build_spec_coveff: Build specifications for covariate effect...

In BayesERtools: Bayesian Exposure-Response Analysis Tools

Build specifications for covariate effect simulation/visualization

Description

Build specifications for covariate effect simulation/visualization

Usage

build_spec_coveff(
  ermod,
  data = NULL,
  qi_width_cov = 0.9,
  n_sigfig = 3,
  use_seps = TRUE,
  drop_trailing_dec_mark = TRUE
)

Arguments

ermod	an object of class ermod
data	an optional data frame to derive the covariate values for forest plots. If NULL (default), the data used to fit the model is used.
qi_width_cov	the width of the quantile interval for continuous covariates in the forest plot. Default is 0.9 (i.e. visualize effect of covariate effect at their 5th and 95th percentile values).
n_sigfig	Number of significant figures to form value_label of continuous variables. See gt::vec_fmt_number() for details.
use_seps	Whether to use separators for thousands in printing numbers. See gt::vec_fmt_number() for details.
drop_trailing_dec_mark	Whether to drop the trailing decimal mark (".") in value_label of continuous variables. See gt::vec_fmt_number() for details.

Value

spec_coveff (return object) is a data frame for the specification of the covariate effects to be visualized. This is internally generated by build_spec_coveff() if you run sim_coveff() or plot_coveff() directly. Alternatively, you can develop your own or modify the one generated by build_spec_coveff() and supply it to sim_coveff() or plot_coveff(). The data frame should have the following columns (but it's probably easier to try build_spec_coveff() and see the structure):

• var_order: The order of the covariate in the forest plot. The exposure variable is always the first one and the covariates are ordered by the order they are supplied in the var_cov argument of the ⁠dev_ermod_*⁠ function. If you used a model from dev_ermod_bin_cov_sel(), then the order is determined by the variable selection process.

• var_name: The name of the variable.

• var_label: The label of the variable to be used for plot. This is the same as var_name by default.

• value_order: The order of the value of the variable to be evaluated.

• value_annot: The annotation of the value of the variable to be evaluated. This appears on the right hand side of the forest plot.

• value_label: The label of the value of the variable to be evaluated.

• value_cont: The value for continuous variables.

• value_cat: The value for categorical variables.

• is_ref_value: Whether the value is the reference value.

• show_ref_value: Whether to show the reference value in the plot and table. This is TRUE by default for is_ref_value == TRUE, otherwise NA (and ignored).

• is_covariate: Whether the variable is a covariate (TRUE) or exposure variable (FALSE).

Examples

data(d_sim_binom_cov_hgly2)

ermod_bin <- dev_ermod_bin(
  data = d_sim_binom_cov_hgly2,
  var_resp = "AEFLAG",
  var_exposure = "AUCss_1000",
  var_cov = c("BHBA1C_5", "RACE"),
)

spec_coveff <- build_spec_coveff(ermod_bin)
plot_coveff(ermod_bin, spec_coveff = spec_coveff)

BayesERtools documentation built on June 8, 2025, 1:26 p.m.