R/transform_odeparms.R
transform_odeparms.RdThe transformations are intended to map parameters that should only take on
restricted values to the full scale of real numbers. For kinetic rate
constants and other paramters that can only take on positive values, a
simple log transformation is used. For compositional parameters, such as the
formations fractions that should always sum up to 1 and can not be negative,
the ilr transformation is used.
transform_odeparms( parms, mkinmod, transform_rates = TRUE, transform_fractions = TRUE ) backtransform_odeparms( transparms, mkinmod, transform_rates = TRUE, transform_fractions = TRUE )
| parms | Parameters of kinetic models as used in the differential equations. |
|---|---|
| mkinmod | The kinetic model of class |
| transform_rates | Boolean specifying if kinetic rate constants should be transformed in the model specification used in the fitting for better compliance with the assumption of normal distribution of the estimator. If TRUE, also alpha and beta parameters of the FOMC model are log-transformed, as well as k1 and k2 rate constants for the DFOP and HS models and the break point tb of the HS model. |
| transform_fractions | Boolean specifying if formation fractions
constants should be transformed in the model specification used in the
fitting for better compliance with the assumption of normal distribution
of the estimator. The default (TRUE) is to do transformations. The g
parameter of the DFOP and HS models are also transformed, as they can also
be seen as compositional data. The transformation used for these
transformations is the |
| transparms | Transformed parameters of kinetic models as used in the fitting procedure. |
A vector of transformed or backtransformed parameters with the same names as the original parameters.
The transformation of sets of formation fractions is fragile, as it supposes
the same ordering of the components in forward and backward transformation.
This is no problem for the internal use in mkinfit.
backtransform_odeparms: Backtransform the set of transformed parameters
#># Fit the model to the FOCUS example dataset D using defaults fit <- mkinfit(SFO_SFO, FOCUS_2006_D, quiet = TRUE)#> Warning: Observations with value of zero were removed from the data#> Estimate Std. Error Lower Upper #> parent_0 99.598 1.5702 96.4038 102.793 #> log_k_parent -2.316 0.0409 -2.3988 -2.233 #> log_k_m1 -5.248 0.1332 -5.5184 -4.977 #> f_parent_ilr_1 0.041 0.0631 -0.0875 0.169 #> sigma 3.126 0.3585 2.3961 3.855#> Estimate se_notrans t value Pr(>t) Lower Upper #> parent_0 99.59848 1.57022 63.43 2.30e-36 96.40384 102.7931 #> k_parent 0.09870 0.00403 24.47 4.96e-23 0.09082 0.1073 #> k_m1 0.00526 0.00070 7.51 6.16e-09 0.00401 0.0069 #> f_parent_to_m1 0.51448 0.02230 23.07 3.10e-22 0.46912 0.5596 #> sigma 3.12550 0.35852 8.72 2.24e-10 2.39609 3.8549# \dontrun{ # Compare to the version without transforming rate parameters fit.2 <- mkinfit(SFO_SFO, FOCUS_2006_D, transform_rates = FALSE, quiet = TRUE)#> Warning: Observations with value of zero were removed from the data#> Error in if (cost < cost.current) { assign("cost.current", cost, inherits = TRUE) if (!quiet) cat(ifelse(OLS, "Sum of squared residuals", "Negative log-likelihood"), " at call ", calls, ": ", cost.current, "\n", sep = "")}: Fehlender Wert, wo TRUE/FALSE nötig ist#>#> Error in summary(fit.2): Objekt 'fit.2' nicht gefunden#> Error in print(fit.2.s$par, 3): Objekt 'fit.2.s' nicht gefunden#> Error in print(fit.2.s$bpar, 3): Objekt 'fit.2.s' nicht gefunden# } initials <- fit$start$value names(initials) <- rownames(fit$start) transformed <- fit$start_transformed$value names(transformed) <- rownames(fit$start_transformed) transform_odeparms(initials, SFO_SFO)#> parent_0 log_k_parent log_k_m1 f_parent_ilr_1 #> 100.750000 -2.302585 -2.301586 0.000000backtransform_odeparms(transformed, SFO_SFO)#> parent_0 k_parent k_m1 f_parent_to_m1 #> 100.7500 0.1000 0.1001 0.5000# \dontrun{ # The case of formation fractions SFO_SFO.ff <- mkinmod( parent = list(type = "SFO", to = "m1", sink = TRUE), m1 = list(type = "SFO"), use_of_ff = "max")#>#> Warning: Observations with value of zero were removed from the data#> Estimate Std. Error Lower Upper #> parent_0 99.598 1.5702 96.4038 102.793 #> log_k_parent -2.316 0.0409 -2.3988 -2.233 #> log_k_m1 -5.248 0.1332 -5.5184 -4.977 #> f_parent_ilr_1 0.041 0.0631 -0.0875 0.169 #> sigma 3.126 0.3585 2.3961 3.855#> Estimate se_notrans t value Pr(>t) Lower Upper #> parent_0 99.59848 1.57022 63.43 2.30e-36 96.40384 102.7931 #> k_parent 0.09870 0.00403 24.47 4.96e-23 0.09082 0.1073 #> k_m1 0.00526 0.00070 7.51 6.16e-09 0.00401 0.0069 #> f_parent_to_m1 0.51448 0.02230 23.07 3.10e-22 0.46912 0.5596 #> sigma 3.12550 0.35852 8.72 2.24e-10 2.39609 3.8549initials <- c("f_parent_to_m1" = 0.5) transformed <- transform_odeparms(initials, SFO_SFO.ff) backtransform_odeparms(transformed, SFO_SFO.ff)#> f_parent_to_m1 #> 0.5# And without sink SFO_SFO.ff.2 <- mkinmod( parent = list(type = "SFO", to = "m1", sink = FALSE), m1 = list(type = "SFO"), use_of_ff = "max")#>#> Warning: Observations with value of zero were removed from the data#> Estimate Std. Error Lower Upper #> parent_0 84.79 3.012 78.67 90.91 #> log_k_parent -2.76 0.082 -2.92 -2.59 #> log_k_m1 -4.21 0.123 -4.46 -3.96 #> sigma 8.22 0.943 6.31 10.14#> Estimate se_notrans t value Pr(>t) Lower Upper #> parent_0 84.7916 3.01203 28.15 1.92e-25 78.6704 90.913 #> k_parent 0.0635 0.00521 12.19 2.91e-14 0.0538 0.075 #> k_m1 0.0148 0.00182 8.13 8.81e-10 0.0115 0.019 #> sigma 8.2229 0.94323 8.72 1.73e-10 6.3060 10.140# }