Description Usage Format Details Examples

Dataset used by the multonomial mixed effects 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). This dataset contains 10 small areas and two periods. The response variable has three categories. The last is the reference category. The variables are as follows:

1 |

A data frame with 30 rows and 9 variables in columns

area: area indicator.

Time: time indicator.

sample: the sample size of each domain.

Population: the population size of each domain.

Y1: the first category of the response variable.

Y2: the second category of the response variable.

Y3: the third category of the response variable.

X1: the covariate for the first category of the response variable.

X2: the covariate for the second category of the response variable.

1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 | ```
k=3 #number of categories of the response variable
pp=c(1,1) #vector with the number of auxiliary variables in each category
data(simdata2)
mod=2 #type of model
datar=data.mme(simdata2,k,pp,mod)
##Model fit
result=model(datar$d,datar$t,pp,datar$Xk,datar$X,datar$Z,datar$initial,datar$y[,1:(k-1)],
datar$n,datar$N,mod)
##Analytic MSE
msef=msef.it(pp,datar$X,result,datar$n,datar$N)
B=1 #Bootstrap iterations
ss=12345 #SEED
set.seed(ss)
##Bootstrap parametric BIAS and MSE
mse.pboot=mseb(pp,datar$Xk,datar$X,datar$Z,datar$n,datar$N,result,B,mod)
``` |

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