Extract model parameters from mkinfit models

- -

@@ -151,148 +90,147 @@ model parameters, in order to avoid working with a fitted model without considering the error structure that was assumed for the fit.

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

+# S3 method for mkinfit
+parms(object, transformed = FALSE, ...)
 
-    Arguments
-    
-    
-    
-      
-      
-    
-    
-      
-      
-    
-    
-      
-      
-    
-    object A fitted model object. Methods are implemented for
-mkinfit() objects and for mmkin() objects.
... Not used
transformed Should the parameters be returned
-as used internally during the optimisation?
-
-    Value
+# S3 method for mmkin
+parms(object, transformed = FALSE, ...)

object	A fitted model object. Methods are implemented for -`mkinfit()` objects and for `mmkin()` objects.
...	Not used
transformed	Should the parameters be returned -as used internally during the optimisation?

Arguments

object: A fitted model object. Methods are implemented for +mkinfit() objects and for mmkin() objects.
...: Not used
transformed: Should the parameters be returned +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.

Examples

# mkinfit objects
-fit <- mkinfit("SFO", FOCUS_2006_C, quiet = TRUE)
-parms(fit)
-
#>   parent_0   k_parent      sigma 
-#> 82.4921598  0.3060633  4.6730124 
parms(fit, transformed = TRUE)
-
#>     parent_0 log_k_parent        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  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  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 91.058971589
-#> 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  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 94.8481459
-#> 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 91.058971589 90.34509493 98.14858820 94.311323734
-#> k1        0.21954588  0.044946770  0.41232288  0.31697588  0.080663857
-#> k2        0.02957934  0.002868336  0.07581766  0.03260384  0.003425417
-#> g         0.44845068  0.526942415  0.66091967  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 -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 91.0589716 90.3450949 98.1485882 94.3113237
-#> log_k1   -1.5161940 -3.1022764 -0.8859486 -1.1489296 -2.5174647
-#> log_k2   -3.5206791 -5.8540232 -2.5794240 -3.4233253 -5.6765322
-#> g_qlogis -0.2069326  0.1078741  0.6673953  0.6332573 -0.6514943
-#> sigma     1.3569047  2.2213022  1.3416908  2.8715985  1.9420678
-#> 
# }
-

Examples

# mkinfit objects
+fit <- mkinfit("SFO", FOCUS_2006_C, quiet = TRUE)
+parms(fit)
+#>   parent_0   k_parent      sigma 
+#> 82.4921598  0.3060633  4.6730124 
+parms(fit, transformed = TRUE)
+#>     parent_0 log_k_parent        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  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  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 91.058971589
+#> 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  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 94.8481459
+#> 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 91.058971589 90.34509493 98.14858820 94.311323734
+#> k1        0.21954588  0.044946770  0.41232288  0.31697588  0.080663857
+#> k2        0.02957934  0.002868336  0.07581766  0.03260384  0.003425417
+#> g         0.44845068  0.526942415  0.66091967  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 -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 91.0589716 90.3450949 98.1485882 94.3113237
+#> log_k1   -1.5161940 -3.1022764 -0.8859486 -1.1489296 -2.5174647
+#> log_k2   -3.5206791 -5.8540232 -2.5794240 -3.4233253 -5.6765322
+#> g_qlogis -0.2069326  0.1078741  0.6673953  0.6332573 -0.6514943
+#> sigma     1.3569047  2.2213022  1.3416908  2.8715985  1.9420678
+#> 
+# }
+