#' @export
glmtf.fit <- 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) {
# parameter initialization ---------------------------------------------------
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)
# family ---------------------------------------------------------------------
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$initialize)
mustart <- mukeep
}
if (EMPTY) {
# quando o modelo é nulo (mexer depois...)----------------------------------
eta <- rep.int(0, nobs) + offset
if (!valideta(eta)) stop("invalid linear predictor values in empty model", call. = FALSE)
mu <- linkinv(eta)
if (!validmu(mu)) stop("invalid fitted means in empty model", call. = FALSE)
dev <- sum(dev.resids(y, mu, weights))
w <- sqrt((weights * mu.eta(eta)^2) / variance(mu))
residuals <- (y - mu)/mu.eta(eta)
good <- rep_len(TRUE, length(residuals))
boundary <- conv <- TRUE
coef <- numeric()
iter <- 0L
} else {
# not null model -----------------------------------------------------------
coefold <- NULL
# starting eta
eta <- if (!is.null(etastart)) {
etastart
} else if (!is.null(start)) {
if (length(start) != nvars) {
stop(gettextf("length of 'start' should equal %d and correspond to initial coefs for %s",
nvars, paste(deparse(xnames), collapse = ", ")), domain = NA)
} else {
coefold <- start
offset + as.vector(if (NCOL(x) == 1L) x * start else x %*% start)
}
} else {
family$linkfun(mustart)
}
# starting mu
mu <- linkinv(eta)
if (!(validmu(mu) && valideta(eta))) {
stop("cannot find valid starting values: please specify some", call. = FALSE)
}
# initial deviance
devold <- sum(dev.resids(y, mu, weights))
boundary <- conv <- FALSE
# main loop ----------------------------------------------------------------
for (iter in 1L:control$maxit) {
good <- weights > 0
varmu <- variance(mu)[good]
# checks
if (anyNA(varmu)) stop("NAs in V(mu)")
if (any(varmu == 0)) stop("0s in V(mu)")
mu.eta.val <- mu.eta(eta)
if (any(is.na(mu.eta.val[good]))) stop("NAs in d(mu)/d(eta)")
good <- (weights > 0) & (mu.eta.val != 0)
if (all(!good)) {
conv <- FALSE
warning(gettextf("no observations informative at iteration %d", iter), domain = NA)
break
}
z <- (eta - offset)[good] + (y - mu)[good] / mu.eta.val[good]
w <- sqrt((weights[good] * mu.eta.val[good]^2) / variance(mu)[good])
# weighted least square
# fit <- .Call(C_Cdqrls,
# x[good, , drop = FALSE] * w,
# z * w,
# min(1e-07, control$epsilon/1000),
# check = FALSE)
fit <- .tf_qr_fit(x[good, , drop = FALSE] * w,
z * w,
min(1e-07, control$epsilon/1000),
check = FALSE)
# checks
if (any(!is.finite(fit$coefficients))) {
conv <- FALSE
warning(gettextf("non-finite coefficients at iteration %d", iter), domain = NA)
break
}
if (nobs < fit$rank)
stop(sprintf(ngettext(nobs, "X matrix has rank %d, but only %d observation",
"X matrix has rank %d, but only %d observations"),
fit$rank, nobs), domain = NA)
if (!singular.ok && fit$rank < nvars)
stop("singular fit encountered")
start[fit$pivot] <- 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", sep = "")
boundary <- FALSE
# bondaries (tirar?) -----------------------------------------------------
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", sep = "")
}
# bondaries 2 (tirar?)
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", sep = "")
}
# convergence
if (abs(dev - devold) / (0.1 + abs(dev)) < control$epsilon) {
conv <- TRUE
coef <- start
break
} else {
devold <- dev
coef <- coefold <- start
}
# fim do laço
}
# convergence diagnostics --------------------------------------------------
if (!conv) warning("glm.fit: algorithm did not converge", call. = FALSE)
if (boundary) warning("glm.fit: algorithm stopped at boundary value", call. = FALSE)
eps <- 10 * .Machine$double.eps
if (family$family == "binomial") {
if (any(mu > 1 - eps) || any(mu < eps))
warning("glm.fit: fitted probabilities numerically 0 or 1 occurred", call. = FALSE)
}
if (family$family == "poisson") {
if (any(mu < eps))
warning("glm.fit: fitted rates numerically 0 occurred", call. = FALSE)
}
# ???
if (fit$rank < nvars) coef[fit$pivot][seq.int(fit$rank + 1, nvars)] <- NA
xxnames <- xnames[fit$pivot]
residuals <- (y - mu) / mu.eta(eta)
fit$qr <- as.matrix(fit$qr)
nr <- min(sum(good), nvars)
if (nr < nvars) {
Rmat <- diag(nvars)
Rmat[1L:nr, 1L:nvars] <- fit$qr[1L:nr, 1L:nvars]
} else {
Rmat <- fit$qr[1L:nvars, 1L:nvars]
}
Rmat <- as.matrix(Rmat)
Rmat[row(Rmat) > col(Rmat)] <- 0
names(coef) <- xnames
colnames(fit$qr) <- xxnames
dimnames(Rmat) <- list(xxnames, xxnames)
}
# finalizacao ----------------------------------------------------------------
names(residuals) <- ynames
names(mu) <- ynames
names(eta) <- ynames
wt <- rep.int(0, nobs)
wt[good] <- w^2
names(wt) <- ynames
names(weights) <- ynames
names(y) <- ynames
if (!EMPTY) names(fit$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$rank
resdf <- n.ok - rank
aic.model <- aic(y, n, mu, weights, dev) + 2 * rank
# return ---------------------------------------------------------------------
list(coefficients = coef, residuals = residuals, fitted.values = mu,
effects = if (!EMPTY) fit$effects, R = if (!EMPTY) Rmat,
rank = rank, qr = if (!EMPTY) structure(fit[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)
}
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