Adds covariate effect to :class:pharmpy.model
.
The following effects have templates:
Linear function for continuous covariates (lin)
Function:
(equation could not be rendered, see API doc on website)
Init: 0.001
Upper:
If median of covariate equals minimum: 100,000
Otherwise: (equation could not be rendered, see API doc on website)
Lower:
If median of covariate equals maximum: -100,000
Otherwise: (equation could not be rendered, see API doc on website)
Linear function for categorical covariates (cat)
Function:
If covariate is the most common category:
(equation could not be rendered, see API doc on website)
For each additional category:
(equation could not be rendered, see API doc on website)
Init: 0.001
Upper: 5
Lower: -1
(alternative) Linear function for categorical covariates (cat2)
Function:
If covariate is the most common category:
(equation could not be rendered, see API doc on website)
For each additional category:
(equation could not be rendered, see API doc on website)
Init: 0.001
Upper: 6
Lower: 0
Piecewise linear function/"hockey-stick", continuous covariates only (piece_lin)
Function:
If cov <= median:
(equation could not be rendered, see API doc on website)
If cov > median:
(equation could not be rendered, see API doc on website)
Init: 0.001
Upper:
For first state: (equation could not be rendered, see API doc on website)
Otherwise: 100,000
Lower:
For first state: -100,000
Otherwise: (equation could not be rendered, see API doc on website)
Exponential function, continuous covariates only (exp)
Function:
(equation could not be rendered, see API doc on website)
Init:
If lower > 0.001 or upper < 0.001: (equation could not be rendered, see API doc on website)
If estimated init is 0: (equation could not be rendered, see API doc on website)
Otherwise: 0.001
Upper:
If min - median = 0 or max - median = 0: 100
Otherwise:
(equation could not be rendered, see API doc on website)
Lower:
If min - median = 0 or max - median = 0: 0.01
Otherwise:
(equation could not be rendered, see API doc on website)
Power function, continuous covariates only (pow)
Function:
(equation could not be rendered, see API doc on website)
Init: 0.001
Upper: 100,000
Lower: -100
add_covariate_effect(
model,
parameter,
covariate,
effect,
operation = "*",
allow_nested = FALSE
)
(Model) Pharmpy model object
(Model) Pharmpy model to add covariate effect to.
(str) Name of parameter to add covariate effect to.
(str) Name of covariate.
(str) Type of covariate effect. May be abbreviated covariate effect (see above) or custom.
(str) Whether the covariate effect should be added or multiplied (default).
(logical) Whether to allow adding a covariate effect when one already exists for the input parameter-covariate pair.
if (FALSE) {
model <- load_example_model("pheno")
model <- add_covariate_effect(model, "CL", "APGR", "exp")
model$statements$before_odes$full_expression("CL")
}
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