R/glm.fit2.R

utils::globalVariables("n", add = TRUE)

glm.fit2 <- 
function (x, y, weights = rep(1, nobs), start = NULL, etastart = NULL, 
    mustart = NULL, offset = rep(0, nobs), family = gaussian(), 
    control = list(), intercept = TRUE, singular.ok = TRUE) 
{
    control <- do.call("glm.control", control)
    x <- as.matrix(x)
    xnames <- dimnames(x)[[2L]]
    ynames <- if (is.matrix(y)) rownames(y) else names(y)
    conv <- FALSE
    nobs <- NROW(y)
    nvars <- ncol(x)
    EMPTY <- nvars == 0
    if (is.null(weights)) 
        weights <- rep.int(1, nobs)
    if (is.null(offset)) 
        offset <- rep.int(0, nobs)
    variance <- family$variance
    linkinv <- family$linkinv
    if (!is.function(variance) || !is.function(linkinv)) 
        stop("'family' argument seems not to be a valid family object", 
            call. = FALSE)
    dev.resids <- family$dev.resids
    aic <- family$aic
    mu.eta <- family$mu.eta
    unless.null <- function(x, if.null) if (is.null(x)) if.null else x
    valideta <- unless.null(family$valideta, function(eta) TRUE)
    validmu <- unless.null(family$validmu, function(mu) TRUE)
    if (is.null(mustart)) {
        eval(family$initialize)
    }
    else {
        mukeep <- mustart
        eval(family$initialize)
        mustart <- mukeep
    }
    if (EMPTY) {
        eta <- rep.int(0, nobs) + offset
        if (!valideta(eta)) 
            stop("invalid linear predictor values in empty model", 
                call. = FALSE)
        mu <- linkinv(eta)
        if (!validmu(mu)) 
            stop("invalid fitted means in empty model", call. = FALSE)
        dev <- sum(dev.resids(y, mu, weights))
        w <- ((weights * mu.eta(eta)^2)/variance(mu))^0.5
        residuals <- (y - mu)/mu.eta(eta)
        good <- rep(TRUE, length(residuals))
        boundary <- conv <- TRUE
        coef <- numeric()
        iter <- 0L
    }
    else {
        coefold <- NULL
        eta <- if (!is.null(etastart)) 
            etastart
        else if (!is.null(start)) 
            if (length(start) != nvars) 
                stop(gettextf("length of 'start' should equal %d and correspond to initial coefs for %s", 
                  nvars, paste(deparse(xnames), collapse = ", ")), 
                  domain = NA)
            else {
                coefold <- start
                offset + as.vector(if (NCOL(x) == 1L) x*start else x%*%start)
            }
        else family$linkfun(mustart)
        mu <- linkinv(eta)
        if (!(validmu(mu) && valideta(eta))) 
            stop("cannot find valid starting values: please specify some", 
                call. = FALSE)
        devold <- sum(dev.resids(y, mu, weights))
        boundary <- conv <- FALSE
        for (iter in 1L:control$maxit) {
            good <- weights > 0
            varmu <- variance(mu)[good]
            if (any(is.na(varmu))) 
                stop("NAs in V(mu)")
            if (any(varmu == 0)) 
                stop("0s in V(mu)")
            mu.eta.val <- mu.eta(eta)
            if (any(is.na(mu.eta.val[good]))) 
                stop("NAs in d(mu)/d(eta)")
            good <- (weights > 0) & (mu.eta.val != 0)
            if (all(!good)) {
                conv <- FALSE
                warning("no observations informative at iteration ", 
                  iter)
                break
            }
            z <- (eta - offset)[good] + (y - mu)[good]/mu.eta.val[good]
            w <- sqrt((weights[good] * mu.eta.val[good]^2)/variance(mu)[good])
            ngoodobs <- as.integer(nobs - sum(!good))
            fit <- lm.fit(x=x[good, , drop = FALSE]*w, y=z*w, singular.ok=TRUE, 
                          tol=min(1e-07, control$epsilon/1000))
            fit$coefficients[is.na(fit$coefficients)] <- 0
            if (any(!is.finite(fit$coefficients))) {
                conv <- FALSE
                warning(gettextf("non-finite coefficients at iteration %d", 
                  iter), domain = NA)
                break
            }
            if (nobs < fit$rank) 
                stop(gettextf("X matrix has rank %d, but only %d observations", 
                  fit$rank, nobs), domain = NA)
            start <- fit$coefficients
            eta <- drop(x %*% start)
            mu <- linkinv(eta <- eta + offset)
            dev <- sum(dev.resids(y, mu, weights))
            if (control$trace) 
                cat("Deviance =", dev, "Iterations -", iter, 
                  "\n")
            boundary <- FALSE
            if (!is.finite(dev)) {
                if (is.null(coefold)) 
                  stop("no valid set of coefficients has been found: please supply starting values", 
                    call. = FALSE)
                warning("step size truncated due to divergence", 
                  call. = FALSE)
                ii <- 1
                while (!is.finite(dev)) {
                  if (ii > control$maxit) 
                    stop("inner loop 1; cannot correct step size", 
                      call. = FALSE)
                  ii <- ii + 1
                  start <- (start + coefold)/2
                  eta <- drop(x %*% start)
                  mu <- linkinv(eta <- eta + offset)
