Description Usage Arguments Details Value Methods See Also Examples
Calculate small area predictions and their variances.
1 2 3 4 5 6 7 |
object |
a model object for which prediction is desired. |
... |
Arguments to be passed to methods. |
version |
set to "1.0.0" or set options(maSAE_version = "1.0.0") to use the functions from maSAE 1.0.0. See NEWS.md for 2.0.0. |
use_lm |
Rather for internal use, stick with the default. |
Based on the structure of the saeObj
given, predict
decides,
which
predictor to use:
If a smallAreaMeans-data.frame covering all fixed effects is given, the
exhaustive
estimator \hat{\tilde{y}}_{g, synth} is calculated.
If a smallAreaMeans-data.frame not covering all fixed effects is given, the
partially
exhaustive
estimator \hat{\tilde{y}}_{g, greg} is calculated.
If no smallAreaMeans-data.frame but s1 is given, the three-phase
estimator \hat{\tilde{y}}_{g, g3reg} is calculated.
If neither smallAreaMeans nor s1 are given, the non-exhaustive
estimator \hat{\tilde{y}}_{g, psynth} is calculated.
If a clustering variable is given, the cluster sampling design equivalents
of the
above estimators are used.
If version
is not set to "1.0.0", the (pseudo) small and synthetic
estimations and their variances are also calculated (see
vignette("A_Taxonomy_of_Estimators", package = "maSAE")
)
A data frame containing predictions and variances for each small area, see Details above.
signature(object = saeObj)
Calculate predictions and variances according to the auxiliary information given, see Details above.
signature(object = sadObj)
Calculate design-based predictions and variances.
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 | ## ## design-based estimation
## load data
data("s2", package = "maSAE")
## create object
saeO <- maSAE::saObj(data = s2, f = y ~ NULL | g)
## design-based estimation for all small areas given by g
maSAE::predict(saeO)
## ## model-assisted estimation
## load data
data("s1", "s2", package = "maSAE")
str(s1)
s12 <- maSAE::bind_data(s1, s2)
## create object
saeO <- maSAE::saObj(data = s12, f = y ~ x1 + x2 + x3 | g, s2 = "phase2")
## small area estimation
maSAE::predict(saeO)
|
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