sandbox/Shubhankit/sandbox/Week6-7/Code/Covariance Matrix Integrated Regression Function/glmi.R

In braverock/PerformanceAnalytics: Econometric Tools for Performance and Risk Analysis

glmi <- function (formula, family = gaussian, data,vcov = NULL, weights, subset, 
          na.action, start = NULL, etastart, mustart, offset, control = list(...), 
          model = TRUE, method = "glm.fit", x = FALSE, y = TRUE, contrasts = NULL, 
          ...) 
{
  call <- match.call()
  if (is.character(family)) 
    family <- get(family, mode = "function", envir = parent.frame())
  if (is.function(family)) 
    family <- family()
  if (is.null(family$family)) {
    print(family)
    stop("'family' not recognized")
  }
  if (missing(data)) 
    data <- environment(formula)
  mf <- match.call(expand.dots = FALSE)
  m <- match(c("formula", "data", "subset", "weights", "na.action", 
               "etastart", "mustart", "offset"), names(mf), 0L)
  mf <- mf[c(1L, m)]
  mf$drop.unused.levels <- TRUE
  mf[[1L]] <- as.name("model.frame")
  mf <- eval(mf, parent.frame())
  if (identical(method, "model.frame")) 
    return(mf)
  if (!is.character(method) && !is.function(method)) 
    stop("invalid 'method' argument")
  if (identical(method, "glm.fit")) 
    control <- do.call("glm.control", control)
  mt <- attr(mf, "terms")
  Y <- model.response(mf, "any")
  if (length(dim(Y)) == 1L) {
    nm <- rownames(Y)
    dim(Y) <- NULL
    if (!is.null(nm)) 
      names(Y) <- nm
  }
  X <- if (!is.empty.model(mt)) 
    model.matrix(mt, mf, contrasts)
  else matrix(, NROW(Y), 0L)
  weights <- as.vector(model.weights(mf))
  if (!is.null(weights) && !is.numeric(weights)) 
    stop("'weights' must be a numeric vector")
  if (!is.null(weights) && any(weights < 0)) 
    stop("negative weights not allowed")
  offset <- as.vector(model.offset(mf))
  if (!is.null(offset)) {
    if (length(offset) != NROW(Y)) 
      stop(gettextf("number of offsets is %d should equal %d (number of observations)", 
                    length(offset), NROW(Y)), domain = NA)
  }
  mustart <- model.extract(mf, "mustart")
  etastart <- model.extract(mf, "etastart")
  fit <- eval(call(if (is.function(method)) "method" else method, 
                   x = X, y = Y, weights = weights, start = start, etastart = etastart, 
                   mustart = mustart, offset = offset, family = family, 
                   control = control, intercept = attr(mt, "intercept") > 
                     0L))
  if (length(offset) && attr(mt, "intercept") > 0L) {
    fit2 <- eval(call(if (is.function(method)) "method" else method, 
                      x = X[, "(Intercept)", drop = FALSE], y = Y, weights = weights, 
                      offset = offset, family = family, control = control, 
                      intercept = TRUE))
    if (!fit2$converged) 
      warning("fitting to calculate the null deviance did not converge -- increase 'maxit'?")
    fit$null.deviance <- fit2$deviance
  }
  if (model) 
    fit$model <- mf
  fit$na.action <- attr(mf, "na.action")
  if (x) 
    fit$x <- X
  if (!y) 
    fit$y <- NULL
  fit <- c(fit, list(call = call, formula = formula, terms = mt, 
                     data = data, offset = offset, control = control, method = method, 
                     contrasts = attr(X, "contrasts"), xlevels = .getXlevels(mt, 
                                                                             mf)))
  class(fit) <- c(fit$class, c("glm", "lm"))
  fit
  if(is.null(vcov)) {
    se <- vcov(fit)
  } else {
    if (is.function(vcov))
      se <- vcov(fit)
    else
      se <- vcov
  }
  fit = list(fit,vHaC = se) 
  fit
  
}