SAEval (version 1.0.0)

coverage: Coverage diagnostic

Description

coverage diagnostic tests the validity between the 95% adjusted confidence intervals of the model based estimates making comparison with the corresponding adjusted confidence intervals for the direct estimates.

Usage

coverage(data,dir,sae,v.dir,mse.sae,alfa=0.05)

Value

Object of class data.frame. The data frame contains information for the small area estimators (methods), non-coverage total (non_coverage), number of small area domains (domains), non-overlap ratio (non_overlap), p-value for Binomial statistic (p_value) and the test result (results).

Arguments

data

a data frame containing the direct and small area estimates among with their variance, e.g. SAEval_example.

dir

formula identifing the direct estimates.

sae

formula identifing the small area estimates.

v.dir

formula identifing the direct estimates variance.

mse.sae

formula identifing the small area estimates mean squared error.

alfa

double number. The significance level of the non-parametric Binomial test (default=0.05).

Author

Developed by Andrea Fasulo

Details

This diagnostic measures the overlap between the confidence intervals, which is expected to be not significantly different from the 95% of the numbers of small areas.

The small area with both direct estimate and variance of the direct estimates equal to NA value are automatically removed from the data.

References

Brown, G., Chambers, R., Heady, P., Heasman, D. (2001), Evaluation of small area estimation methods - An application to unemployment estimates from the UK LFS, in Proceedings of Statistics Canada Symposium 2001: Achieving Data Quality in a Statistical Agency: A Methodological Perspective, Statistics Canada.

Mukhopadhyay, P. K., McDowell, A. (2011). Small area estimation for survey data analysis using SAS software, http://support.sas.com/rnd/app/papers/smallarea.pdf.

Srivastava, A. K., Sud, U. C., Chandra, H. (2007). Small area estimation - An application to National Sample Survey Data, Journal of the Indian Society of Agricultural Statistics, 61(2), 249-254.

Examples

# Load example data
data(SAEval_example)

SAEval.coverage<-coverage(data=SAEval_example,
       dir=~y_d,
       sae=~y_syna+y_eblupa+y_spaznr+y_eblupb+y_synb+y_logis,
       v.dir=~mse_d,
       mse.sae=~mse_sa+mse_eba2+mse_spaznr+mse_ebb+mse_sb+mse_log)

SAEval.coverage