mseFH.ns.mprop {sae.prop}

Parametric Bootstrap Mean Squared Error of EBLUPs based on a Multivariate Fay Herriot model with Additive Logistic Transformation for Non-Sampled Data

Description

This function gives the MSE of transformed EBLUP based on a multivariate Fay-Herriot model. For sampled domains, MSE is estimated using modified parametric bootstrap approach proposed by Gonzalez-Manteiga. For non-sampled domains, MSE is estimated using modified approach by using average sampling variance of sampled domain in each cluster.

Usage

mseFH.ns.mprop(
  formula,
  vardir,
  MAXITER = 100,
  PRECISION = 1e-04,
  cluster = "auto",
  B = 400,
  data
)

Arguments

formula

an object of class formula that describe the fitted model.

vardir

sampling variances of direct estimations. If data is defined, it is a vector containing names of sampling variance columns. If data is not defined, it should be a data frame of sampling variances of direct estimators. The order is var1, var2, \dots, var(q-1), cov12, \dots, cov1(q-1), cov23, \dots, cov(q-2)(q-1).

MAXITER

maximum number of iterations allowed in the Fisher-scoring algorithm, Default: 100.

PRECISION

convergence tolerance limit for the Fisher-scoring algorithm, Default: 1e-4.

cluster

Default: "auto". If cluster = "auto", then the clustering will be performed by the function by finding optimal number of cluster. If cluster is a vector containing numbers of cluster for each category, then clustering will be performed based on the chosen number of cluster. If cluster is a data frame or matrix containing cluster information, then the vector will be used directly to find average of random effects. Clustering is performed with k-medoids algorithms using the function pamk. If "auto" is chosen, krange are set to 2:(nrow(data)-1).

B

number of Bootstrap iterations in calculating MSE, Default: 400.

data

optional data frame containing the variables named in formula and vardir.

Value

The function returns a list with the following objects:

est

a data frame containing values of the estimators for each domains.

• PC : transformed EBLUP estimators using inverse alr for each categoory.

• status : status of corresponding domain, whether sampled or non-sampled.

fit

a list containing the following objects (model is fitted using REML):

• convergence : a logical value equal to TRUE if Fisher-scoring algorithm converges in less than MAXITER iterations.

• iterations : number of iterations performed by the Fisher-scoring algorithm.

• estcoef : a data frame that contains the estimated model coefficients, standard errors, t-statistics, and p-values of each coefficient.

• refvar : estimated covariance matrix of random effects.

• cluster : cluster of each category.

• cluster.information : a list containing data frames with average random effects of sampled domain in each cluster.

components

a list containing the following objects:

• random.effects : data frame containing estimated random effect values of the fitted model for each category and their status whether sampled or non-sampled.

• residuals : data frame containing residuals of the fitted model for each category and their status whether sampled or non-sampled.

mse

data frame containing estimated MSE of the estimators.

• PC : estimated MSE of plugin (PC) estimators for each category.

• status : status of domain, whether sampled or non-sampled.

Examples

## Not run: 
## Load dataset
data(datasaem.ns)

## If data is defined
Fo = list(Y1 ~ X1,
          Y2 ~ X2,
          Y3 ~ X3)
vardir = c("v1", "v2", "v3", "v12", "v13", "v23")
MSE.ns <- mseFH.ns.mprop(Fo, vardir, data = datasaem.ns, B = 10)

## If data is undefined (and option for cluster arguments)
Fo = list(datasaem.ns$Y1 ~ datasaem.ns$X1,
          datasaem.ns$Y2 ~ datasaem.ns$X2,
          datasaem.ns$Y3 ~ datasaem.ns$X3)
vardir = datasaem.ns[, c("v1", "v2", "v3", "v12", "v13", "v23")]

### "auto"
MSE.ns1 <- mseFH.ns.mprop(Fo, vardir, cluster = "auto", B = 10)

### number of clusters
MSE.ns2 <- mseFH.ns.mprop(Fo, vardir, cluster = c(3, 2, 2), B = 10)

### data frame or matrix containing cluster for each domain
MSE.ns3 <- mseFH.ns.mprop(Fo, vardir, cluster = datasaem.ns[, c("c1", "c2", "c3")], B = 10)

## See the estimators
MSE.ns$mse

## NOTE:
## B = 10 is just for examples.
## Please choose a proper number for Bootstrap iterations in real calculation.

## End(Not run)