Measurement error of an output. With add and prop:
sd = sqrt(add^2 + (prop * f)^2), where f is the model prediction (the
combined2 form of nlmixr2); use add alone for additive error and prop
alone for proportional error. With exp: exponential error, that is
additive error of standard deviation exp on the log scale (data fitted as
log(y)). Exponential error is not combined with the others.
Usage
residual_error(add = 0, prop = 0, exp = 0)
Arguments
- add
Additive standard deviation (in the units of the output).
- prop
Proportional standard deviation (for example 0.1 for 10%).
- exp
Standard deviation on the log scale (exponential error).
Value
An object of class pkident_residual.
Details
Proportional and exponential errors differ in the Fisher information:
with proportional error the variance depends on the parameters and carries
information of its own; with exponential error it does not.
Examples
residual_error(prop = 0.1)
#> <pkident residual error> add = 0, prop = 0.1
residual_error(add = 0.05, prop = 0.1)
#> <pkident residual error> add = 0.05, prop = 0.1
residual_error(exp = 0.2)
#> <pkident residual error> exponential (additive on the log scale), sd = 0.2