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#' @export
model_parameters.svy2lme <- function(model,
ci = 0.95,
effects = "all",
include_sigma = FALSE,
keep = NULL,
drop = NULL,
verbose = TRUE,
...) {
dots <- list(...)
# which component to return?
effects <- match.arg(effects, choices = c("fixed", "random", "all"))
params <- params_variance <- NULL
if (effects %in% c("fixed", "all")) {
# Processing
fun_args <- list(
model,
ci = ci,
ci_method = "wald",
standardize = NULL,
p_adjust = NULL,
wb_component = FALSE,
keep_parameters = keep,
drop_parameters = drop,
verbose = verbose,
include_sigma = include_sigma,
include_info = FALSE,
vcov = NULL,
vcov_args = NULL
)
fun_args <- c(fun_args, dots)
params <- do.call(".extract_parameters_mixed", fun_args)
params$Effects <- "fixed"
}
att <- attributes(params)
if (effects %in% c("random", "all")) {
params_variance <- .extract_random_variances(
model,
ci = ci,
effects = effects
)
}
# merge random and fixed effects, if necessary
if (!is.null(params) && !is.null(params_variance)) {
params$Level <- NA
params$Group <- ""
params <- params[match(colnames(params_variance), colnames(params))]
}
params <- rbind(params, params_variance)
# remove empty column
if (!is.null(params$Level) && all(is.na(params$Level))) {
params$Level <- NULL
}
# due to rbind(), we lose attributes from "extract_parameters()",
# so we add those attributes back here...
if (!is.null(att)) {
attributes(params) <- utils::modifyList(att, attributes(params))
}
params <- .add_model_parameters_attributes(
params,
model,
ci = ci,
exponentiate = FALSE,
bootstrap = FALSE,
iterations = 1000,
ci_method = "wald",
p_adjust = NULL,
verbose = verbose,
include_info = FALSE,
group_level = FALSE,
wb_component = FALSE,
...
)
attr(params, "object_name") <- insight::safe_deparse_symbol(substitute(model))
class(params) <- c("parameters_model", "see_parameters_model", class(params))
params
}
#' @export
standard_error.svy2lme <- function(model, ...) {
.data_frame(
Parameter = .remove_backticks_from_string(colnames(model$Vbeta)),
SE = as.vector(sqrt(diag(model$Vbeta)))
)
}
#' @export
p_value.svy2lme <- function(model, ...) {
stat <- insight::get_statistic(model)
p <- 2 * stats::pnorm(abs(stat$Statistic), lower.tail = FALSE)
.data_frame(
Parameter = stat$Parameter,
p = as.vector(p)
)
}
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