Nothing
asypow.noncent <- function(theta.ha, info.mat, constraints, nobs.ell=1,
get.ho=TRUE) {
#----------------------------------------------------------------------
#
# theta.ha: Array of parameter values under the alternative hypothesis.
#
# info.mat: The information matrix, the second derivate matrix of the
# expected log likelihood under the alternative hypothesis.
# The negative of the hessian matrix.
#
# constraints: the constraints which set the null hypothesis from the
# alternative hypothesis. They are in matrix form.
# CONSTRAINT[,1] is 1 for setting parameter to a value
# 2 for equality of two parameters
# CONSTRAINT[,2] is case on CONSTRAINT[,1]
# (1) Index of parameter to set to value
# (2) Index of one of two parameters to be set equal
# CONSTRAINT[,3] is case on CONSTRAINT[,1]
# (1) Value to which parameter is set
# (2) Index of other of two parameters to be set equal
#
# nobs.ell: The number of observations used in computing the information
# matrix. That is, info.mat is that for nobs.ell observations.
#
# get.ho: If TRUE, estimates of the parameter values under the null
# hypothesis are calculated and returned, otherwise not.
#
#
# RETURNS a list of:
#
# w: noncentrality parameter for 1 observation
#
# df: degrees of freedom of the test
#
# theta.ho: estimates of the parameter values under the null hypothesis
#
#----------------------------------------------------------------------
if (is.matrix(theta.ha)) theta.ha <- as.vector(t(theta.ha))
p <- length(theta.ha)
if (length(info.mat)==1) info.mat <- matrix(info.mat, nrow=1, ncol=1)
dimi <- dim(info.mat)
if (dimi[1] != dimi[2]) stop("info.mat is not a square matrix")
if (p != dimi[1])
stop("length of theta.ha must match dimension of info.mat")
Aans <- asypow.construct.a(constraints,length(theta.ha))
Ainv <- solve(Aans$a)
hess <- t(Ainv) %*% info.mat %*% Ainv
constraints <- Aans$constraints
s <- dim(constraints)[1]
r <- p - s
hess.phiphi <- hess[1:s, 1:s]
if (r > 0) {
hess.philam <- hess[1:s, (s+1):p]
hess.lamphi <- hess[(s+1):p, 1:s]
hess.lamlam <- hess[(s+1):p, (s+1):p]
w <- hess.phiphi -
hess.philam %*% solve(hess.lamlam) %*% hess.lamphi
} else w <- hess.phiphi
phi.ha.tran <- transformPhi(theta.ha, constraints)
phi.diff <- Aans$phi.ho - phi.ha.tran
w <- t(phi.diff) %*% w %*% phi.diff
# now convert to the w statistic for one observation
w <- w / nobs.ell
df <- s
if(r > 0 & get.ho) {
lam.ho.tran <- theta.ha[-Aans$ix.con] +
solve(hess.lamlam) %*% hess.lamphi %*% phi.diff
theta.ho <- asypow.theta.ho(lam.ho.tran, constraints)
return(list(w=w, df=df, theta.ho=theta.ho))
} else return(list(w=w, df=df))
}
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