Boostrap mars wrapper — bootstrap

This is a wrapper around the mars function to perform bootstrapping.

Usage

bootstrap_mars(
  num_bootstraps,
  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, lambda_tolerance = 0),
  tol = 1e-10,
  ...
)

Arguments

num_bootstraps: Number of bootstraps to perform
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. Used for univariate and multilevel structures.
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. Default is 'cor_weighted' for correlations or 'smd_outcome' for standardized mean differences.
weights: User specified matrix of weights for analysis.
structure: Between studies covariance structure, default is "UN" or unstructured. See details for more specifics.
intercept: Whether a model intercept should be specified, default is FALSE meaning no intercept. See details for more information.
missing: Whether missing data should be removed, or kept. Default is removing.
optim_method: Optimization method that is passed to the optim function. Default is 'L-BFGS-B'.
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. TRUE means LASSO results will be run, if number of predictors is less than number of effect sizes, both LASSO and non-LASSO results will be returned, if number of predictors is equal to or greater than the number of effect sizes, the LASSO results will only be returned. Numerical predictors are automatically standardized for optimization efficiency when doing LASSO.
lasso_args: A list of LASSO specific arguments. lambda_tolerance controls tie-breaking across lambda values by choosing the smaller lambda when CV metrics are within this tolerance.
tol: Tolerance of the optimization, default is 1E-10.
...: Not currently used.

Examples

if (FALSE) { # \dontrun{
boot_fit <- bootstrap_mars(
  num_bootstraps = 1,
  data = teacher_expectancy,
  studyID = "study",
  effectID = NULL,
  sample_size = NULL,
  formula = yi ~ 1,
  variance = "vi",
  varcov_type = "univariate",
  structure = "univariate"
)
} # }