a/docs/articles/FOCUS_L.html b/docs/articles/FOCUS_L.html index 8af99f6c..b05963ae 100644 --- a/docs/articles/FOCUS_L.html +++ b/docs/articles/FOCUS_L.html @@ -88,7 +88,7 @@

Example evaluation of FOCUS Laboratory Data L1 to L3

Johannes Ranke

-

2019-04-10

+

2019-05-02

@@ -113,19 +113,19 @@ 
m.L1.SFO <- mkinfit("SFO", FOCUS_2006_L1_mkin, quiet = TRUE)
 summary(m.L1.SFO)
## mkin version used for fitting:    0.9.49.4 
-## R version used for fitting:       3.5.3 
-## Date of fit:     Wed Apr 10 10:11:33 2019 
-## Date of summary: Wed Apr 10 10:11:33 2019 
+## R version used for fitting:       3.6.0 
+## Date of fit:     Thu May  2 12:40:31 2019 
+## Date of summary: Thu May  2 12:40:31 2019 
 ## 
 ## Equations:
 ## d_parent/dt = - k_parent_sink * parent
 ## 
 ## Model predictions using solution type analytical 
 ## 
-## Fitted with method using 133 model solutions performed in 0.344 s
+## Fitted using 133 model solutions performed in 0.289 s
 ## 
 ## Error model:
-## NULL
+## Constant variance 
 ## 
 ## Starting values for parameters to be optimised:
 ##                   value   type
@@ -163,7 +163,7 @@
 ## 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
 ## 
-## Chi2 error levels in percent:
+## FOCUS Chi2 error levels in percent:
 ##          err.min n.optim df
 ## All data   3.424       2  7
 ## parent     3.424       2  7
@@ -214,9 +214,9 @@
 
## Warning in cov2cor(ans$cov.unscaled): diag(.) had 0 or NA entries; non-
 ## finite result is doubtful

 
## mkin version used for fitting:    0.9.49.4 
-## R version used for fitting:       3.5.3 
-## Date of fit:     Wed Apr 10 10:11:34 2019 
-## Date of summary: Wed Apr 10 10:11:34 2019 
+## R version used for fitting:       3.6.0 
+## Date of fit:     Thu May  2 12:40:33 2019 
+## Date of summary: Thu May  2 12:40:33 2019 
 ## 
 ## 
 ## Warning: Optimisation did not converge:
@@ -228,24 +228,24 @@
 ## 
 ## Model predictions using solution type analytical 
 ## 
-## Fitted with method using 344 model solutions performed in 0.778 s
+## Fitted using 599 model solutions performed in 1.277 s
 ## 
 ## Error model:
-## NULL
+## Constant variance 
 ## 
 ## Starting values for parameters to be optimised:
-##             value   type
-## parent_0 89.85000  state
-## alpha     1.00000 deparm
-## beta     10.00000 deparm
-## sigma     2.77987  error
+##              value   type
+## parent_0 89.850000  state
+## alpha     1.000000 deparm
+## beta     10.000000 deparm
+## sigma     2.779868  error
 ## 
 ## Starting values for the transformed parameters actually optimised:
 ##               value lower upper
 ## parent_0  89.850000  -Inf   Inf
 ## log_alpha  0.000000  -Inf   Inf
 ## log_beta   2.302585  -Inf   Inf
-## sigma      2.779870     0   Inf
+## sigma      2.779868     0   Inf
 ## 
 ## Fixed parameter values:
 ## None
@@ -253,16 +253,16 @@
 ## Optimised, transformed parameters with symmetric confidence intervals:
 ##           Estimate Std. Error  Lower  Upper
 ## parent_0     92.47     1.2810 89.720 95.220
-## log_alpha    10.60        NaN    NaN    NaN
-## log_beta     12.95        NaN    NaN    NaN
-## sigma         2.78     0.4554  1.803  3.757
+## log_alpha    10.66        NaN    NaN    NaN
+## log_beta     13.01        NaN    NaN    NaN
+## sigma         2.78     0.4599  1.794  3.766
 ## 
 ## Parameter correlation:
 ##           parent_0 log_alpha log_beta    sigma
-## parent_0  1.000000       NaN      NaN 0.008714
+## parent_0  1.000000       NaN      NaN 0.003475
 ## log_alpha      NaN         1      NaN      NaN
 ## log_beta       NaN       NaN        1      NaN
-## sigma     0.008714       NaN      NaN 1.000000
+## sigma     0.003475       NaN      NaN 1.000000
 ## 
 ## Backtransformed parameters:
 ## Confidence intervals for internally transformed parameters are asymmetric.
@@ -270,11 +270,11 @@
 ## for estimators of untransformed parameters.
 ##           Estimate  t value    Pr(>t)  Lower  Upper
 ## parent_0     92.47 72.13000 1.052e-19 89.720 95.220
-## alpha     40090.00  0.02388 4.906e-01     NA     NA
-## beta     419300.00  0.02388 4.906e-01     NA     NA
-## sigma         2.78  6.00000 1.628e-05  1.803  3.757
+## alpha     42700.00  0.02298 4.910e-01     NA     NA
+## beta     446600.00  0.02298 4.910e-01     NA     NA
+## sigma         2.78  6.00000 1.628e-05  1.794  3.766
 ## 
-## Chi2 error levels in percent:
+## FOCUS Chi2 error levels in percent:
 ##          err.min n.optim df
 ## All data   3.619       3  6
 ## parent     3.619       3  6
@@ -318,19 +318,19 @@
 

