R/D1.R

Defines functions D1

Documented in D1

#' Compare two nested models using D1-statistic
#'
#' The D1-statistics is the multivariate Wald test.
#'
#' @param fit1 An object of class \code{mira}, produced by \code{with()}.
#' @param fit0 An object of class \code{mira}, produced by \code{with()}. The
#' model in \code{fit0} is a nested within \code{fit1}. The default null
#' model \code{fit0 = NULL} compares \code{fit1} to the intercept-only model.
#' @param dfcom A single number denoting the
#' complete-data degrees of freedom of model \code{fit1}. If not specified,
#' it is set equal to \code{df.residual} of model \code{fit1}. If that cannot
#' be done, the procedure assumes (perhaps incorrectly) a large sample.
#' @param df.com Deprecated
#' @note Warning: `D1()` assumes that the order of the variables is the
#' same in different models. See
#' \url{https://github.com/amices/mice/issues/420} for details.
#' @references
#' Li, K. H., T. E. Raghunathan, and D. B. Rubin. 1991.
#' Large-Sample Significance Levels from Multiply Imputed Data Using
#' Moment-Based Statistics and an F Reference Distribution.
#' \emph{Journal of the American Statistical Association}, 86(416): 1065–73.
#'
#' \url{https://stefvanbuuren.name/fimd/sec-multiparameter.html#sec:wald}
#' @examples
#' # Compare two linear models:
#' imp <- mice(nhanes2, seed = 51009, print = FALSE)
#' mi1 <- with(data = imp, expr = lm(bmi ~ age + hyp + chl))
#' mi0 <- with(data = imp, expr = lm(bmi ~ age + hyp))
#' D1(mi1, mi0)
#' \dontrun{
#' # Compare two logistic regression models
#' imp <- mice(boys, maxit = 2, print = FALSE)
#' fit1 <- with(imp, glm(gen > levels(gen)[1] ~ hgt + hc + reg, family = binomial))
#' fit0 <- with(imp, glm(gen > levels(gen)[1] ~ hgt + hc, family = binomial))
#' D1(fit1, fit0)
#' }
#' @seealso \code{\link[mitml]{testModels}}
#' @export
D1 <- function(fit1, fit0 = NULL, dfcom = NULL, df.com = NULL) {
  install.on.demand("mitml")

  # legacy handling
  if (!missing(df.com)) {
    warning("argument df.com is deprecated; please use dfcom instead.",
      call. = FALSE
    )
    dfcom <- df.com
  }

  dfcom <- get.dfcom(fit1, dfcom)

  # fit1: a fitlist or mira-object
  # fit0: named numerical vector, character vector, or list
  fit1 <- as.mitml.result(fit1)
  est1 <- pool(fit1, dfcom = dfcom)
  qbar1 <- getqbar(est1)

  if (is.null(fit0)) {
    # test all estimates equal to zero, except intercept
    beta <- rep(0, length(qbar1))
    names(beta) <- names(qbar1)
    fit0 <- lapply(fit1, fix.coef, beta = beta)
    fit0 <- as.mitml.result(fit0)
  } else if (is.mira(fit0)) {
    fit0 <- as.mitml.result(fit0)
  }

  tmr <- mitml::testModels(fit1, fit0, method = "D1", df.com = dfcom)

  out <- list(
    call = match.call(),
    result = tmr$test,
    formulas = list(
      `1` = formula(fit1[[1L]]),
      `2` = formula(fit0[[1L]])
    ),
    m = tmr$m,
    method = "D1",
    use = NULL,
    dfcom = tmr$df.com
  )
  class(out) <- c("mice.anova", class(fit1))
  out
}

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mice documentation built on Nov. 19, 2022, 5:06 p.m.