Description Usage Arguments Details Value References See Also Examples
This function calculates, for selected domains, EBLUPs of domain means based on the nested error linear regression model of Battese, Harter and Fuller (1988).
1 
formula 
an object of class 
dom 

selectdom 

meanxpop 

popnsize 

method 
a character string. If 
data 
optional data frame containing the variables named in 
A typical model has the form response ~ terms where response is the (numeric) response vector and terms is a series of terms which specifies a linear predictor for response. A terms specification of the form first + second indicates all the terms in first together with all the terms in second with duplicates removed.
A formula has an implied intercept term. To remove this use either y ~ x  1 or y ~ 0 + x. See formula
for more details of allowed formulae.
The function returns a list with the following objects:
eblup 
data frame with number of rows equal to number of selected domains ( 
fit 
a list containing the following objects:

Cases with NA values in formula
or dom
are ignored.
 Battese, G.E., Harter, R.M. and Fuller, W.A. (1988). An ErrorComponents Model for Prediction of County Crop Areas Using Survey and Satellite Data, Journal of the American Statistical Association 83, 2836.
 Rao, J.N.K. (2003). Small Area Estimation. New York: John Wiley and Sons.
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27  # Load data set for segments (units within domains)
data(cornsoybean)
# Load data set for counties
data(cornsoybeanmeans)
attach(cornsoybeanmeans)
# Construct data frame with county means of auxiliary variables for
# domains. First column must include the county code
Xmean < data.frame(CountyIndex, MeanCornPixPerSeg, MeanSoyBeansPixPerSeg)
Popn < data.frame(CountyIndex, PopnSegments)
# Compute EBLUPs of county means of corn crop areas for all counties
resultCorn < eblupBHF(CornHec ~ CornPix + SoyBeansPix, dom=County,
meanxpop=Xmean, popnsize=Popn, data=cornsoybean)
resultCorn$eblup
# Compute EBLUPs of county means of soy beans crop areas for
# a subset of counties using ML method
domains < c(10,1,5)
resultBean < eblupBHF(SoyBeansHec ~ CornPix + SoyBeansPix, dom=County,
selectdom=domains, meanxpop=Xmean, popnsize=Popn,
method="ML", data=cornsoybean)
resultBean$eblup
resultBean$fit
detach(cornsoybeanmeans)

Loading required package: MASS
Loading required package: lme4
Loading required package: Matrix
domain eblup sampsize
1 1 122.5825 1
2 2 123.5274 1
3 3 113.0343 1
4 4 114.9901 2
5 5 137.2660 3
6 6 108.9807 3
7 7 116.4839 3
8 8 122.7711 3
9 9 111.5648 4
10 10 124.1565 5
11 11 112.4626 5
12 12 131.2515 6
domain eblup sampsize
1 10 100.61124 5
2 1 78.63690 1
3 5 66.26077 3
$summary
Linear mixed model fit by maximum likelihood ['lmerMod']
Formula: ys ~ 1 + Xs + (1  dom)
AIC BIC logLik deviance df.resid
323.2 331.2 156.6 313.2 32
Scaled residuals:
Min 1Q Median 3Q Max
1.9871 0.5487 0.1716 0.4525 1.8864
Random effects:
Groups Name Variance Std.Dev.
dom (Intercept) 219.3 14.81
Residual 170.3 13.05
Number of obs: 37, groups: dom, 12
Fixed effects:
Estimate Std. Error t value
Xs(Intercept) 16.34566 25.46383 0.642
XsCornPix 0.02807 0.05348 0.525
XsSoyBeansPix 0.49679 0.05537 8.972
Correlation of Fixed Effects:
Xs(In) XsCrnP
XsCornPix 0.935
XsSoyBensPx 0.878 0.718
$fixed
Xs(Intercept) XsCornPix XsSoyBeansPix
16.34565854 0.02806513 0.49678511
$random
(Intercept)
1 7.5341978
2 4.6221630
3 6.3898804
4 20.0120622
5 19.7224381
6 0.1073779
7 13.8480569
8 10.2146579
9 3.9717548
10 9.4323917
11 25.2797012
12 5.8740155
$errorvar
[1] 170.2855
$refvar
[1] 219.3221
$loglike
[1] 156.5848
$residuals
[1] 5.8496848 3.5887294 4.9612165 16.9877875 1.4500676 1.3770236
[7] 2.9223939 13.7674804 7.4177099 25.9298102 18.5954704 24.6159854
[13] 2.2390235 11.6250849 7.1633492 21.3097437 6.2155529 6.8691125
[19] 5.9048206 7.1602978 8.6973762 2.8897097 9.5627347 22.4392362
[25] 4.8193887 3.6233464 7.0495500 18.1539675 0.8801249 4.8172079
[31] 12.4602739 11.5223532 1.0827759 2.0509344 4.2551656 10.7593676
[37] 12.4977069
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