Dear all,
I am working on modifying the R working matrix to commodate some other
correlations that not included in the package. I am having problem to locate
where the R matrix are defined for regular matrices, i.e. independence,
exchangeable, AR and unstructure. it might have something within
.C("Cgee",but don't understand it well enough to know. Can you
anyone
help?
/*gee source code*/
function (formula = formula(data), id = id, data = parent.frame(),
subset, na.action, R = NULL, b = NULL, tol = 0.001, maxiter = 25,
family = gaussian, corstr = "independence", Mv = 1, silent = TRUE,
contrasts = NULL, scale.fix = FALSE, scale.value = 1, v4.4compat FALSE)
{
message("Beginning Cgee S-function, @(#) geeformula.q 4.13
98/01/27")
call <- match.call()
m <- match.call(expand = FALSE)
m$R <- m$b <- m$tol <- m$maxiter <- m$link <- m$varfun <-
m$corstr <-
m$Mv <- m$silent <- m$contrasts <- m$family <- m$scale.fix <-
m$scale.value
<- m$v4.4compat <- NULL
if (is.null(m$id))
m$id <- as.name("id")
if (!is.null(m$na.action) && m$na.action != "na.omit") {
warning("Only 'na.omit' is implemented for gee\ncontinuing
with
'na.action=na.omit'")
m$na.action <- as.name("na.omit")
}
m[[1]] <- as.name("model.frame")
m <- eval(m, parent.frame())
Terms <- attr(m, "terms")
y <- as.matrix(model.extract(m, "response"))
x <- model.matrix(Terms, m, contrasts)
Q <- qr(x)
if (Q$rank < ncol(x))
stop("rank-deficient model matrix")
N <- rep(1, length(y))
if (dim(y)[2] == 2) {
N <- as.vector(y %*% c(1, 1))
y <- y[, 1]
}
else {
if (dim(y)[2] > 2)
stop("Only binomial response matrices (2 columns)")
}
offset <- model.extract(m, offset)
id <- model.extract(m, id)
if (is.null(id)) {
stop("Id variable not found")
}
nobs <- nrow(x)
p <- ncol(x)
xnames <- dimnames(x)[[2]]
if (is.null(xnames)) {
xnames <- paste("x", 1:p, sep = "")
dimnames(x) <- list(NULL, xnames)
}
if (is.character(family))
family <- get(family)
if (is.function(family))
family <- family()
if (!is.null(b)) {
beta <- matrix(as.double(b), ncol = 1)
if (nrow(beta) != p) {
stop("Dim beta != ncol(x)")
}
message("user's initial regression estimate")
print(beta)
}
else {
message("running glm to get initial regression estimate")
mm <- match.call(expand = FALSE)
mm$R <- mm$b <- mm$tol <- mm$maxiter <- mm$link <-
mm$varfun <-
mm$corstr <- mm$Mv <- mm$silent <- mm$contrasts <- mm$scale.fix
<-
mm$scale.value <- mm$id <- NULL
mm[[1]] <- as.name("glm")
beta <- eval(mm, parent.frame())$coef
print(beta)
beta <- as.numeric(beta)
}
if (length(id) != length(y))
stop("Id and y not same length")
maxclsz <- as.integer(max(unlist(lapply(split(id, id),
"length"))))
maxiter <- as.integer(maxiter)
silent <- as.integer(silent)
if (length(offset) <= 1)
offset <- rep(0, length(y))
if (length(offset) != length(y))
stop("offset and y not same length")
offset <- as.double(offset)
if (!is.null(R)) {
Rr <- nrow(R)
if (Rr != ncol(R))
stop("R is not square!")
if (Rr < maxclsz)
stop("R not big enough to accommodate some clusters.")
