ci function

Standard deviation and p-values of the estimated model parameters

This function calculates the standard deviations and the p-values of the estimated model parameters. The standard deviations are obtained from the asymptotic Fisher information matrix in the fitting algorithms modelfit1, modelfit2, modelfit3, depending of the current multinomial mixed model.

ci(a, F)

Arguments

• a: vector with the estimated parameters obtained from modelfit1, modelfit2 or modelfit3.
• F: inverse of the Fisher Information Matrix obtained from modelfit1, modelfit2 or modelfit3.

Returns

A list containing the following components. - Std.dev: vector with the standard deviations of the parameters. The parameters are sorted per category.

• p.value: vector with the p-values of the parameters for testing H0:a=0.

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  #Type of model
datar=data.mme(simdata,k,pp,mod)

#Model fit
result=modelfit1(pp,datar$Xk,datar$X,datar$Z,datar$initial,datar$y[,1:(k-1)],
       datar$n,datar$N)
beta=result[[8]][,1] #fixed effects
Fisher=result[[3]] #Fisher information matrix

##Standard deviation and p-values
res=ci(beta,Fisher)

References

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

See Also

modelfit1, modelfit2, modelfit3.