## ---- include = FALSE----------------------------------------------------
require(knitr)
opts_chunk$set(engine='R', tidy=FALSE)

## ---- echo = TRUE, cache = TRUE, fig = TRUE, fig.width = 8, fig.height = 7----
library(mkin)
# Define the kinetic model
m_SFO_SFO_SFO <- mkinmod(parent = mkinsub("SFO", "M1"),
                         M1 = mkinsub("SFO", "M2"),
                         M2 = mkinsub("SFO"),
                         use_of_ff = "max", quiet = TRUE)


# Produce model predictions using some arbitrary parameters
sampling_times = c(0, 1, 3, 7, 14, 28, 60, 90, 120)
d_SFO_SFO_SFO <- mkinpredict(m_SFO_SFO_SFO,
  c(k_parent = 0.03,
    f_parent_to_M1 = 0.5, k_M1 = log(2)/100,
    f_M1_to_M2 = 0.9, k_M2 = log(2)/50),
  c(parent = 100, M1 = 0, M2 = 0),
  sampling_times)

# Generate a dataset by adding normally distributed errors with
# standard deviation 3, for two replicates at each sampling time
d_SFO_SFO_SFO_err <- add_err(d_SFO_SFO_SFO, reps = 2,
                             sdfunc = function(x) 3,
                             n = 1, seed = 123456789 )

# Fit the model to the dataset
f_SFO_SFO_SFO <- mkinfit(m_SFO_SFO_SFO, d_SFO_SFO_SFO_err[[1]], quiet = TRUE)

# Plot the results separately for parent and metabolites
plot_sep(f_SFO_SFO_SFO, lpos = c("topright", "bottomright", "bottomright"))