sample_parameters_uniformly function

sample_parameters_uniformly

Sample parameter vectors using uniform sampling

Each parameter value will be randomly sampled from a uniform distribution with the bounds being estimate ± estimate * fraction.

sample_parameters_uniformly(
  model,
  parameter_estimates,
  fraction = 0.1,
  force_posdef_samples = NULL,
  n = 1,
  seed = NULL,
  scale = "normal"
)

Arguments

• model: (Model) Pharmpy model
• parameter_estimates: (array) Parameter estimates for parameters to use
• fraction: (numeric) Fraction of estimate value to use for distribution bounds
• force_posdef_samples: (numeric (optional)) Number of samples to reject before forcing variability parameters to give positive definite covariance matrices.
• n: (numeric) Number of samples
• seed: (numeric (optional)) Random number generator or seed
• scale: (str) Scale to perform sampling on. Valid options are 'normal' and 'UCP'

Returns

(data.frame) samples

Examples

## Not run:

model <- load_example_model("pheno")
results <- load_example_modelfit_results("pheno")
rng <- create_rng(23)
pe <- results$parameter_estimates
sample_parameters_uniformly(model, pe, n=3, seed=rng)
## End(Not run)

See Also

sample_parameters_from_covariance_matrix : Sample parameter vectors using the

uncertainty covariance matrix

sample_individual_estimates : Sample individual estiates given their covariance