Description Usage Arguments Value Author(s) References See Also Examples
The function fit.saery
fits the model for three options. This function and eblup.saery
use the REML method to fit the model.
1 2 3 4 5 6 | 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)
|
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 |
conf.level |
a value under 1 for the confidence level for the confidence intervals returned by the function |
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.
Maria Dolores Esteban Lefler, Domingo Morales Gonzalez, Agustin Perez Martin
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.
eblup.saery
, ~~~
1 2 3 4 5 6 7 8 9 10 11 12 13 14 | 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
|
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