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#' @title Parameters from Generalized Additive (Mixed) Models
#' @name model_parameters.cgam
#'
#' @description Extract and compute indices and measures to describe parameters
#' of generalized additive models (GAM(M)s).
#'
#' @param model A gam/gamm model.
#' @inheritParams model_parameters.default
#'
#' @seealso [insight::standardize_names()] to rename
#' columns into a consistent, standardized naming scheme.
#'
#' @details The reporting of degrees of freedom *for the spline terms*
#' slightly differs from the output of `summary(model)`, for example in the
#' case of `mgcv::gam()`. The *estimated degrees of freedom*, column
#' `edf` in the summary-output, is named `df` in the returned data
#' frame, while the column `df_error` in the returned data frame refers to
#' the residual degrees of freedom that are returned by `df.residual()`.
#' Hence, the values in the the column `df_error` differ from the column
#' `Ref.df` from the summary, which is intentional, as these reference
#' degrees of freedom \dQuote{is not very interpretable}
#' ([web](https://stat.ethz.ch/pipermail/r-help/2019-March/462135.html)).
#'
#' @return A data frame of indices related to the model's parameters.
#'
#' @examples
#' library(parameters)
#' if (require("mgcv")) {
#' dat <- gamSim(1, n = 400, dist = "normal", scale = 2)
#' model <- gam(y ~ s(x0) + s(x1) + s(x2) + s(x3), data = dat)
#' model_parameters(model)
#' }
#' @export
model_parameters.cgam <- function(model,
ci = 0.95,
ci_method = "residual",
bootstrap = FALSE,
iterations = 1000,
standardize = NULL,
exponentiate = FALSE,
p_adjust = NULL,
keep = NULL,
drop = NULL,
verbose = TRUE,
...) {
# validation check, warn if unsupported argument is used.
dot_args <- .check_dots(
dots = list(...),
not_allowed = c("vcov", "vcov_args", "component"),
class(model)[1],
verbose = verbose
)
# Processing
if (bootstrap) {
params <- bootstrap_parameters(
model,
iterations = iterations,
ci = ci,
...
)
} else {
fun_args <- list(
model,
ci = ci,
ci_method = ci_method,
component = "all",
merge_by = c("Parameter", "Component"),
standardize = standardize,
p_adjust = p_adjust,
keep_parameters = keep,
drop_parameters = drop,
verbose = verbose,
vcov = NULL,
vcov_args = NULL
)
fun_args <- c(fun_args, dot_args)
params <- do.call(".extract_parameters_generic", fun_args)
}
# fix statistic column
if ("t" %in% names(params) && !is.null(params$Component) && "smooth_terms" %in% params$Component) {
names(params)[names(params) == "t"] <- "t / F"
}
# fix estimated df column
if (inherits(model, c("gam", "cgam", "scam", "rqss")) && "smooth_terms" %in% params$Component && !("df" %in% names(params))) { # nolint
params$df <- params$Coefficient
params$df[params$Component != "smooth_terms"] <- NA
params$df_error[params$Component == "smooth_terms"] <- NA
params$Coefficient[params$Component == "smooth_terms"] <- NA
# reorder
insert_column <- which(names(params) == "df_error")
if (!length(insert_column)) {
insert_column <- which(names(params) == "p")
}
if (length(insert_column)) {
n_col <- ncol(params)
params <- params[c(1:(insert_column - 1), n_col, insert_column:(n_col - 1))]
}
} else if (all(c("df", "df_error") %in% names(params)) && "smooth_terms" %in% params$Component) {
params$df_error[params$Component == "smooth_terms"] <- NA
}
# exponentiate coefficients and SE/CI, if requested
params <- .exponentiate_parameters(params, model, exponentiate)
params <- .add_model_parameters_attributes(
params,
model,
ci,
exponentiate,
p_adjust = p_adjust,
verbose = verbose,
...
)
if ("CI" %in% colnames(params)) {
params$CI[is.na(params$CI_low)] <- NA
}
attr(params, "object_name") <- insight::safe_deparse_symbol(substitute(model))
class(params) <- c("parameters_model", "see_parameters_model", class(params))
params
}
#' @export
p_value.cgam <- function(model, component = "all", ...) {
component <- insight::validate_argument(
component,
c("all", "conditional", "smooth_terms")
)
params <- insight::get_parameters(model, component = "all")
cs <- summary(model)
p <- as.vector(cs$coefficients[, 4])
if (!is.null(cs$coefficients2)) p <- c(p, as.vector(cs$coefficients2[, "p.value"]))
out <- .data_frame(
Parameter = params$Parameter,
Component = params$Component,
p = as.vector(p)
)
if (component != "all") {
out <- out[out$Component == component, ]
}
out
}
#' @export
standard_error.cgam <- function(model, ...) {
sc <- summary(model)
se <- as.vector(sc$coefficients[, "StdErr"])
params <- insight::get_parameters(model, component = "all")
if (!is.null(sc$coefficients2)) se <- c(se, rep(NA, nrow(sc$coefficients2)))
.data_frame(
Parameter = params$Parameter,
SE = se,
Component = params$Component
)
}
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