Regression Model Tables

regression_tables(
  x,
  y = NULL,
  z = NULL,
  standardized = TRUE,
  unstandardized = TRUE,
  ci = 0.95,
  ci_method = NULL,
  bootstrap = FALSE,
  iterations = NULL,
  digits = 3,
  print = TRUE
)

Arguments

x	a model object
y	a model object
z	a model object
standardized	Logical, indicating whether or not to print standardized estimates. Standardized estimates are based on "refit" of the model on standardized data but it will not standardize categorical predictors. Defualt is TRUE.
unstandardized	Logical, indicating whether or not to print unstandardized estimates. Default is TRUE.
ci	Confidence Interval (CI) level. Default to 0.95 (95 ci_methodDocumention based on ?parameters::parameters. Method for computing degrees of freedom for confidence intervals (CI) and the related p-values. Allowed are following options (which vary depending on the model class): "residual", "normal", "likelihood", "satterthwaite", "kenward", "wald", "profile", "boot", "uniroot", "ml1", "betwithin", "hdi", "quantile", "ci", "eti", "si", "bci", or "bcai". See section Confidence intervals and approximation of degrees of freedom in model_parameters() for further details. When ci_method=NULL, in most cases "wald" is used then. bootstrapDocumention based on ?parameters::parameters. Should estimates be based on bootstrapped model? If TRUE, then arguments of Bayesian regressions apply (see also bootstrap_parameters()). iterationsDocumention based on ?parameters::parameters. The number of bootstrap replicates. This only apply in the case of bootstrapped frequentist models. digitsHow many decimal places to round to? Default is 3. printCreate a knitr table for displaying as html table? (default = TRUE)