From b5ee48a86e4b1d4c05aaadb80b44954e2e994ebc Mon Sep 17 00:00:00 2001 From: Johannes Ranke Date: Wed, 27 May 2020 07:12:51 +0200 Subject: Add docs generated using released version 0.9.52 --- docs/reference/parms.html | 105 ++++++++-------------------------------------- 1 file changed, 17 insertions(+), 88 deletions(-) (limited to 'docs/reference/parms.html') diff --git a/docs/reference/parms.html b/docs/reference/parms.html index 2fe91c26..f62b3898 100644 --- a/docs/reference/parms.html +++ b/docs/reference/parms.html @@ -74,7 +74,7 @@ considering the error structure that was assumed for the fit." /> mkin - 0.9.50.3 + 0.9.50.2 @@ -111,9 +111,6 @@ considering the error structure that was assumed for the fit." />
  • Example evaluation of NAFTA SOP Attachment examples
    • -
  • - Some benchmark timings -
  • @@ -154,9 +151,6 @@ considering the error structure that was assumed for the fit.

    parms(object, ...)
 
 # S3 method for mkinfit
-parms(object, transformed = FALSE, ...)
-
-# S3 method for mmkin
 parms(object, transformed = FALSE, ...)

    Arguments

    @@ -164,8 +158,7 @@ considering the error structure that was assumed for the fit.

    object -

    A fitted model object. Methods are implemented for -mkinfit() objects and for mmkin() objects.

    +

    A fitted model object

    ... @@ -180,88 +173,24 @@ as used internally during the optimisation?

    Value

    -

    For mkinfit objects, a numeric vector of fitted model parameters. -For mmkin row objects, a matrix with the parameters with a -row for each dataset. If the mmkin object has more than one row, a list of -such matrices is returned.

    +

    A numeric vector of fitted model parameters

    Examples

    - 
    # mkinfit objects
-fit <- mkinfit("SFO", FOCUS_2006_C, quiet = TRUE)
-parms(fit)
    #>      parent_0 k_parent_sink         sigma 
+    
    fit <- mkinfit("SFO", FOCUS_2006_C)
    #> Ordinary least squares optimisation
    #> Sum of squared residuals at call 1: 2388.077
+#> Sum of squared residuals at call 3: 2388.077
+#> Sum of squared residuals at call 4: 247.1962
+#> Sum of squared residuals at call 7: 200.6791
+#> Sum of squared residuals at call 10: 197.7231
+#> Sum of squared residuals at call 11: 197.0872
+#> Sum of squared residuals at call 14: 196.535
+#> Sum of squared residuals at call 15: 196.535
+#> Sum of squared residuals at call 16: 196.535
+#> Sum of squared residuals at call 17: 196.5334
+#> Sum of squared residuals at call 20: 196.5334
+#> Sum of squared residuals at call 25: 196.5334
+#> Negative log-likelihood at call 31: 26.64668
    #> Optimisation successfully terminated.
    parms(fit)
    #>      parent_0 k_parent_sink         sigma 
 #>    82.4921598     0.3060633     4.6730124 
    parms(fit, transformed = TRUE)
    #>          parent_0 log_k_parent_sink             sigma 
-#>         82.492160         -1.183963          4.673012 
    