}
else {
R <- matrix(as.double(rep(0, maxclsz * maxclsz)), nrow = maxclsz)
}
links <- c("identity", "log", "logit",
"inverse", "probit",
"cloglog")
fams <- c("gaussian", "poisson",
"binomial", "Gamma", "quasi")
varfuns <- c("constant", "mu", "mu(1-mu)",
"mu^2")
corstrs <- c("independence", "fixed",
"stat_M_dep", "non_stat_M_dep",
"exchangeable", "AR-M", "unstructured")
linkv <- as.integer(match(c(family$link), links, -1))
famv <- match(family$family, fams, -1)
if (famv < 1)
stop("unknown family")
if (famv <= 4)
varfunv <- famv
else varfunv <- match(family$varfun, varfuns, -1)
varfunv <- as.integer(varfunv)
corstrv <- as.integer(match(corstr, corstrs, -1))
if (linkv < 1)
stop("unknown link.")
if (varfunv < 1)
stop("unknown varfun.")
if (corstrv < 1)
stop("unknown corstr.")
naivvar <- matrix(rep(0, p * p), nrow = p)
robvar <- matrix(rep(0, p * p), nrow = p)
phi <- as.double(scale.value)
scale.fix <- as.integer(scale.fix)
errstate <- as.integer(1)
tol <- as.double(tol)
Mv <- as.integer(Mv)
maxcl <- as.integer(0)
if (!(is.double(x)))
x <- as.double(x)
if (!(is.double(y)))
y <- as.double(y)
if (!(is.double(id)))
id <- as.double(id)
if (!(is.double(N)))
N <- as.double(N)
modvec <- as.integer(c(linkv, varfunv, corstrv))
if (v4.4compat)
compatflag <- 1
else compatflag <- 0
z <- .C("Cgee", x, y, id, N, offset, nobs, p, modvec, Mv,
estb = beta, nv = naivvar, rv = robvar, sc = phi, wcor = R,
tol, mc = maxcl, iter = maxiter, silent, err = errstate,
scale.fix, as.integer(compatflag), PACKAGE = "gee")
if (z$err != 0)
warning("Cgee had an error (code= ", z$err, "). Results
suspect.")
if (min(eigen(z$wcor)$values) < 0) {
warning("Working correlation estimate not positive definite")
z$err <- z$err + 1000
}
fit <- list()
attr(fit, "class") <- c("gee", "glm")
fit$title <- "GEE: GENERALIZED LINEAR MODELS FOR DEPENDENT
DATA"
fit$version <- "gee S-function, version 4.13 modified 98/01/27
(1998)"
links <- c("Identity", "Logarithm",
"Logit", "Reciprocal",
"Probit", "Cloglog")
varfuns <- c("Gaussian", "Poisson",
"Binomial", "Gamma")
corstrs <- c("Independent", "Fixed", "Stationary
M-dependent",
"Non-Stationary M-dependent", "Exchangeable",
"AR-M",
"Unstructured")
fit$model <- list()
fit$model$link <- links[linkv]
fit$model$varfun <- varfuns[varfunv]
fit$model$corstr <- corstrs[corstrv]
if (!is.na(match(c(corstrv), c(3, 4, 6))))
fit$model$M <- Mv
fit$call <- call
fit$terms <- Terms
fit$formula <- as.vector(attr(Terms, "formula"))
fit$contrasts <- attr(x, "contrasts")
fit$nobs <- nobs
fit$iterations <- z$iter
fit$coefficients <- as.vector(z$estb)
fit$nas <- is.na(fit$coefficients)
names(fit$coefficients) <- xnames
eta <- as.vector(x %*% fit$coefficients)
fit$linear.predictors <- eta
mu <- as.vector(family$linkinv(eta))
fit$fitted.values <- mu
fit$residuals <- y - mu
fit$family <- family
fit$y <- as.vector(y)
fit$id <- as.vector(id)
fit$max.id <- z$mc
z$wcor <- matrix(z$wcor, ncol = maxclsz)
fit$working.correlation <- z$wcor
fit$scale <- z$sc
fit$robust.variance <- z$rv
fit$naive.variance <- z$nv
fit$xnames <- xnames
fit$error <- z$err
dimnames(fit$robust.variance) <- list(xnames, xnames)
dimnames(fit$naive.variance) <- list(xnames, xnames)
fit
}
<environment: namespace:gee>
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