mspe_MS_LOGISTIC_SUMCA {SumcaVer1}

Model selection MSPE estimation in mixed logistic model using SUMCA method. Calculate the model selection mspe of mixed logistic model using SUMCA method.

Description

Model selection MSPE estimation in mixed logistic model using SUMCA method. Calculate the model selection mspe of mixed logistic model using SUMCA method.

Usage

mspe_MS_LOGISTIC_SUMCA(m, p, ni, X, beta, A, K, R)

Arguments

m

number of small areas

p

number of complete model parameters

ni

sample size of each small area

X

covariates for the complete model

beta

regression coefficients of the complete model

A

variance of area-specific random effects

K

number of Monte Carlo for the SUMCA method

R

number of simulation runs

Value

Par1: return estimation of model parameters of the complete model

Par2: return estimation of model parameters of the reduced model

MSPE: return empirical MSPE of small area predictor

mspe.Sumca: return mspe of small area predictor using the SUMCA method

RB.SUMCA: return relative bias (RB) of mspe of small area predictor using the SUMCA method

BIC: return BIC of the complete and reduced models

Examples

mspe_MS_LOGISTIC_SUMCA(20,3,2,matrix(runif(60,0,1),nrow=20,byrow=TRUE),c(1,1,1),10,5,5)