hbsae (version 1.2)

fSurvReg: Compute small area estimates based on the survey regression estimator.

Description

This function computes survey regression estimates as a special case of unit-level model small area estimates with a (relatively) very large value for the between-area variance but without including area effects in the model fit. The model assumes a single overall variance parameter, so that the resulting estimated variances are not area-specific but smoothed. Varying inclusion probabilities may be taken into account by including them in the model, e.g. as an additional covariate, and/or as model variance structure by specifying arguments v and vpop, see fSAE.Unit. The resulting estimates may be used as input estimates for area-level model small area estimation.

Usage

fSurvReg(y, X, area, Narea, Xpop, removeEmpty = TRUE, ...)

Arguments

y

response vector of length n.

X

n x p model matrix.

area

n-vector of area codes, typically a factor variable with m levels, where m is the number of in-sample areas.

Narea

M-vector of area population sizes.

Xpop

M x p matrix of population means.

removeEmpty

whether out-of-sample areas should be removed from the results. If FALSE these areas are retained in the vectors of estimates, but they will have (relatively) very large standard errors.

...

optional arguments v and vpop passed to fSAE.Unit.

Value

An object of class sae containing the survey regression small area estimates and their estimated variances.

References

G.E. Battese, R.M. Harter and W.A. Fuller (1988). An Error-Components Model for Prediction of County Crop Areas Using Survey and Satellite Data. Journal of the American Statistical Association, 83(401), 28-36.

J.N.K. Rao and I. Molina (2015). Small Area Estimation. Wiley.

See Also

sae-class

Examples

# NOT RUN {
d <- generateFakeData()

# generate design matrix, variable of interest, area indicator and population data
dat <- fSAE(y0 ~ x + area2, data=d$sam, area="area", popdata=d$Xpop,
            type="data")

sae <- fSurvReg(dat$y, dat$X, dat$area, dat$Narea, dat$PopMeans)
EST(sae)  # estimates
RMSE(sae)  # standard errors
# }