This function always returns degradation model parameters as well as error 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, ...) # S3 method for mmkin parms(object, transformed = FALSE, ...)
| object | A fitted model object. Methods are implemented for
|
|---|---|
| ... | Not used |
| transformed | Should the parameters be returned as used internally during the optimisation? |
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.
#> parent_0 k_parent_sink sigma #> 82.4921598 0.3060633 4.6730124parms(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 6.50457673parms(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 #># }