This function returns degradation model parameters as well as error model parameters per default, 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, errparms = TRUE, ...)
# S3 method for mmkin
parms(object, transformed = FALSE, errparms = TRUE, ...)
# S3 method for multistart
parms(object, exclude_failed = TRUE, ...)A fitted model object.
Not used
Should the parameters be returned as used internally during the optimisation?
Should the error model parameters be returned in addition to the degradation parameters?
For multistart objects, should rows for failed fits be removed from the returned parameter matrix?
Depending on the object, a numeric vector of fitted model parameters, a matrix (e.g. for mmkin row objects), or a list of matrices (e.g. for mmkin objects with more than one row).
# 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
#>
# }