sample_parameters_from_covariance_matrix function

sample_parameters_from_covariance_matrix

Sample parameter vectors using the covariance matrix

If parameters is not provided all estimated parameters will be used

sample_parameters_from_covariance_matrix(
  model,
  parameter_estimates,
  covariance_matrix,
  force_posdef_samples = NULL,
  force_posdef_covmatrix = FALSE,
  n = 1,
  seed = NULL
)

Arguments

• model: (Model) Input model
• parameter_estimates: (array) Parameter estimates to use as means in sampling
• covariance_matrix: (data.frame) Parameter uncertainty covariance matrix
• force_posdef_samples: (numeric (optional)) Set to how many iterations to do before forcing all samples to be positive definite. NULL is default and means never and 0 means always
• force_posdef_covmatrix: (logical) Set to TRUE to force the input covariance matrix to be positive definite
• n: (numeric) Number of samples
• seed: (numeric (optional)) Random number generator

Returns

(data.frame) A dataframe with one sample per row

Examples

## Not run:

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

See Also

sample_parameters_uniformly : Sample parameter vectors using uniform distribution

sample_individual_estimates : Sample individual estiates given their covariance