                  dev <- sum(dev.resids(y, mu, weights))
                }
                boundary <- TRUE
                if (control$trace) 
                  cat("Step halved: new deviance =", dev, "\n")
            }
            if (!(valideta(eta) && validmu(mu))) {
                if (is.null(coefold)) 
                  stop("no valid set of coefficients has been found: please supply starting values", 
                    call. = FALSE)
                warning("step size truncated: out of bounds", 
                  call. = FALSE)
                ii <- 1
                while (!(valideta(eta) && validmu(mu))) {
                  if (ii > control$maxit) 
                    stop("inner loop 2; cannot correct step size", 
                      call. = FALSE)
                  ii <- ii + 1
                  start <- (start + coefold)/2
                  eta <- drop(x %*% start)
                  mu <- linkinv(eta <- eta + offset)
                }
                boundary <- TRUE
                dev <- sum(dev.resids(y, mu, weights))
                if (control$trace) 
                  cat("Step halved: new deviance =", dev, "\n")
            }
            if (((dev - devold)/(0.1 + abs(dev)) >= control$epsilon)&(iter>1)) {
                if (is.null(coefold)) 
                  stop("no valid set of coefficients has been found: please supply starting values", 
                    call. = FALSE)
                warning("step size truncated due to increasing deviance", call. = FALSE)
                ii <- 1
                while ((dev - devold)/(0.1 + abs(dev)) > -control$epsilon) {
                  if (ii > control$maxit) break
                  ii <- ii + 1
                  start <- (start + coefold)/2
                  eta <- drop(x %*% start)
                  mu <- linkinv(eta <- eta + offset)
                  dev <- sum(dev.resids(y, mu, weights))
                }
                if (ii > control$maxit) warning("inner loop 3; cannot correct step size")
                else if (control$trace) cat("Step halved: new deviance =", dev, "\n") 
            }
            if (abs(dev - devold)/(0.1 + abs(dev)) < control$epsilon) {
                conv <- TRUE
                coef <- start
                break
            }
            else {
                devold <- dev
                coef <- coefold <- start
            }
        }
        if (!conv) 
            warning("glm.fit2: algorithm did not converge. Try increasing the maximum iterations", call. = FALSE)
        if (boundary) 
            warning("glm.fit2: algorithm stopped at boundary value", 
                call. = FALSE)
        eps <- 10 * .Machine$double.eps
        if (family$family == "binomial") {
            if (any(mu > 1 - eps) || any(mu < eps)) 
                warning("glm.fit2: fitted probabilities numerically 0 or 1 occurred", 
                  call. = FALSE)
        }
        if (family$family == "poisson") {
            if (any(mu < eps)) 
                warning("glm.fit2: fitted rates numerically 0 occurred", 
                  call. = FALSE)
        }
        if (fit$rank < nvars) {
            if (!singular.ok) stop("singular fit encountered")
            coef[fit$qr$pivot][seq.int(fit$rank + 1, nvars)] <- NA
        }
        xxnames <- xnames[fit$qr$pivot]
        residuals <- (y - mu)/mu.eta(eta)
        fit$qr$qr <- as.matrix(fit$qr$qr)
        nr <- min(sum(good), nvars)
        if (nr < nvars) {
            Rmat <- diag(nvars)
            Rmat[1L:nr, 1L:nvars] <- fit$qr$qr[1L:nr, 1L:nvars]
        }
        else Rmat <- fit$qr$qr[1L:nvars, 1L:nvars]
        Rmat <- as.matrix(Rmat)
        Rmat[row(Rmat) > col(Rmat)] <- 0
        names(coef) <- xnames
        colnames(fit$qr$qr) <- xxnames
        dimnames(Rmat) <- list(xxnames, xxnames)
    }
    names(residuals) <- ynames
    names(mu) <- ynames
    names(eta) <- ynames
    wt <- rep.int(0, nobs)
    wt[good] <- w^2
    names(wt) <- ynames
    names(weights) <- ynames
    names(y) <- ynames
    if (!EMPTY) names(fit$effects) <- c(xxnames[seq_len(fit$rank)], rep.int("", sum(good) - fit$rank))
    wtdmu <- if (intercept) sum(weights * y)/sum(weights) else linkinv(offset)
    nulldev <- sum(dev.resids(y, wtdmu, weights))
    n.ok <- nobs - sum(weights == 0)
    nulldf <- n.ok - as.integer(intercept)
    rank <- if (EMPTY) 0 else fit$rank
    resdf <- n.ok - rank
    aic.model <- aic(y, n, mu, weights, dev) + 2 * rank
    list(coefficients = coef, residuals = residuals, fitted.values = mu, 
        effects = if (!EMPTY) fit$effects, R = if (!EMPTY) Rmat, 
        rank = rank, qr = if (!EMPTY) structure(fit$qr[c("qr", "rank", 
            "qraux", "pivot", "tol")], class = "qr"), family = family, 
        linear.predictors = eta, deviance = dev, aic = aic.model, 
        null.deviance = nulldev, iter = iter, weights = wt, prior.weights = weights, 
        df.residual = resdf, df.null = nulldf, y = y, converged = conv, 
        boundary = boundary)
}

Try the glm2 package in your browser

Any scripts or data that you put into this service are public.

glm2 documentation built on May 2, 2019, 6:09 a.m.