smoothUnit: Smooth via basic unit level model

In survey: Analysis of Complex Survey Samples

Smooth via basic unit level model

Description

Generates small area estimates by smoothing direct estimates using a basic unit level model. This model assumes sampling is ignorable (no selection bias). It's a Bayesian linear (family="gaussian") or generalised linear (family="binomial") mixed model for the unit-level data with individual-level covariates and area-level random effects.

Usage

svysmoothUnit(
  formula,
  domain,
  design,
  family = c("gaussian", "binomial"),
  X.pop = NULL,
  adj.mat = NULL,
  domain.size = NULL,
  pc.u = 1,
  pc.alpha = 0.01,
  pc.u.phi = 0.5,
  pc.alpha.phi = 2/3,
  level = 0.95,
  n.sample = 250,
  return.samples = FALSE,
  X.pop.weights = NULL,...
)

Arguments

formula	An object of class 'formula' describing the model to be fitted.
domain	One-sided formula specifying factors containing domain labels
design	An object of class "survey.design" containing the data for the model
family	of the response variable, currently supports 'binomial' (default with logit link function) or 'gaussian'.
X.pop	Data frame of population unit-level covariates. One of the column name needs to match the domain specified, in order to be linked to the data input. Currently only supporting time-invariant covariates.
adj.mat	Adjacency matrix with rownames matching the domain labels. If set to NULL, the IID spatial effect will be used.
domain.size	Data frame of domain sizes. One of the column names needs to match the name of the domain variable, in order to be linked to the data input and there must be a column names 'size' containing domain sizes. The default option is no transformation, but logit and log are implemented.
pc.u	Hyperparameter U for the PC prior on precisions. See the INLA documentation for more details on the parameterization.
pc.alpha	Hyperparameter alpha for the PC prior on precisions.
pc.u.phi	Hyperparameter U for the PC prior on the mixture probability phi in BYM2 model.
pc.alpha.phi	Hyperparameter alpha for the PC prior on the mixture probability phi in BYM2 model.
level	The specified level for the posterior credible intervals
n.sample	Number of draws from posterior used to compute summaries
return.samples	If TRUE, return matrix of posterior samples of area level quantities
X.pop.weights	Optional vector of weights to use when aggregating unit level predictions
...	for future expansion

Value

A svysae object

References

Battese, G. E., Harter, R. M., & Fuller, W. A. (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.

See Also

The survey-sae vignette

survey documentation built on Aug. 28, 2024, 3 a.m.