 
summary(m.L2.FOMC, data = FALSE)

 
## mkin version used for fitting:    0.9.49.4 
-## R version used for fitting:       3.5.3 
-## Date of fit:     Wed Apr 10 10:11:35 2019 
-## Date of summary: Wed Apr 10 10:11:35 2019 
+## R version used for fitting:       3.6.0 
+## Date of fit:     Thu May  2 12:40:34 2019 
+## Date of summary: Thu May  2 12:40:34 2019 
 ## 
 ## Equations:
 ## d_parent/dt = - (alpha/beta) * 1/((time/beta) + 1) * parent
 ## 
 ## Model predictions using solution type analytical 
 ## 
-## Fitted with method using 240 model solutions performed in 0.564 s
+## Fitted using 240 model solutions performed in 0.506 s
 ## 
 ## Error model:
-## NULL
+## Constant variance 
 ## 
 ## Starting values for parameters to be optimised:
 ##              value   type
@@ -373,7 +373,7 @@
 ## beta        1.234   4.012 1.942e-03  0.6945  2.192
 ## sigma       2.276   4.899 5.977e-04  1.2050  3.347
 ## 
-## Chi2 error levels in percent:
+## FOCUS Chi2 error levels in percent:
 ##          err.min n.optim df
 ## All data   6.205       3  3
 ## parent     6.205       3  3
@@ -393,9 +393,9 @@
 

 
summary(m.L2.DFOP, data = FALSE)

 
## mkin version used for fitting:    0.9.49.4 
-## R version used for fitting:       3.5.3 
-## Date of fit:     Wed Apr 10 10:11:37 2019 
-## Date of summary: Wed Apr 10 10:11:37 2019 
+## R version used for fitting:       3.6.0 
+## Date of fit:     Thu May  2 12:40:35 2019 
+## Date of summary: Thu May  2 12:40:35 2019 
 ## 
 ## Equations:
 ## d_parent/dt = - ((k1 * g * exp(-k1 * time) + k2 * (1 - g) *
@@ -404,10 +404,10 @@
 ## 
 ## Model predictions using solution type analytical 
 ## 
-## Fitted with method using 585 model solutions performed in 1.296 s
+## Fitted using 587 model solutions performed in 1.273 s
 ## 
 ## Error model:
-## NULL
+## Constant variance 
 ## 
 ## Starting values for parameters to be optimised:
 ##              value   type
@@ -431,18 +431,18 @@
 ## Optimised, transformed parameters with symmetric confidence intervals:
 ##          Estimate Std. Error      Lower     Upper
 ## parent_0  93.9500  9.998e-01    91.5900   96.3100
-## log_k1     3.1350  2.336e+03 -5522.0000 5528.0000
+## log_k1     3.1330  2.265e+03 -5354.0000 5360.0000
 ## log_k2    -1.0880  6.285e-02    -1.2370   -0.9394
 ## g_ilr     -0.2821  7.033e-02    -0.4484   -0.1158
 ## sigma      1.4140  2.886e-01     0.7314    2.0960
 ## 
 ## Parameter correlation:
 ##            parent_0     log_k1     log_k2      g_ilr      sigma
-## parent_0  1.000e+00  5.247e-07 -1.026e-10  2.665e-01 -8.076e-11
-## log_k1    5.247e-07  1.000e+00  8.592e-05 -1.690e-04 -7.938e-06
-## log_k2   -1.026e-10  8.592e-05  1.000e+00 -7.903e-01  5.048e-10
-## g_ilr     2.665e-01 -1.690e-04 -7.903e-01  1.000e+00 -6.476e-10
-## sigma    -8.076e-11 -7.938e-06  5.048e-10 -6.476e-10  1.000e+00
+## parent_0  1.000e+00  5.434e-07 -9.989e-11  2.665e-01 -3.978e-10
+## log_k1    5.434e-07  1.000e+00  8.888e-05 -1.748e-04 -8.207e-06
+## log_k2   -9.989e-11  8.888e-05  1.000e+00 -7.903e-01  5.751e-10
+## g_ilr     2.665e-01 -1.748e-04 -7.903e-01  1.000e+00 -7.109e-10
+## sigma    -3.978e-10 -8.207e-06  5.751e-10 -7.109e-10  1.000e+00
 ## 
 ## Backtransformed parameters:
 ## Confidence intervals for internally transformed parameters are asymmetric.
@@ -450,19 +450,19 @@
 ## for estimators of untransformed parameters.
 ##          Estimate   t value    Pr(>t)   Lower   Upper
 ## parent_0  93.9500 9.397e+01 2.036e-12 91.5900 96.3100
-## k1        23.0000 4.377e-04 4.998e-01  0.0000     Inf
+## k1        22.9300 4.514e-04 4.998e-01  0.0000     Inf
 ## k2         0.3369 1.591e+01 4.697e-07  0.2904  0.3909
 ## g          0.4016 1.680e+01 3.238e-07  0.3466  0.4591
 ## sigma      1.4140 4.899e+00 8.776e-04  0.7314  2.0960
 ## 
-## Chi2 error levels in percent:
+## FOCUS Chi2 error levels in percent:
 ##          err.min n.optim df
 ## All data    2.53       4  2
 ## parent      2.53       4  2
 ## 
 ## Estimated disappearance times:
 ##          DT50  DT90 DT50_k1 DT50_k2
-## parent 0.5335 5.311 0.03014   2.058

