Output from sim() is in the form of a
data.frame. The output is in the long format, and split by
compartment. This makes the output ready for plotting e.g. with
ggplot2.
By default, sim() will return data for all compartments.
If you are only interested in the observation data (i.e. the
concentrations in the case of PK), you can select only those by
specifying the only_obs = TRUE option. Please note that the
comp column in the dataset will have the indices for all
the compartments, as well as an extra set of rows for the
"obs" (observation) data, which is scaled by the scaling
factor specified in the model.
The output for the observation, as well as the compartment from which
the observation is taken can be set using the obs
argument:
mod <- new_ode_model(code = "
dAdt[1] = -KA * A[1];
dAdt[2] = -(CL/V) * A[2] + KA*A[1];
", obs = list(cmt = 2, scale = "V"))Multiple observation types
If you would like to output more than one observation type, such as
the concentration of the parent drug and the concentration of the
metabolite, you can control that using the obs argument.
Below is an example where the amount in the absorption compartment as
well as the systemic drug concentration are outputted.
mod <- new_ode_model(code = "
dAdt[1] = -KA * A[1];
dAdt[2] = -(CL/V) * A[2] + KA*A[1];
",
obs = list(
cmt = c(2, 2),
scale = c(1, "V"),
label = c("abs", "conc")
)
)
par <- list(CL = 5, V = 50, KA = .5)
reg <- new_regimen(amt = 100, n = 5, interval = 12)
res <- sim(
ode = mod,
parameters = par,
regimen = reg,
only_obs = TRUE
)Parameters, variables, and covariates
It is often useful to include model parameters, generated variables,
and/or covariates in the output table as well, especially if covariates
and between-subject variability is included in the simulation, or for
debugging models. You can use the output_include argument
for this:
mod_1cmt_iv <- new_ode_model("pk_1cmt_iv")
p <- list(CL = 5, V = 50)
reg <- new_regimen(amt = 100, n = 4, interval = 12, type = "bolus", cmt = 1)
cov_table <- data.frame(
id = c(1, 1, 2, 3),
WT = c(40, 45, 50, 60),
SCR = c(50, 150, 90, 110),
t = c(0, 5, 0, 0)
)
dat <- sim(
ode = mod_1cmt_iv,
parameters = p,
regimen = reg,
covariates_table = cov_table,
covariates_implementation = list(SCR = "interpolate"),
n_ind = 3,
only_obs = TRUE,
output_include = list(parameters = TRUE, covariates = TRUE)
)## Simulating 3 individuals.
head(dat)## id t comp y obs_type CL V WT SCR
## 1 1 0 obs 2.000000 1 5 50 40 50
## 4 1 1 obs 1.809675 1 5 50 41 70
## 40 1 2 obs 1.637462 1 5 50 42 90
## 76 1 3 obs 1.481636 1 5 50 43 110
## 112 1 4 obs 1.340640 1 5 50 44 130
## 142 1 5 obs 1.213061 1 5 50 45 150