R/fit.R

In jtrecenti/tensorglm:

#' @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)
}