model function

Choose between the three models

This function chooses one of the three models.

model(d, t, pp, Xk, X, Z, initial, y, M, MM, mod)

Arguments

• d: number of areas.
• t: number of time periods.
• pp: vector with the number of the auxiliary variables per category.
• Xk: list of matrices with the auxiliary variables per category obtained from data.mme. The dimension of the list is the number of domains.
• X: list of matrices with the auxiliary variables obtained from data.mme. The dimension of the list is the number of categories of the response variable minus one.
• Z: design matrix of random effects obtained from data.mme.
• initial: output of the function initial.values.
• y: matrix with the response variable obtained from data.mme. The rows are the domains and the columns are the categories of the response variable.
• M: vector with the area sample sizes.
• MM: vector with the population sample sizes.
• mod: a number specifying the type of models: 1=multinomial mixed model with one independent random effect in each category of the response variable (Model 1), 2=multinomial mixed model with two independent random effects in each category of the response variable: one domain random effect and another independent time and domain random effect (Model 2) and 3= multinomial model with two independent random effects in each category of the response variable: one domain random effect and another correlated time and domain random effect (Model 3).

Returns

the output of the function modelfit1, modelfit2 or modelfit3.

Examples

k=3 #number of categories of the response variable
pp=c(1,1) #vector with the number of auxiliary variables in each category
data(simdata)  #data
mod=1 #Model 1
datar=data.mme(simdata,k,pp,mod)
result=model(datar$d,datar$t,pp,datar$Xk,datar$X,datar$Z,datar$initial,datar$y[,1:(k-1)],
datar$n,datar$N, mod)

References

Lopez-Vizcaino, ME, Lombardia, MJ and Morales, D (2013). Multinomial-based small area estimation of labour force indicators. Statistical Modelling, 13 ,153-178.

Lopez-Vizcaino, ME, Lombardia, MJ and Morales, D (2013). Small area estimation of labour force indicator under a multinomial mixed model with correlated time and area effects. Submitted for review.