An object of class emdi that represents point predictions of regional
disaggregated indicators. Optionally it also contains corresponding MSE
estimates. Depending on the estimation the object is also of class direct
or model. Objects of these classes have methods for the generic functions
estimators, print, plot (only for
class model) and summary.
The following components are always included in an emdi object:
calla list containing an image of the function call that produced the object.
fixeda formula of fixed effects used in the nested error linear
regression (see also fixed in ebp). Not
filled for class direct.
frameworka list with following components:
rll
N_dom_smp number of domains in the sample.
N_dom_unobs number of out-of-sample domains. Not
filled for class direct.
N_pop total number of units in population. Not
filled for class direct.
N_smp total number of units in sample.
pop_domains_vec an arranged vector
of the domain indicator variable. Not
filled for class direct.
smp_data an arranged data set of sample data. Not
filled for class direct.
smp_domains a character naming the
domain indicator variable.
smp_domains_vec an arranged vector of
the domain indicator variable.
inddata frame containing estimates for indicators per domain.
methodcharacter returning the method for estimation of the optimal lambda, here "reml". Not filled for class direct.
modelan object returned by the lme function of type "lme"
and representing a fitted linear mixed-effects model (for
further explanations see lme and
lmeObject). Not
filled for class direct.
MSEdata frame containing MSE estimates corresponding to the
point predictions in ind per indicator per domain if MSE is selected
to be TRUE in function call. If FALSE, MSE is
NULL.
transformationcharacter returning the selected transformation
type (see also transformation in ebp). Not
filled for class direct.
transform_parama list with two elements, optimal_lambda
and shift_par, where the first contains the optimal parameter for a
Box-Cox transformation or NULL for no and log transformation and the
second the potential shift parameter in the log or Box-Cox transformation
and NULL for no transformation. Not filled for class direct.
successful_bootstrapsa matrix with domains as rows and indicators as columns. The cells contain the number of successful bootstraps for each combination. Not filled for class model.
Alfons, A. and Templ, M. (2013). Estimation of Social Exclusion Indicators from Complex Surveys: The R Package laeken. Journal of Statistical Software, 54(15), 1-25. Molina, I. and Rao, J.N.K. (2010). Small area estimation of poverty indicators. The Canadian Journal of Statistics, Vol. 38, No.3, 369-385.