Summary method for class "saem.mmkin"

Lists model equations, initial parameter values, optimised parameters for fixed effects (population), random effects (deviations from the population mean) and residual error model, as well as the resulting endpoints such as formation fractions and DT50 values. Optionally (default is FALSE), the data are listed in full.

# S3 method for saem.mmkin
summary(object, data = FALSE, verbose = FALSE, distimes = TRUE, ...)

# S3 method for summary.saem.mmkin
print(x, digits = max(3, getOption("digits") - 3), verbose = x$verbose, ...)

Arguments

object	an object of class saem.mmkin
data	logical, indicating whether the full data should be included in the summary.
verbose	Should the summary be verbose?
distimes	logical, indicating whether DT50 and DT90 values should be included.
...	optional arguments passed to methods like print.
x	an object of class summary.saem.mmkin
digits	Number of digits to use for printing

Value

The summary function returns a list based on the saemix::SaemixObject obtained in the fit, with at least the following additional components

saemixversion, mkinversion, Rversion

The saemix, mkin and R versions used

date.fit, date.summary

The dates where the fit and the summary were produced

diffs

The differential equations used in the degradation model

use_of_ff

Was maximum or minimum use made of formation fractions

data

The data

confint_trans

Transformed parameters as used in the optimisation, with confidence intervals

confint_back

Backtransformed parameters, with confidence intervals if available

confint_errmod

Error model parameters with confidence intervals

ff

The estimated formation fractions derived from the fitted model.

distimes

The DT50 and DT90 values for each observed variable.

SFORB

If applicable, eigenvalues of SFORB components of the model.

The print method is called for its side effect, i.e. printing the summary.

Author

Johannes Ranke for the mkin specific parts saemix authors for the parts inherited from saemix.

Examples

# Generate five datasets following DFOP-SFO kinetics
sampling_times = c(0, 1, 3, 7, 14, 28, 60, 90, 120)
dfop_sfo <- mkinmod(parent = mkinsub("DFOP", "m1"),
 m1 = mkinsub("SFO"), quiet = TRUE)
set.seed(1234)
k1_in <- rlnorm(5, log(0.1), 0.3)
k2_in <- rlnorm(5, log(0.02), 0.3)
g_in <- plogis(rnorm(5, qlogis(0.5), 0.3))
f_parent_to_m1_in <- plogis(rnorm(5, qlogis(0.3), 0.3))
k_m1_in <- rlnorm(5, log(0.02), 0.3)

pred_dfop_sfo <- function(k1, k2, g, f_parent_to_m1, k_m1) {
  mkinpredict(dfop_sfo,
    c(k1 = k1, k2 = k2, g = g, f_parent_to_m1 = f_parent_to_m1, k_m1 = k_m1),
    c(parent = 100, m1 = 0),
    sampling_times)
}

ds_mean_dfop_sfo <- lapply(1:5, function(i) {
  mkinpredict(dfop_sfo,
    c(k1 = k1_in[i], k2 = k2_in[i], g = g_in[i],
      f_parent_to_m1 = f_parent_to_m1_in[i], k_m1 = k_m1_in[i]),
    c(parent = 100, m1 = 0),
    sampling_times)
})
names(ds_mean_dfop_sfo) <- paste("ds", 1:5)

ds_syn_dfop_sfo <- lapply(ds_mean_dfop_sfo, function(ds) {
  add_err(ds,
    sdfunc = function(value) sqrt(1^2 + value^2 * 0.07^2),
    n = 1)[[1]]
})

# \dontrun{
# Evaluate using mmkin and saem
f_mmkin_dfop_sfo <- mmkin(list(dfop_sfo), ds_syn_dfop_sfo,
  quiet = TRUE, error_model = "tc", cores = 5)
f_saem_dfop_sfo <- saem(f_mmkin_dfop_sfo)
#> Warning: argument is not a function
#>  
#> Error in rxModelVars_(obj): Not compatible with STRSXP: [type=NULL].
summary(f_saem_dfop_sfo, data = TRUE)
#> Error in h(simpleError(msg, call)): error in evaluating the argument 'object' in selecting a method for function 'summary': object 'f_saem_dfop_sfo' not found
# }