Skip to contents

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).

See also

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
#> 
# }