summary.saem.mmkin {mkin}

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)