+## parent 0.5335 5.311 0.03023   2.058

 
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.

 
 
@@ -492,9 +492,9 @@
 
We can extract the summary and plot for e.g. the DFOP fit, using square brackets for indexing which will result in the use of the summary and plot functions working on mkinfit objects.

 
summary(mm.L3[["DFOP", 1]])

 
## mkin version used for fitting:    0.9.49.4 
-## R version used for fitting:       3.5.3 
-## Date of fit:     Wed Apr 10 10:11:39 2019 
-## Date of summary: Wed Apr 10 10:11:39 2019 
+## R version used for fitting:       3.6.0 
+## Date of fit:     Thu May  2 12:40:37 2019 
+## Date of summary: Thu May  2 12:40:37 2019 
 ## 
 ## Equations:
 ## d_parent/dt = - ((k1 * g * exp(-k1 * time) + k2 * (1 - g) *
@@ -503,10 +503,10 @@
 ## 
 ## Model predictions using solution type analytical 
 ## 
-## Fitted with method using 372 model solutions performed in 0.809 s
+## Fitted using 372 model solutions performed in 0.777 s
 ## 
 ## Error model:
-## NULL
+## Constant variance 
 ## 
 ## Starting values for parameters to be optimised:
 ##              value   type
@@ -537,11 +537,11 @@
 ## 
 ## Parameter correlation:
 ##           parent_0     log_k1     log_k2      g_ilr      sigma
-## parent_0 1.000e+00  1.732e-01  2.282e-02  4.009e-01  1.656e-07
-## log_k1   1.732e-01  1.000e+00  4.945e-01 -5.809e-01  6.759e-08
-## log_k2   2.282e-02  4.945e-01  1.000e+00 -6.812e-01  3.867e-07
-## g_ilr    4.009e-01 -5.809e-01 -6.812e-01  1.000e+00 -3.839e-07
-## sigma    1.656e-07  6.759e-08  3.867e-07 -3.839e-07  1.000e+00
+## parent_0 1.000e+00  1.732e-01  2.282e-02  4.009e-01  1.660e-07
+## log_k1   1.732e-01  1.000e+00  4.945e-01 -5.809e-01  6.635e-08
+## log_k2   2.282e-02  4.945e-01  1.000e+00 -6.812e-01  3.880e-07
+## g_ilr    4.009e-01 -5.809e-01 -6.812e-01  1.000e+00 -3.822e-07
+## sigma    1.660e-07  6.635e-08  3.880e-07 -3.822e-07  1.000e+00
 ## 
 ## Backtransformed parameters:
 ## Confidence intervals for internally transformed parameters are asymmetric.
@@ -554,7 +554,7 @@
 ## g         0.45660  34.920 2.581e-05  0.41540   0.49850
 ## sigma     1.01700   4.000 1.400e-02  0.20790   1.82700
 ## 
-## Chi2 error levels in percent:
+## FOCUS Chi2 error levels in percent:
 ##          err.min n.optim df
 ## All data   2.225       4  4
 ## parent     2.225       4  4
@@ -597,19 +597,19 @@
 
