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,
covariates = NULL,
covariate_quantile = 0.5,
distimes = TRUE,
...
)# S3 method for summary.saem.mmkin
print(x, digits = max(3, getOption("digits") - 3), verbose = x$verbose, ...)
The summary function returns a list based on the saemix::SaemixObject
obtained in the fit, with at least the following additional components
The saemix, mkin and R versions used
The dates where the fit and the summary were produced
The differential equations used in the degradation model
Was maximum or minimum use made of formation fractions
The data
Transformed parameters as used in the optimisation, with confidence intervals
Backtransformed parameters, with confidence intervals if available
Error model parameters with confidence intervals
The estimated formation fractions derived from the fitted model.
The DT50 and DT90 values for each observed variable.
If applicable, eigenvalues of SFORB components of the model.
The print method is called for its side effect, i.e. printing the summary.
an object of class saem.mmkin
logical, indicating whether the full data should be included in the summary.
Should the summary be verbose?
Numeric vector with covariate values for all variables in any covariate models in the object. If given, it overrides '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).
logical, indicating whether DT50 and DT90 values should be included.
optional arguments passed to methods like print.
an object of class summary.saem.mmkin
Number of digits to use for printing
Johannes Ranke for the mkin specific parts saemix authors for the parts inherited from saemix.
# 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]]
})
if (FALSE) {
# 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@model@covariance.model
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)
}
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