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.
Usage
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, ...)
# S3 method for saem.mmkin
parms(object, ci = FALSE, covariates = NULL, ...)Arguments
- object
A fitted model object.
- ...
Not used
- transformed
Should the parameters be returned as used internally during the optimisation?
- errparms
Should the error model parameters be returned in addition to the degradation parameters?
- exclude_failed
For multistart objects, should rows for failed fits be removed from the returned parameter matrix?
- ci
Should a matrix with estimates and confidence interval boundaries be returned? If FALSE (default), a vector of estimates is returned if no covariates are given, otherwise a matrix of estimates is returned, with each column corresponding to a row of the data frame holding the covariates
- covariates
A data frame holding covariate values for which to return parameter values. Only has an effect if 'ci' is FALSE.
Value
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).
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 91.058971584
#> k1 0.044946770
#> k2 0.002868336
#> g 0.526942414
#> 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 94.8481458
#> 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 91.058971584 90.34509493 98.14858820 94.311323735
#> 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 0.526942414 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 82.148095
#> 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 98.38393898 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
#>
# }