The \(\chi^2\) error level of 3.3% as well as the plot suggest that the SFO model fits very well. The error level at which the \(\chi^2\) test passes is slightly lower for the FOMC model. However, the difference appears negligible.

 
summary(mm.L4[["SFO", 1]], data = FALSE)

 
## mkin version used for fitting:    0.9.49.4 
-## R version used for fitting:       3.5.3 
-## Date of fit:     Wed Apr 10 10:11:39 2019 
-## Date of summary: Wed Apr 10 10:11:40 2019 
+## R version used for fitting:       3.6.0 
+## Date of fit:     Thu May  2 12:40:38 2019 
+## Date of summary: Thu May  2 12:40:38 2019 
 ## 
 ## Equations:
 ## d_parent/dt = - k_parent_sink * parent
 ## 
 ## Model predictions using solution type analytical 
 ## 
-## Fitted with method using 146 model solutions performed in 0.306 s
+## Fitted using 146 model solutions performed in 0.298 s
 ## 
 ## Error model:
-## NULL
+## Constant variance 
 ## 
 ## Starting values for parameters to be optimised:
 ##                  value   type
@@ -634,9 +634,9 @@
 ## 
 ## Parameter correlation:
 ##                    parent_0 log_k_parent_sink      sigma
-## parent_0          1.000e+00         5.938e-01  5.612e-10
-## log_k_parent_sink 5.938e-01         1.000e+00 -4.994e-10
-## sigma             5.612e-10        -4.994e-10  1.000e+00
+## parent_0          1.000e+00         5.938e-01  4.256e-10
+## log_k_parent_sink 5.938e-01         1.000e+00 -7.280e-10
+## sigma             4.256e-10        -7.280e-10  1.000e+00
 ## 
 ## Backtransformed parameters:
 ## Confidence intervals for internally transformed parameters are asymmetric.
@@ -647,7 +647,7 @@
 ## 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
 ## 
-## Chi2 error levels in percent:
+## FOCUS Chi2 error levels in percent:
 ##          err.min n.optim df
 ## All data   3.287       2  6
 ## parent     3.287       2  6
@@ -661,19 +661,19 @@
 ## parent  106  352

 
summary(mm.L4[["FOMC", 1]], data = FALSE)

 
## mkin version used for fitting:    0.9.49.4 
-## R version used for fitting:       3.5.3 
-## Date of fit:     Wed Apr 10 10:11:40 2019 
-## Date of summary: Wed Apr 10 10:11:40 2019 
+## R version used for fitting:       3.6.0 
+## Date of fit:     Thu May  2 12:40:38 2019 
+## Date of summary: Thu May  2 12:40:38 2019 
 ## 
 ## Equations:
 ## d_parent/dt = - (alpha/beta) * 1/((time/beta) + 1) * parent
 ## 
 ## Model predictions using solution type analytical 
 ## 
-## Fitted with method using 224 model solutions performed in 0.478 s
+## Fitted using 224 model solutions performed in 0.458 s
 ## 
 ## Error model:
-## NULL
+## Constant variance 
 ## 
 ## Starting values for parameters to be optimised:
 ##              value   type
@@ -701,10 +701,10 @@
 ## 
 ## Parameter correlation:
 ##             parent_0  log_alpha   log_beta      sigma
-## parent_0   1.000e+00 -4.696e-01 -5.543e-01 -2.460e-07
-## log_alpha -4.696e-01  1.000e+00  9.889e-01  2.351e-08
-## log_beta  -5.543e-01  9.889e-01  1.000e+00  5.079e-08
-## sigma     -2.460e-07  2.351e-08  5.079e-08  1.000e+00
+## parent_0   1.000e+00 -4.696e-01 -5.543e-01 -2.473e-07
+## log_alpha -4.696e-01  1.000e+00  9.889e-01  2.429e-08
+## log_beta  -5.543e-01  9.889e-01  1.000e+00  5.183e-08
+## sigma     -2.473e-07  2.429e-08  5.183e-08  1.000e+00
 ## 
 ## Backtransformed parameters:
 ## Confidence intervals for internally transformed parameters are asymmetric.
@@ -716,7 +716,7 @@
 ## beta      64.9800   2.540 3.201e-02 21.7800 193.900
 ## sigma      1.8300   4.000 8.065e-03  0.5598   3.100
 ## 
-## Chi2 error levels in percent:
+## FOCUS Chi2 error levels in percent:
 ##          err.min n.optim df
 ## All data   2.029       3  5
 ## parent     2.029       3  5
-- 
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