R/summary.emdi.R

In SoerenPannier/emdi: Estimating and Mapping Disaggregated Indicators

#' Summarizes an emdiObject
#'
#' Additional information about the data and model in small area estimation
#' methods and components of an emdi object are extracted. The generic function
#' summary has methods for classes "direct", "ebp" and "fh" and the returned
#' object is suitable for printing  with the \code{print}.
#' @param object an object of type "direct", "ebp" or "fh", representing point
#' and MSE estimates. Objects differ depending on the estimation method.
#' @param ... additional arguments that are not used in this method.
#' @return an object of type "summary.direct", "summary.ebp" or "summary.fh"
#' with information about the sample and population data, the usage of
#' transformation, normality tests and information of the model fit.
#' @references
#' Lahiri, P. and Suntornchost, J. (2015), Variable selection for linear mixed
#' models with applications in small area estimation, The Indian Journal of
#' Statistics 77-B(2), 312-320. \cr \cr
#' Marhuenda, Y., Morales, D. and Pardo, M.C. (2014). Information criteria for
#' Fay-Herriot model selection. Computational Statistics and Data Analysis 70,
#' 268-280. \cr \cr
#' Nakagawa S, Schielzeth H (2013). A general and simple method for obtaining
#' R2 from generalized linear mixed-effects models. Methods in Ecology and
#' Evolution, 4(2), 133-142.
#' @seealso \code{\link{emdiObject}}, \code{\link{direct}}, \code{\link{ebp}},
#' \code{\link{fh}}, \code{\link[MuMIn]{r.squaredGLMM}},
#' \code{\link[moments]{skewness}},
#' \code{\link[moments]{kurtosis}}, \code{\link[stats]{shapiro.test}}
#' @examples
#' \donttest{
#' # Example for models of type ebp
#'
#' # Loading data - population and sample data
#' data("eusilcA_pop")
#' data("eusilcA_smp")
#'
#' # Example with two additional indicators
#' emdi_model <- ebp(
#'   fixed = eqIncome ~ gender + eqsize + cash +
#'     self_empl + unempl_ben + age_ben + surv_ben + sick_ben + dis_ben + rent +
#'     fam_allow + house_allow + cap_inv + tax_adj, pop_data = eusilcA_pop,
#'   pop_domains = "district", smp_data = eusilcA_smp, smp_domains = "district",
#'   threshold = function(y) {
#'     0.6 * median(y)
#'   }, L = 50, MSE = TRUE, B = 50,
#'   custom_indicator = list(
#'     my_max = function(y) {
#'       max(y)
#'     },
#'     my_min = function(y) {
#'       min(y)
#'     }
#'   ), na.rm = TRUE, cpus = 1
#' )
#'
#' # Example 1: Receive first overview
#' summary(emdi_model)
#'
#'
#' # Example for models of type fh
#'
#' # Loading data - population and sample data
#' data("eusilcA_popAgg")
#' data("eusilcA_smpAgg")
#'
#' # Combine sample and population data
#' combined_data <- combine_data(
#'   pop_data = eusilcA_popAgg,
#'   pop_domains = "Domain",
#'   smp_data = eusilcA_smpAgg,
#'   smp_domains = "Domain"
#' )
#'
#' # Generation of the emdi object
#' fh_std <- fh(
#'   fixed = Mean ~ cash + self_empl, vardir = "Var_Mean",
#'   combined_data = combined_data, domains = "Domain",
#'   method = "ml", MSE = TRUE
#' )
#'
#' # Example 2: Receive first overview
#' summary(fh_std)
#' }
#' @name emdi_summaries
#' @order 1
NULL