#' Regression Coefficients
#'
#' Displays a table for regression coefficients.
#' Multiple models can be added (x, y, and z).
#' @param x a model object
#' @param y a model object
#' @param z a model object
#' @param 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.
#' @param unstandardized Logical, indicating whether or not to print
#' unstandardized estimates. Default is TRUE.
#' @param ci_level Confidence Interval (CI) level. Default to 0.95 (95%)
#' @param ci_method Documention 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.
#' @param bootstrap Documention based on ?parameters::parameters.
#' Should estimates be based on bootstrapped model? If TRUE, then arguments
#' of Bayesian regressions apply (see also bootstrap_parameters()).
#' @param iterations Documention based on ?parameters::parameters.
#' The number of bootstrap replicates. This only apply in the case of
#' bootstrapped frequentist models.
#' @param effects "fixed" or "all" fixed and random effects.
#' default is "all"
#' @param digits How many decimal places to round to? Default is 3.
#' @param print Create a knitr table for displaying as html table?
#' (default = TRUE)
#' @export
#'
regression_coeff <- function(x, y = NULL, z = NULL,
standardized = TRUE,
unstandardized = TRUE,
ci_level = 0.95, ci_method = NULL,
bootstrap = FALSE, iterations = NULL,
effects = "all",
digits = 3, print = TRUE) {
table <- get_coeff(x, effects = effects,
standardized = standardized,
ci_level = ci_level, ci_method = ci_method,
bootstrap = bootstrap,
iterations = iterations)
table <- dplyr::mutate(table, Model = "H1")
x_formula <- insight::find_formula(x)$conditional |>
deparse() |>
stringr::str_trim("left") |>
paste(collapse = "")
x_n <- insight::model_info(x)$n_obs
dv <- insight::find_response(x)
footer_x <- paste("H1: ", x_formula, "; N = ", x_n, sep = "")
if (!is.null(y)) {
y_table <- get_coeff(y, effects = effects,
standardized = standardized,
ci_level = ci_level, ci_method = ci_method,
bootstrap = bootstrap,
iterations = iterations)
y_table <- dplyr::mutate(y_table, Model = "H2")
y_formula <- insight::find_formula(y)$conditional |>
deparse() |>
stringr::str_trim("left") |>
paste(collapse = "")
y_n <- insight::model_info(y)$n_obs
table <- dplyr::bind_rows(table, y_table)
footer_y <- paste("H2: ", y_formula, "; N = ", y_n, sep = "")
}
if (!is.null(z)) {
z_table <- get_coeff(z, effects = effects,
standardized = standardized,
ci_level = ci_level, ci_method = ci_method,
bootstrap = bootstrap,
iterations = iterations)
z_table <- dplyr::mutate(z_table, Model = "H3")
z_formula <- insight::find_formula(z)$conditional |>
deparse() |>
stringr::str_trim("left") |>
paste(collapse = "")
z_n <- insight::model_info(z)$n_obs
table <- dplyr::bind_rows(table, z_table)
footer_z <- paste("H3: ", z_formula, "; N = ", z_n, sep = "")
}
table <- dplyr::relocate(table, Model, .before = Term)
if (print == TRUE) {
table_title <- paste("Regression Coefficients: ", dv, sep = "")
table <- gt::gt(table) |>
table_styling() |>
gt::tab_header(title = table_title) |>
gt::cols_align(align = "left", columns = c(Model, Term)) |>
gt::sub_small_vals(columns = p, threshold = .001) |>
gt::fmt_number(decimals = 3, use_seps = FALSE) |>
gt::fmt_number(columns = df, decimals = 0, use_seps = FALSE) |>
gt::tab_footnote(footer_x) |>
gt::cols_merge_range(col_begin = ci_low_unstd,
col_end = ci_high_unstd,
sep = gt::html(" — "))
if (unstandardized == TRUE & standardized == TRUE) {
table <- table |>
gt::cols_merge_range(col_begin = ci_low_std,
col_end = ci_high_std,
sep = gt::html(" — ")) |>
gt::cols_hide(columns = c(SE)) |>
gt::tab_spanner(label = "Unstandardized",
columns = c(b, ci_low_unstd)) |>
gt::tab_spanner(label = "Standardized",
columns = c(B, ci_low_std, SE_B)) |>
gt::cols_label(ci_low_unstd = "95% CI",
B = "β",
ci_low_std = "95% CI",
SE_B = "SE")
}
if (unstandardized == TRUE & standardized == FALSE) {
table <- table |>
gt::tab_spanner(label = "Unstandardized",
columns = c(b, ci_low_unstd, SE)) |>
gt::cols_label(ci_low_unstd = "95% CI")
}
if (unstandardized == FALSE & standardized == TRUE) {
table <- table |>
gt::cols_merge_range(col_begin = ci_low_std,
col_end = ci_high_std,
sep = gt::html(" — ")) |>
gt::cols_hide(columns = c(b, ci_low_unstd, SE)) |>
gt::tab_spanner(label = "Standardized",
columns = c(B, ci_low_std, SE_B)) |>
gt::cols_label(B = "β",
ci_low_std = "95% CI",
SE_B = "SE")
}
if (!is.null(y)) {
table <- gt::tab_footnote(table, footer_y)
}
if (!is.null(z)) {
table <- gt::tab_footnote(table, footer_z)
}
} else if (print == FALSE) {
table <- as.data.frame(table)
}
return(table)
}
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