phi.direct.ct {mme} | R Documentation |
Variance components for Model 3
Description
This function calculates the variance components for the multinomial mixed model with two independent random effects
for each category of the response variable: one domain random effect and another correlated time and domain random effect (Model 3). This variance components
are used in the second part of the fitting algorithm
implemented in modelfit3
. The algorithm adapts the ideas of Schall (1991) to a multivariate model. The variance components are
estimated by the REML method.
Usage
phi.direct.ct(p, sigmap, X, theta, phi1, phi2, u1, u2, rho)
Arguments
p |
vector with the number of auxiliary variables per category. |
sigmap |
a list with the model variance-covariance matrices for each domain obtained from |
X |
list of matrices with the auxiliary variables obtained from |
theta |
matrix with the estimated log-probabilites of each category in front of the reference category obtained from |
phi1 |
vector with the initial values of the first variance component obtained from |
phi2 |
vector with the initial values of the second variance component obtained from |
u1 |
matrix with the values of the first random effect obtained from |
u2 |
matrix with the values of the second random effect obtained from |
rho |
vector with the initial values of the correlation parameter obtained from |
Value
a list containing the following components.
phi1.new |
vector with the values of the variance component for the first random effect. |
phi2.new |
vector with the values of the variance component for the second random effect. |
rho.new |
vector with the correlation parameter. |
References
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.
Schall, R (1991). Estimation in generalized linear models with random effects. Biometrika, 78,719-727.
See Also
data.mme
, initial.values
,
wmatrix
, phi.mult.ct
,
prmu.time
, Fbetaf.ct
sPhikf.ct
, ci
,
modelfit3
, msef.ct
,
mseb
, omega
Examples
k=3 #number of categories of the response variable
pp=c(1,1) #vector with the number of auxiliary variables in each category
mod=3 #type of model
data(simdata3) #data
datar=data.mme(simdata3,k,pp,mod)
initial=datar$initial
mean=prmu.time(datar$n,datar$Xk,initial$beta.0,initial$u1.0,initial$u2.0)
sigmap=wmatrix(datar$n,mean$estimated.probabilities)
##The variance components
phi.ct=phi.direct.ct(pp,sigmap,datar$X,mean$eta,initial$phi1.0,
initial$phi2.0,initial$u1.0,initial$u2.0,initial$rho.0)