summary.saem.mmkin: Summary method for class "saem.mmkin"

In mkin: Kinetic Evaluation of Chemical Degradation Data

Summary method for class "saem.mmkin"

Description

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.

Usage

## S3 method for class 'saem.mmkin'
summary(
  object,
  data = FALSE,
  verbose = FALSE,
  covariates = NULL,
  covariate_quantile = 0.5,
  distimes = TRUE,
  ...
)

## S3 method for class '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?
covariates	Numeric vector with covariate values for all variables in any covariate models in the object. If given, it overrides 'covariate_quantile'.
covariate_quantile	This argument only has an effect if the fitted object has covariate models. If so, the default is to show endpoints for the median of the covariate values (50th percentile).
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(s)

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]]
})

## Not run: 
# 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)
print(f_saem_dfop_sfo)
illparms(f_saem_dfop_sfo)
f_saem_dfop_sfo_2 <- update(f_saem_dfop_sfo,
  no_random_effect = c("parent_0", "log_k_m1"))
illparms(f_saem_dfop_sfo_2)
intervals(f_saem_dfop_sfo_2)
summary(f_saem_dfop_sfo_2, data = TRUE)
# Add a correlation between random effects of g and k2
cov_model_3 <- f_saem_dfop_sfo_2$so@[e-mail]
cov_model_3["log_k2", "g_qlogis"] <- 1
cov_model_3["g_qlogis", "log_k2"] <- 1
f_saem_dfop_sfo_3 <- update(f_saem_dfop_sfo,
  covariance.model = cov_model_3)
intervals(f_saem_dfop_sfo_3)
# The correlation does not improve the fit judged by AIC and BIC, although
# the likelihood is higher with the additional parameter
anova(f_saem_dfop_sfo, f_saem_dfop_sfo_2, f_saem_dfop_sfo_3)

## End(Not run)

mkin documentation built on Oct. 14, 2023, 5:08 p.m.