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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