Nothing
info.poisson.design <- function(model="linear", theta, xpoints,
natx=1, group.size=1) {
#-----------------------------------------------------------------------
# Returns the information matrix for a poisson design
#
# model: One of {"linear", "quadratic"} Only enough to ensure a unique
# match need be supplied.
#
# theta: Matrix of parameters of the linear part of the model.
# Each row represents a group.
#
# xpoints: Matrix of covariate values for each group.
# Each row represents a group.
#
# natx: Matrix of number of observations at xpoints for each group.
# Each row represents a group.
# At covariate value xpoint[i,j] there are natx[i,j] observations.
#
# group.size: The relative number of observations in each group
#
#
# Returns: The the information matrix for one observation for this design.
# The observation is assumed to be spread over xpoints in proportion
# to natx.
#
#-----------------------------------------------------------------------
if (is.vector(theta)) theta <- t(as.matrix(theta))
dimt <- dim(theta)
ngroups <- dimt[1]
if (is.vector(xpoints)) {
xpoints <- matrix(xpoints,ngroups,length(xpoints), byrow=TRUE)
dimp <- dim(xpoints)
} else {
dimp <- dim(xpoints)
if (dimp[1] != ngroups)
stop("Number of rows of xpoints and theta must match")
}
if (is.vector(natx)) {
if (length(natx) == 1)
natx <- matrix(natx,ngroups,dimp[2],byrow=TRUE) else {
if (length(natx) != dimp[2] )
stop ("length of natx must match number of xpoints")
natx <- matrix(natx,ngroups,dimp[2],byrow=TRUE)
}
dimn <- dim(natx)
} else {
dimn <- dim(natx)
if (dimn[1] != dimp[1] || dimn[2] != dimp[2])
stop("xpoints and natx must have the same dimensions")
}
lngrpsz <- length(group.size)
if (lngrpsz == 1) group.size <- rep(group.size,ngroups) else
if (ngroups != lngrpsz)
stop("\nNumber of rows of theta and length of group.size must match")
info <- vector("list",dimp[1])
for (j in 1:dimp[1]) {
info[[j]] <- 0
for (i in 1:length(xpoints[j,]))
if(natx[j, i] != 0) info[[j]] <- info[[j]] + natx[j,i] *
info.poisson.one(model,theta[j,],xpoints[j,i])
info[[j]] <- info[[j]] * group.size[j]
}
info <- k.blocks.info(info)
return(info/sum(group.size * natx))
}
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