-# mmkin objects
-ds <- lapply(experimental_data_for_UBA_2019[6:10],
- function(x) subset(x$data[c("name", "time", "value")]))
-names(ds) <- paste("Dataset", 6:10)
-# \dontrun{
-fits <- mmkin(c("SFO", "FOMC", "DFOP"), ds, quiet = TRUE, cores = 1)
-parms(fits["SFO", ])
    #>                 Dataset 6    Dataset 7  Dataset 8  Dataset 9  Dataset 10
-#> parent_0      88.52275400 82.666781678 86.8547308 91.7779306 82.14809450
-#> k_parent_sink  0.05794659  0.009647805  0.2102974  0.1232258  0.00720421
-#> sigma          5.15274487  7.040168584  3.6769645  6.4669234  6.50457673
    parms(fits[, 2])
    #> $SFO
-#>                  Dataset 7
-#> parent_0      82.666781678
-#> k_parent_sink  0.009647805
-#> sigma          7.040168584
-#> 
-#> $FOMC
-#>           Dataset 7
-#> parent_0 92.6837649
-#> alpha     0.4967832
-#> beta     14.1451255
-#> sigma     1.9167519
-#> 
-#> $DFOP
-#>             Dataset 7
-#> parent_0 91.058971503
-#> k1        0.044946770
-#> k2        0.002868336
-#> g         0.526942415
-#> sigma     2.221302196
-#> 
    parms(fits)
    #> $SFO
-#>                 Dataset 6    Dataset 7  Dataset 8  Dataset 9  Dataset 10
-#> parent_0      88.52275400 82.666781678 86.8547308 91.7779306 82.14809450
-#> k_parent_sink  0.05794659  0.009647805  0.2102974  0.1232258  0.00720421
-#> sigma          5.15274487  7.040168584  3.6769645  6.4669234  6.50457673
-#> 
-#> $FOMC
-#>          Dataset 6  Dataset 7 Dataset 8 Dataset 9 Dataset 10
-#> parent_0 95.558575 92.6837649 90.719787 98.383939 94.8481458
-#> alpha     1.338667  0.4967832  1.639099  1.074460  0.2805272
-#> beta     13.033315 14.1451255  5.007077  4.397126  6.9052224
-#> sigma     1.847671  1.9167519  1.066063  3.146056  1.6222778
-#> 
-#> $DFOP
-#>            Dataset 6    Dataset 7   Dataset 8   Dataset 9   Dataset 10
-#> parent_0 96.55213663 91.058971503 90.34509469 98.14858850 94.311323409
-#> k1        0.21954589  0.044946770  0.41232289  0.31697588  0.080663853
-#> k2        0.02957934  0.002868336  0.07581767  0.03260384  0.003425417
-#> g         0.44845068  0.526942415  0.66091965  0.65322767  0.342652880
-#> sigma     1.35690468  2.221302196  1.34169076  2.87159846  1.942067831
-#> 
    parms(fits, transformed = TRUE)
    #> $SFO
-#>                   Dataset 6 Dataset 7 Dataset 8 Dataset 9 Dataset 10
-#> parent_0          88.522754 82.666782 86.854731 91.777931  82.148094
-#> log_k_parent_sink -2.848234 -4.641025 -1.559232 -2.093737  -4.933090
-#> sigma              5.152745  7.040169  3.676964  6.466923   6.504577
-#> 
-#> $FOMC
-#>            Dataset 6  Dataset 7  Dataset 8   Dataset 9 Dataset 10
-#> parent_0  95.5585751 92.6837649 90.7197870 98.38393897  94.848146
-#> log_alpha  0.2916741 -0.6996015  0.4941466  0.07181816  -1.271085
-#> log_beta   2.5675088  2.6493701  1.6108523  1.48095106   1.932278
-#> sigma      1.8476712  1.9167519  1.0660627  3.14605557   1.622278
-#> 
-#> $DFOP
-#>           Dataset 6   Dataset 7  Dataset 8  Dataset 9 Dataset 10
-#> parent_0 96.5521366 91.05897150 90.3450947 98.1485885  94.311323
-#> log_k1   -1.5161940 -3.10227638 -0.8859485 -1.1489296  -2.517465
-#> log_k2   -3.5206791 -5.85402317 -2.5794240 -3.4233253  -5.676532
-#> g_ilr    -0.1463234  0.07627854  0.4719196  0.4477805  -0.460676
-#> sigma     1.3569047  2.22130220  1.3416908  2.8715985   1.942068
-#> 
    # }
-
    
+#>         82.492160         -1.183963          4.673012