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#' @export
model_parameters.nestedLogit <- function(model,
ci = 0.95,
ci_method = NULL,
component = "all",
bootstrap = FALSE,
iterations = 1000,
standardize = NULL,
exponentiate = FALSE,
p_adjust = NULL,
vcov = NULL,
vcov_args = NULL,
include_info = getOption("parameters_info", FALSE),
keep = NULL,
drop = NULL,
verbose = TRUE,
...) {
dots <- list(...)
# set default
if (is.null(ci_method)) {
if (isTRUE(bootstrap)) {
ci_method <- "quantile"
} else if (!is.null(vcov) || !is.null(vcov_args)) {
ci_method <- "wald"
} else {
ci_method <- "profile"
}
}
# "component" might be set to "conditional", when called from "compare_parameters()"
# set to "all" here.
if (identical(component, "conditional")) {
component <- "all"
}
# profiled CIs may take a long time to compute, so we warn the user about it
if (any(unlist(insight::n_obs(model)) > 1e4) && identical(ci_method, "profile")) {
insight::format_alert(
"Profiled confidence intervals may take longer time to compute.",
"Use `ci_method=\"wald\"` for faster computation of CIs."
)
}
# tell user that profiled CIs don't respect vcov-args
if (identical(ci_method, "profile") && (!is.null(vcov) || !is.null(vcov_args)) && isTRUE(verbose)) {
insight::format_alert(
"When `ci_method=\"profile\"`, `vcov` only modifies standard errors, test-statistic and p-values, but not confidence intervals.", # nolint
"Use `ci_method=\"wald\"` to return confidence intervals based on robust standard errors."
)
}
fun_args <- list(
model = model,
ci = ci,
ci_method = ci_method,
component = component,
bootstrap = bootstrap,
iterations = iterations,
merge_by = c("Parameter", "Response", "Component"),
standardize = standardize,
exponentiate = exponentiate,
p_adjust = p_adjust,
include_info = include_info,
keep_parameters = keep,
drop_parameters = drop,
vcov = vcov,
vcov_args = vcov_args
)
fun_args <- c(fun_args, dots)
out <- do.call(".model_parameters_generic", fun_args)
attr(out, "object_name") <- insight::safe_deparse_symbol(substitute(model))
out
}
#' @export
standard_error.nestedLogit <- function(model,
component = "all",
vcov = NULL,
vcov_args = NULL,
verbose = TRUE,
...) {
dots <- list(...)
se <- NULL
# vcov: matrix
if (is.matrix(vcov)) {
se <- sqrt(diag(vcov))
}
# vcov: function which returns a matrix
if (is.function(vcov)) {
fun_args <- c(list(model), vcov_args, dots)
se <- .safe(sqrt(diag(do.call("vcov", fun_args))))
}
# vcov: character
if (is.character(vcov)) {
.vcov <- insight::get_varcov(
model,
component = component,
vcov = vcov,
vcov_args = vcov_args,
verbose = verbose,
...
)
se <- unlist(lapply(.vcov, function(i) sqrt(diag(i))), use.names = FALSE)
}
# classical se from summary()
if (is.null(se)) {
se <- as.vector(as.data.frame(do.call(rbind, lapply(model$models, function(i) {
stats::coef(summary(i))
})))[, "Std. Error"])
}
# classical se from get_varcov()
if (is.null(se)) {
.vcov <- insight::get_varcov(
model,
component = component,
verbose = verbose,
...
)
se <- unlist(lapply(.vcov, function(i) sqrt(diag(i))), use.names = FALSE)
}
params <- insight::get_parameters(model, component = component)
.data_frame(
Parameter = params$Parameter,
SE = as.vector(se),
Response = params$Response,
Component = params$Component
)
}
#' @export
p_value.nestedLogit <- function(model,
dof = NULL,
method = NULL,
component = "all",
vcov = NULL,
vcov_args = NULL,
verbose = TRUE,
...) {
if (is.null(vcov)) {
p <- as.vector(as.data.frame(do.call(rbind, lapply(model$models, function(i) {
stats::coef(summary(i))
})))[, "Pr(>|z|)"])
} else {
p <- p_value.default(
model,
dof = dof,
method = method,
component = component,
vcov = vcov,
vcov_args = vcov_args,
verbose = verbose,
...
)[["p"]]
}
params <- insight::get_parameters(model, component = component)
.data_frame(
Parameter = params$Parameter,
p = p,
Response = params$Response,
Component = params$Component
)
}
#' @export
ci.nestedLogit <- function(x,
ci = 0.95,
dof = NULL,
method = "profile",
component = "all",
vcov = NULL,
vcov_args = NULL,
verbose = TRUE,
...) {
out <- lapply(
x$models,
ci,
dof = dof,
method = method,
vcov = vcov,
vcov_args = vcov_args,
verbose = verbose,
...
)
for (i in names(out)) {
out[[i]]$Component <- i
}
out <- do.call(rbind, out)
row.names(out) <- NULL
if (!is.null(component) && !identical(component, "all")) {
comp <- intersect(names(x$models), component)
if (!length(comp) && verbose) {
insight::format_alert(
paste0(
"No matching model found. Possible values for `component` are ",
toString(paste0("\"", names(x$models), "\"")),
"."
)
)
} else {
out <- out[out$Component %in% component, ]
}
}
params <- insight::get_parameters(x, component = component)
out$Response <- params$Response
out[c("Parameter", "CI", "CI_low", "CI_high", "Response", "Component")]
}
#' @export
simulate_model.nestedLogit <- function(model, iterations = 1000, ...) {
if (is.null(iterations)) iterations <- 1000
params <- insight::get_parameters(model, component = "all", verbose = FALSE)
varcov <- insight::get_varcov(model, component = "all", verbose = FALSE, ...)
out <- lapply(unique(params$Component), function(i) {
pars <- params[params$Component == i, ]
betas <- stats::setNames(pars$Estimate, pars$Parameter)
d <- as.data.frame(.mvrnorm(n = iterations, mu = betas, Sigma = varcov[[i]]))
d$Component <- i
d
})
out <- do.call(rbind, out)
class(out) <- c("parameters_simulate_model", class(out))
attr(out, "object_name") <- insight::safe_deparse_symbol(substitute(model))
out
}
#' @export
simulate_parameters.nestedLogit <- function(model,
iterations = 1000,
centrality = "median",
ci = 0.95,
ci_method = "quantile",
test = "p-value",
...) {
sim_data <- simulate_model(model, iterations = iterations, ...)
out <- lapply(unique(sim_data$Component), function(i) {
pars <- sim_data[sim_data$Component == i, ]
d <- .summary_bootstrap(
data = pars,
test = test,
centrality = centrality,
ci = ci,
ci_method = ci_method,
...
)
d$Component <- i
d
})
out <- do.call(rbind, out)
class(out) <- c("parameters_simulate", "see_parameters_simulate", class(out))
attr(out, "object_name") <- insight::safe_deparse_symbol(substitute(model))
attr(out, "iterations") <- iterations
attr(out, "ci") <- ci
attr(out, "ci_method") <- ci_method
attr(out, "centrality") <- centrality
attr(out, "simulated") <- TRUE
out
}
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