fSAE.Area | R Documentation |
This function returns small area estimates based on the basic area-level model, also known as the Fay-Herriot model.
It calls fSAE.Unit
to carry out the computations.
fSAE.Area(est.init, var.init, X, x, ...)
est.init |
m-vector of initial estimates, where m is the number of in-sample areas. |
var.init |
m-vector of corresponding variance estimates. |
X |
M x p matrix of area-level covariates (typically population means), where M is the number of areas for which estimates are computed.
If missing, a column vector of ones of the same length as |
x |
an optional m x p matrix with auxiliary area-level covariates to be used for fitting the model,
where the rows correspond to the components of |
... |
additional arguments passed to |
An object of class sae
containing the small area estimates and MSEs, the model fit, and model selection measures.
R.E. Fay and R.A. Herriot (1979). Estimates of Income for Small Places: An Application of James-Stein Procedures to Census Data. Journal of the American Statistical Association 74(366), 269-277.
J.N.K. Rao and I. Molina (2015). Small Area Estimation. Wiley.
sae-class
d <- generateFakeData() # first compute input estimates without "borrowing strength" over areas sae0 <- fSAE(y0 ~ x + area2, data=d$sam, area="area", popdata=d$Xpop, type="direct", keep.data=TRUE) # compute small area estimates based on the basic area-level model # using the above survey regression estimates as input sae <- fSAE.Area(EST(sae0), MSE(sae0), X=sae0$Xp) EST(sae) # estimates RMSE(sae) # standard errors
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