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#' Create an aba control object.
#'
#' The aba control which determines how an aba summary will be calculated and
#' printed to console.
#'
#' @param include_covariates boolean. Whether to include covariates in coefs
#' @param include_intercept boolean. Whether to include intercept in coefs
#' @param pval_digits integer. How many decimals of a pvalue to show
#' @param aic_digits integer. How many decimals of AIC value to show
#' @param metric_digits integer. Default value of how many decimals to show
#' for model metrics (e.g., auc, adj.r.squared, etc)
#' @param coef_digits integer. Default value of how many decimals to show
#' for model coefficients
#' @return a list with the control parameters specified
#' @export
#'
#' @examples
#'
#' df <- adnimerge %>% dplyr::filter(VISCODE == 'bl')
#'
#' # standard example
#' model <- df %>% aba_model() %>%
#' set_groups(everyone()) %>%
#' set_outcomes(CSF_ABETA_STATUS_bl) %>%
#' set_predictors(
#' PLASMA_PTAU181_bl, PLASMA_NFL_bl,
#' c(PLASMA_PTAU181_bl, PLASMA_NFL_bl)
#' ) %>%
#' set_covariates(AGE, GENDER, EDUCATION) %>%
#' set_stats('glm') %>%
#' aba_fit()
#'
#' # no control -> default
#' model_summary <- model %>% aba_summary()
#' print(model_summary)
#'
#' # add a control object - don't include covariate coefficients
#' my_control <- aba_control(include_covariates = FALSE)
#' model_summary2 <- model %>% aba_summary(control = my_control)
#' print(model_summary2)
#'
aba_control <- function(include_intercept = FALSE,
include_covariates = TRUE,
pval_digits = 4,
aic_digits = 0,
metric_digits = 2,
coef_digits = 2) {
ctrl <- list(
include_covariates = include_covariates,
include_intercept = include_intercept,
pval_digits = pval_digits,
aic_digits = aic_digits,
metric_digits = metric_digits,
coef_digits = coef_digits
)
class(ctrl) <- 'abaControl'
return(ctrl)
}
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