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# generic function for CI calculation
.ci_generic <- function(model,
ci = 0.95,
method = "wald",
dof = NULL,
effects = "fixed",
component = "all",
vcov = NULL,
vcov_args = NULL,
verbose = TRUE,
...) {
# check method
if (is.null(method)) {
method <- "wald"
}
method <- tolower(method)
method <- insight::validate_argument(
method,
c(
"wald", "ml1", "betwithin", "kr", "satterthwaite", "kenward", "boot",
"profile", "residual", "normal"
)
)
effects <- insight::validate_argument(effects, c("fixed", "random", "all"))
component <- insight::validate_argument(
component,
c(
"all", "conditional", "zi", "zero_inflated", "dispersion", "precision",
"scale", "smooth_terms", "full", "marginal"
)
)
if (method == "ml1") { # nolint
return(ci_ml1(model, ci = ci))
} else if (method == "betwithin") {
return(ci_betwithin(model, ci = ci))
} else if (method == "satterthwaite") {
return(ci_satterthwaite(model, ci = ci))
} else if (method %in% c("kenward", "kr")) {
return(ci_kenward(model, ci = ci))
}
# default CIs follow here (methods wald, boot, profile, residual, normal)
out <- lapply(ci, function(i) {
.ci_dof(
model = model,
ci = i,
dof = dof,
effects = effects,
component = component,
method = method,
vcov = vcov,
vcov_args = vcov_args,
verbose = verbose,
...
)
})
out <- do.call(rbind, out)
row.names(out) <- NULL
out
}
#' @keywords internal
.ci_dof <- function(model,
ci,
dof,
effects,
component,
method = "wald",
se = NULL,
vcov = NULL,
vcov_args = NULL,
verbose = TRUE,
...) {
# need parameters to calculate the CIs
if (inherits(model, "emmGrid")) {
params <- insight::get_parameters(
model,
effects = effects,
component = component,
merge_parameters = TRUE
)
} else {
params <- insight::get_parameters(model,
effects = effects,
component = component,
verbose = FALSE
)
}
# check if all estimates are non-NA
params <- .check_rank_deficiency(model, params, verbose = FALSE)
# for polr, we need to fix parameter names
params$Parameter <- gsub("Intercept: ", "", params$Parameter, fixed = TRUE)
# validation check...
if (is.null(method)) {
method <- "wald"
}
method <- tolower(method)
# Fist, we want standard errors for parameters
# --------------------------------------------
# if we have adjusted SE, e.g. from kenward-roger, don't recompute
# standard errors to save time...
if (is.null(se)) {
if (!is.null(vcov) || isTRUE(list(...)[["robust"]])) {
# robust (HC) standard errors?
stderror <- standard_error(model,
component = component,
vcov = vcov,
vcov_args = vcov_args,
verbose = verbose,
...
)
} else {
# normal standard errors, including small-sample approximations
stderror <- switch(method,
kenward = se_kenward(model),
kr = se_kenward(model),
satterthwaite = se_satterthwaite(model),
standard_error(model, component = component)
)
}
# if we have a non-empty stderror, use it
if (insight::is_empty_object(stderror)) {
return(NULL)
}
# filter non-matching parameters, resp. sort stderror and parameters,
# so both have the identical order of values
if (nrow(stderror) != nrow(params) ||
!all(stderror$Parameter %in% params$Parameter) ||
!all(order(stderror$Parameter) == order(params$Parameter))) {
params <- stderror <- merge(stderror, params, sort = FALSE)
}
se <- stderror$SE
}
# Next, we need degrees of freedom
# --------------------------------
# check if we have a valid dof vector
if (is.null(dof)) {
# residual df
dof <- insight::get_df(x = model, type = method, verbose = FALSE)
# make sure we have a value for degrees of freedom
if (is.null(dof) || length(dof) == 0 || .is_chi2_model(model, dof)) {
dof <- Inf
} else if (length(dof) > nrow(params)) {
# filter non-matching parameters
dof <- dof[seq_len(nrow(params))]
}
}
# Now we can calculate CIs
# ------------------------
alpha <- (1 + ci) / 2
fac <- suppressWarnings(stats::qt(alpha, df = dof))
out <- cbind(
CI_low = params$Estimate - se * fac,
CI_high = params$Estimate + se * fac
)
out <- as.data.frame(out)
out$CI <- ci
out$Parameter <- params$Parameter
out <- out[c("Parameter", "CI", "CI_low", "CI_high")]
if ("Component" %in% names(params)) out$Component <- params$Component
if ("Effects" %in% names(params) && effects != "fixed") out$Effects <- params$Effects
if ("Response" %in% names(params)) out$Response <- params$Response
# for cox-panel models, we have non-linear parameters with NA coefficient,
# but test statistic and p-value - don't check for NA estimates in this case
if (anyNA(params$Estimate) && !inherits(model, "coxph.penal")) {
out[stats::complete.cases(out), ]
} else {
out
}
}
.is_chi2_model <- function(model, dof) {
statistic <- insight::find_statistic(model)
(all(dof == 1) && identical(statistic, "chi-squared statistic"))
}
|
