saery (version 1.0)

fit.saery: The function fit.saery is used to fit the correct model for three options.

Description

The function fit.saery fits the model for three options. This function and eblup.saery use the REML method to fit the model.

Usage

fit.saery(X, ydi, D, md, sigma2edi, model = c("INDEP", "AR1", "MA1"), conf.level = 0.95)
fit.saery.AR1(X, ydi, D, md, sigma2edi, conf.level)
fit.saery.MA1(X, ydi, D, md, sigma2edi, conf.level)
fit.saery.indep(X, ydi, D, md, sigma2edi, conf.level)

Arguments

X

a numeric vector or data frame containing the aggregated (population) values of p auxiliary variables. A ones columns must be agregated to calculate the intercept parameter

ydi

a numeric vector with the direct estimator of the indicator of interest for area (domain)

D

a numeric vector with the number of areas (domain) of the data

md

a numeric vector with the number of periods (subdomains) for each area of the data

sigma2edi

a numeric vector with the known variance of the error term

model

Three diferents types of model must be fit. For an indepent model INDEP or an abbreviated version of it must be typed. An AR1 model can be fit using AR1 or an abbreviated version. For MA1 model, use MA1 or an abbreviated version. This function and fit.saery use the REML method to fit the model.

conf.level

a value under 1 for the confidence level for the confidence intervals returned by the function

Value

A list with the fitted parameters of the model are returned. Caonfidence intervals, p-values, the Fisher Scoring matrix and the number of iterations of the model are also returned.

References

Rao, J.N.K., Yu, M., 1994. Small area estimation by combining time series and cross sectional data. Canadian Journal of Statistics 22, 511-528.

Esteban, M.D., Morales, D., Perez, A., Santamaria, L., 2012. Small area estimation of poverty proportions under area-level time models. Computational Statistics and Data Analysis, 56 (10), pp. 2840-2855.

See Also

eblup.saery, ~~~

Examples

sigma2edi <- datos[,6]
X <- as.matrix(datos[,5])
ydi <- datos[,3]
D <- length(unique(datos[,1]))
md <- rep(length(unique(datos[,2])), D)

output.fit.ar1 <- fit.saery(X, ydi, D, md, sigma2edi, "AR", 0.9)
output.fit.ar1

output.fit.ma1 <- fit.saery(X, ydi, D, md, sigma2edi, "MA", 0.9)
output.fit.ma1

output.fit.indep <- fit.saery(X, ydi, D, md, sigma2edi, "indep", 0.9)
output.fit.indep