summary.emdi: Summarizes an emdiObject
In akreutzmann/fayherriot:

Description Usage Arguments Value See Also Examples

Additional information about the data and model in small area estimation methods and components of an emdi object are extracted. The returned object is suitable for printing with the print.summary.emdi method.

1 2	## S3 method for class 'emdi' summary(object, ...)

`object`	an object of type "emdi", representing point and MSE estimates. Objects differ depending on the estimation method: direct vs. model-based.
`...`	additional arguments that are not used in this method.

an object of type "summary.emdi" with following components:

`out_of_smp`	if model-based estimation, number of out-of-sample domains equivalent to `N_dom_unobs` (see `emdiObject`).
`in_smp`	number of in-sample domains equivalent to `N_dom_smp` (see `emdiObject`).
`size_smp`	number of units in sample equivalent to `N_smp` (see `emdiObject`).
`size_pop`	if empirical best prediction, number of units in population equivalent to `N_pop` (see `emdiObject`).
`size_dom`	a data frame with rows Sample_domains and Population_domains (if ebp) representing summary statistics of the sample sizes across domains of sample and population data, respectively.
`transform`	if model-based estimation, a data frame with columns Transformation, Method, Optimal_lambda and Shift_parameter representing the chosen transformation type and estimation method for lambda as well as their results.
`normality`	if model-based estimation, a data frame with columns Skewness, Kurtosis, Shapiro_W and Shapiro_p where the latter two represent the results of a Shapiro-Wilks-Test for normality. Rows correspond to Pearson residuals and random effects of the nested error regression model. The functions `skewness` and `kurtosis` are from the package moments. Details for the Shapiro-Wilks-Test are provided by `shapiro.test`.
`icc`	if empirical best prediction, the value of the intraclass coefficient.
`coeff_determ`	if empirical best prediction, a data frame with columns Marginal_R2 and Conditional_R2 representing two R2 measures for linear mixed models from the MuMIn package obtained by function `r.squaredGLMM`.
`model`	if Fay-Herriot, a list with model components such as information criteria, coefficients of determination or variance and MSE estimation methods.
`call`	a list containing an image of the function call that produced the object.

emdiObject, direct, ebp, r.squaredGLMM, skewness, kurtosis, shapiro.test

## Not run: 
# 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, threshold){max(y)},
my_min = function(y, threshold){min(y)}), na.rm = TRUE, cpus = 1)

# Receive first overview
summary(emdi_model)


# Load aggregated 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")

# Estimation of EBLUP means without transformation --------------------------

# REML
fh_reml <- fh(fixed = Mean ~ eqsize + cash + self_empl, vardir = "Var_Mean",
              combined_data = combined_data, domains = "Domain",
              method = "reml", interval = c(0, 100000000))
# Receive first overview
summary(fh_reml)

## End(Not run)