Permutation mars wrapper — permutation

This is a wrapper around mars() to perform one permutation replicate. Single-level structures permute rows; multilevel structures permute highest-level cluster blocks.

permutation_mars(
  data,
  studyID,
  effectID,
  sample_size,
  effectsize_type = NULL,
  formula = NULL,
  variable_names = NULL,
  effectsize_name = NULL,
  estimation_method = "REML",
  variance = NULL,
  varcov_type,
  weights = NULL,
  structure = "UN",
  intercept = FALSE,
  missing = "remove",
  optim_method = "L-BFGS-B",
  robustID = NULL,
  multivariate_covs = NULL,
  lasso = FALSE,
  lasso_args = list(lambda_grid = 10^seq(-3, 1, length.out = 10), K = 5,
    all_lasso_metrics = FALSE),
  tol = 1e-10,
  ...
)

Arguments

data: Data used for analysis
studyID: Character string representing the study ID
effectID: Character string representing the effect size ID
sample_size: Character string representing the sample size of the studies.
effectsize_type: Type of effect size being analyzed
formula: The formula used for specifying the fixed and random structure.
variable_names: Vector of character strings representing the attributes with correlations. The attributes that are correlated should be separated by an underscore.
effectsize_name: Character string representing the name of the effect size column in the data.
estimation_method: Type of estimation used, either "REML" or "MLE", REML is the default
variance: Character string representing the name of the variance of the effect size in the data.
varcov_type: Type of variance covariance matrix computed.
weights: User specified matrix of weights for analysis.
structure: Between studies covariance structure.
intercept: Whether a model intercept should be specified.
missing: Whether missing data should be removed, or kept.
optim_method: Optimization method that is passed to the optim function.
robustID: A character vector specifying the cluster group to use for computing the robust standard errors.
multivariate_covs: A one-sided formula to specify the covariates used in a multivariate analysis.
lasso: TRUE/FALSE indicator that specifies if LASSO results are returned.
lasso_args: A list of LASSO specific arguments.
tol: Tolerance of the optimization.
...: Not currently used.