Method to get the names of ill-defined parameters

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The method for generalised nonlinear regression fits as obtained -with mkinfit and mmkin checks if the degradation parameters -pass the Wald test (in degradation kinetics often simply called t-test) for -significant difference from zero. For this test, the parameterisation -without parameter transformations is used.

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illparms(object, ...)
-
-# S3 method for mkinfit
-illparms(object, conf.level = 0.95, ...)
-
-# S3 method for illparms.mkinfit
-print(x, ...)
-
-# S3 method for mmkin
-illparms(object, conf.level = 0.95, ...)
-
-# S3 method for illparms.mmkin
-print(x, ...)
-
-# S3 method for saem.mmkin
-illparms(
-  object,
-  conf.level = 0.95,
-  random = TRUE,
-  errmod = TRUE,
-  slopes = TRUE,
-  ...
-)
-
-# S3 method for illparms.saem.mmkin
-print(x, ...)
-
-# S3 method for mhmkin
-illparms(object, conf.level = 0.95, random = TRUE, errmod = TRUE, ...)
-
-# S3 method for illparms.mhmkin
-print(x, ...)

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Arguments

object: The object to investigate
...: For potential future extensions
conf.level: The confidence level for checking p values
x: The object to be printed
random: For hierarchical fits, should random effects be tested?
errmod: For hierarchical fits, should error model parameters be -tested?
slopes: For hierarchical saem fits using saemix as backend, -should slope parameters in the covariate model(starting with 'beta_') be -tested?

Value

- - -

For mkinfit or saem objects, a character vector of parameter -names. For mmkin or mhmkin objects, a matrix like object of class -'illparms.mmkin' or 'illparms.mhmkin'.

Details

The method for hierarchical model fits, also known as nonlinear -mixed-effects model fits as obtained with saem and mhmkin -checks if any of the confidence intervals for the random -effects expressed as standard deviations include zero, and if -the confidence intervals for the error model parameters include -zero.

Note

All return objects have printing methods. For the single fits, printing -does not output anything in the case no ill-defined parameters are found.

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Examples

fit <- mkinfit("FOMC", FOCUS_2006_A, quiet = TRUE)
-illparms(fit)
-#> [1] "parent_0" "alpha"    "beta"     "sigma"   
-# \dontrun{
-fits <- mmkin(
-  c("SFO", "FOMC"),
-  list("FOCUS A" = FOCUS_2006_A,
-       "FOCUS C" = FOCUS_2006_C),
-  quiet = TRUE)
-illparms(fits)
-#>       dataset
-#> model  FOCUS A                      FOCUS C
-#>   SFO                                      
-#>   FOMC parent_0, alpha, beta, sigma        
-# }
-

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