#' @export
model_parameters.HLfit <- model_parameters.default
#' @export
ci.HLfit <- function(x,
ci = 0.95,
method = "wald",
iterations = 100,
...) {
method <- match.arg(tolower(method), choices = c("wald", "ml1", "betwithin", "profile", "boot"))
# Wald approx
if (method == "wald") {
out <- .ci_generic(model = x, ci = ci, dof = Inf)
# ml1 approx
} else if (method == "ml1") {
out <- ci_ml1(x, ci)
# betwithin approx
} else if (method == "betwithin") {
out <- ci_betwithin(x, ci)
# profiled
} else if (method == "profile") {
nparms <- n_parameters(x)
conf <- stats::confint(x, parm = 1:nparms, level = ci, verbose = FALSE, boot_args = NULL)
if (nparms == 1) {
out <- as.data.frame(t(conf$interval))
} else {
out <- as.data.frame(do.call(rbind, lapply(conf, function(i) i$interval)))
}
colnames(out) <- c("CI_low", "CI_high")
out$Parameter <- insight::find_parameters(x, effects = "fixed", flatten = TRUE)
out$CI <- ci
out <- out[c("Parameter", "CI", "CI_low", "CI_high")]
}
# # bootstrapping
# } else if (method == "boot") {
# out <- stats::confint(x, parm = n_parameters(x), level = ci, verbose = FALSE, boot_args = list(nsim = iterations, showpbar = FALSE))
# }
out
}
#' @export
standard_error.HLfit <- function(model, method = NULL, ...) {
if (is.null(method)) method <- "wald"
utils::capture.output({
se <- summary(model)$beta_table[, 2]
})
.data_frame(
Parameter = insight::find_parameters(model,
effects = "fixed",
component = "conditional",
flatten = TRUE
),
SE = as.vector(se)
)
}
#' @export
p_value.HLfit <- p_value.cpglmm
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