fSAE: Fit a linear model with random area effects and compute small...

In hbsae: Hierarchical Bayesian Small Area Estimation

Fit a linear model with random area effects and compute small area estimates.

Description

This function prepares the (unit-level) input data and calls one of the lower level functions fSurvReg, fSAE.Area or fSAE.Unit to compute survey regression, area-level model or unit-level model small area estimates. Area-level model estimates are computed by first computing survey regression estimates and using these as input for fSAE.Area.

Usage

fSAE(
  formula,
  data,
  area = NULL,
  popdata = NULL,
  type = "unit",
  model.direct = NULL,
  formula.area = NULL,
  contrasts.arg = NULL,
  remove.redundant = TRUE,
  redundancy.tol = 1e-07,
  sparse = FALSE,
  ...
)

Arguments

formula	model formula, indicating response variable and covariates.
data	unit-level data frame containing all variables used in formula, area and formula.area arguments. These variables should not contain missing values.
area	name of area indicator variable in data; if NULL, no random effects are used in the model.
popdata	data frame or matrix containing area population totals for all covariates. The rows should correspond to areas for which estimates are required. Column names should include those produced by model.matrix(formula, data, contrasts.arg), up to permutations of the names in interactions. A column named '(Intercept)' is required and should contain the area population sizes. If popdata is NULL, only the model fit is returned.
type	type of small area estimates: "direct" for survey regression, "area" for area-level model, "unit" for unit-level model estimates. If type is "data" then only the data including the model matrix and population means are returned.
model.direct	if type="area", this argument can be used to specify by means of a formula the covariates to use for the computation of the initial survey regression estimates. If unspecified, the covariates specified by formula are used both at the unit level (for the initial estimates) and at the area level (for the area-level model estimates).
formula.area	if type="unit", this is an optional formula specifying covariates that should be used at the area level. These covariates should be available in popdata.
contrasts.arg	list for specification of contrasts for factor variables. Passed to model.matrix.
remove.redundant	if TRUE redundant columns in the design matrix are removed. A warning is issued if the same redundancy does not show also in the corresponding population totals. In the case of the area-level model there may still be redundancy at the area level.
redundancy.tol	tolerance for detecting linear dependencies among the columns of the design matrix. Also used as tolerance in the check whether the design matrix redundancy is shared by the population totals.
sparse	if TRUE sparse.model.matrix (package Matrix) is used to compute the covariate design matrix. This can be efficient for large datasets and a model containing categorical variables with many categories.
...	additional arguments passed to fSAE.Unit or fSurvReg.

Value

An object of class sae containing the small area estimates, their MSEs, and the model fit. If type is "data" a list containing the model matrix, response vector, area indicator, area population sizes and matrix of population means is returned.

See Also

sae-class

Examples

d <- generateFakeData()

# model fitting only
(fit <- fSAE(y0 ~ x + area2, data=d$sam, area="area"))

# model fitting and small area estimation, unit-level model
saeHB <- fSAE(y0 ~ x + area2, data=d$sam, area="area", popdata=d$Xpop,
              silent=TRUE)
saeHB  # print a summary
EST(saeHB)  # small area estimates
RMSE(saeHB)  # error estimates
str(saeHB)
plot(saeHB, list(est=d$mY0), CI=2)  # compare to true population means

# unit-level model with REML model-fit instead of Bayesian approach
saeREML <- fSAE(y0 ~ x + area2, data=d$sam, area="area", popdata=d$Xpop,
                method="REML", silent=TRUE)
plot(saeHB, saeREML)  # compare

# basic area-level model
saeA <- fSAE(y0 ~ x + area2, data=d$sam, area="area", popdata=d$Xpop,
             type="area")
plot(saeHB, saeA)

# SAE estimates based on a linear unit-level model without area effects
saeL <- fSAE(y0 ~ x + area2, data=d$sam, area="area", popdata=d$Xpop,
             method="synthetic")
plot(saeHB, saeL)

# model-based estimation of overall population mean without area effects
est.global <- fSAE(y0 ~ x + area2, data=d$sam, area=NULL,
                   popdata=colSums(d$Xpop), method="synthetic")
EST(est.global); RMSE(est.global)

# no model fitting or estimation, but return design matrix, variable of interest,
#   area indicator, area population sizes and matrix of population means
dat <- fSAE(y0 ~ x + area2, data=d$sam, area="area", popdata=d$Xpop,
            type="data")
str(dat)

hbsae documentation built on March 18, 2022, 6:34 p.m.