PKPDsim can simulate residual errors in your observed data, which
can be done with the res_var
argument to the sim()
function.
This argument requires a list()
with one or more of the
following components:
prop
: proportional error: $$y = \hat{y} \cdot (1 + \mathcal{N}(0, prop))$$add
: additive error: $$y = \hat{y} + \mathcal{N}(0, add))$$exp
: exponential error: $$y = \hat{y} \cdot e^{\mathcal{N}(0, exp)}$$These list elements can be combined, e.g. for a combined proportional and additive
error model one would write e.g.: res_var = list(prop = 0.1, add = 1)
, which
would give a 10% proportional error plus an additive error of 1 concentration unit.
Below are some examples of the res_var
argument
library(PKPDsim) library(ggplot2)
Combined proportional and additive:
mod <- new_ode_model("pk_1cmt_iv") reg <- new_regimen( amt = 1000, n = 5, interval = 12, type = "bolus" ) sim1 <- sim( mod, parameters = list(CL = 5, V = 150), res_var = list(prop = 0.1, add = 1), regimen = reg, only_obs=TRUE ) ggplot(sim1, aes(x = t, y = y)) + geom_point()
Exponential:
sim2 <- sim( mod, parameters = list(CL = 5, V = 150), res_var = list(exp = 0.1), regimen = reg, only_obs=TRUE )
Besides including the residual error at simulation time, there is also the option to
include it afterwards. For that, the function add_ruv()
is useful.
sim3 <- sim1 sim3$y <- add_ruv( x = sim3$y, ruv = list( prop = 0.1, add = 1 ) ) ggplot(sim3, aes(x = t, y = y)) + geom_point()
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