#' Honda's Test for Heteroskedasticity in a Linear Regression Model
#'
#' This function implements the two-sided LM Test of
#' \insertCite{Honda89;textual}{skedastic} for testing for heteroskedasticity
#' in a linear regression model.
#'
#' @details The test assumes that heteroskedasticity, if present, would be
#' either of the form \eqn{\omega_i = \omega(1+\theta z_i)} or of the
#' form \eqn{\omega_i = \omega e^{\theta z_i}}, where
#' where \eqn{z_i} is a deflator (a nonstochastic variable
#' suspected of being related to the error variance), \eqn{\omega} is
#' some unknown constant, and \eqn{\theta} is an unknown parameter
#' representing the degree of heteroskedasticity. Since the test
#' statistic \eqn{Q=\frac{e' A_0 e}{e'e}} is a ratio of quadratic forms
#' in the errors, the Imhof algorithm is used to compute \eqn{p}-values.
#' Since the null distribution is in general asymmetrical, the two-sided
#' \eqn{p}-value is computed using the conditional method of
#' \insertCite{Kulinskaya08;textual}{skedastic}, setting \eqn{A=1}.
#'
#' @param deflator Either a character specifying a column name from the
#' design matrix of \code{mainlm} or an integer giving the index of a
#' column of the design matrix. This variable is suspected to be
#' related to the error variance under the alternative hypothesis.
#' \code{deflator} may not correspond to a column of 1's (intercept).
#' Default \code{NA} means the data will be left in its current order
#' (e.g. in case the existing index is believed to be associated with
#' error variance).
#'
#' @inheritParams breusch_pagan
#' @inheritParams carapeto_holt
#'
#' @return An object of \code{\link[base]{class}} \code{"htest"}. If object is
#' not assigned, its attributes are displayed in the console as a
#' \code{\link[tibble]{tibble}} using \code{\link[broom]{tidy}}.
#' @references{\insertAllCited{}}
#' @importFrom Rdpack reprompt
#' @export
#'
#' @examples
#' mtcars_lm <- lm(mpg ~ wt + qsec + am, data = mtcars)
#' # In this example, only the test statistic is returned, to save time
#' honda(mtcars_lm, deflator = "qsec", statonly = TRUE)
#'
honda <- function(mainlm, deflator = NA, alternative = c("two.sided",
"greater", "less"), twosidedmethod =
c("doubled", "kulinskaya"), qfmethod = "imhof",
statonly = FALSE) {
alternative <- match.arg(alternative, c("two.sided", "greater", "less"))
twosidedmethod <- match.arg(twosidedmethod, c("doubled", "kulinskaya"))
processmainlm(m = mainlm, needy = FALSE)
hasintercept <- columnof1s(X)
if (inherits(mainlm, "list")) {
if (hasintercept[[1]]) {
if (hasintercept[[2]] != 1) stop("Column of 1's must be first column of design matrix")
colnames(X) <- c("(Intercept)", paste0("X", 1:(p - 1)))
} else {
colnames(X) <- paste0("X", 1:p)
}
}
n <- nrow(X)
checkdeflator(deflator, X, p, hasintercept[[1]])
M <- fastM(X, n)
if (is.na(deflator) || is.null(deflator)) {
A0 <- diag(1:n)
} else {
if (!is.na(suppressWarnings(as.integer(deflator)))) {
deflator <- as.integer(deflator)
}
A0 <- diag(X[, deflator])
}
teststat <- as.double((t(e) %*% A0 %*% e) / crossprod(e))
if (statonly) return(teststat)
if (alternative == "greater") {
pval <- pRQF(r = teststat, A = M %*% A0 %*% M,
B = M, algorithm = qfmethod, lower.tail = FALSE)
} else if (alternative == "less") {
pval <- pRQF(r = teststat, A = M %*% A0 %*% M,
B = M, algorithm = qfmethod, lower.tail = TRUE)
} else if (alternative == "two.sided") {
pval <- twosidedpval(q = teststat, CDF = pRQF,
method = twosidedmethod, continuous = TRUE,
A = M %*% A0 %*% M, B = M, algorithm = qfmethod,
lower.tail = TRUE)
}
rval <- structure(list(statistic = teststat, p.value = pval,
null.value = 1,
alternative = alternative), class = "htest")
broom::tidy(rval)
}
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