mspe_MS_LOGISTIC_SUMCA {SumcaVer1} | R Documentation |
Model selection MSPE estimation in mixed logistic model using SUMCA method. Calculate the model selection mspe of mixed logistic model using SUMCA method.
mspe_MS_LOGISTIC_SUMCA(m, p, ni, X, beta, A, K, R)
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 |
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
mspe_MS_LOGISTIC_SUMCA(20,3,2,matrix(runif(60,0,1),nrow=20,byrow=TRUE),c(1,1,1),10,5,5)