R/summary.R

In mice: Multivariate Imputation by Chained Equations

#' Summary of a \code{mira} object
#'
#' @rdname summary
#' @param object A \code{mira} object
#' @param type A length-1 character vector indicating the
#' type of summary. There are three choices: \code{type = "tidy"}
#' return the parameters estimates of each analyses as a data frame.
#' \code{type = "glance"} return the fit statistics of each analysis
#' as a data frame. \code{type = "summary"} returns a list of
#' length \code{m} with the analysis results. The default is
#' \code{"tidy"}.
#' @param ... Other parameters passed down to \code{print()} and \code{summary()}
#' @return \code{NULL}
#' @seealso \code{\link[=mira-class]{mira}}
#' @method summary mira
#' @export
summary.mira <- function(object,
                         type = c("tidy", "glance", "summary"),
                         ...) {
  type <- match.arg(type)
  fitlist <- getfit(object)
  if (type == "tidy") {
    v <- lapply(fitlist, tidy, effects = "fixed", parametric = TRUE, ...) %>% bind_rows()
  }
  if (type == "glance") {
    v <- lapply(fitlist, glance, ...) %>% bind_rows()
  }
  # nobs is needed for pool.r.squared
  # not supplied by broom <= 0.5.6
  if (!"nobs" %in% colnames(v)) {
    v$nobs <- tryCatch(length(stats::residuals(getfit(object)[[1]])),
      error = function(e) NULL
    )
  }
  if (type == "summary") {
    v <- lapply(fitlist, summary, ...)
  }
  v
}


#' Summary of a \code{mids} object
#'
#' @rdname summary
#' @return \code{NULL}
#' @seealso \code{\link[=mids-class]{mids}}
#' @method summary mids
#' @export
summary.mids <- function(object, ...) {
  print(object, ...)
  invisible(object)
}


#' Summary of a \code{mads} object
#'
#' @rdname summary
#' @return \code{NULL}
#' @seealso \code{\link[=mads-class]{mads}}
#' @export
summary.mads <- function(object, ...) {
  print(object, ...)
  invisible(object)
}


#' Print a \code{mice.anova} object
#'
#' @rdname summary
#' @return \code{NULL}
#' @seealso \code{\link{mipo}}
#' @method summary mice.anova
#' @export
summary.mice.anova <- function(object, ...) {
  # handle objects from anova
  out <- object$out

  # handle objects from D1, D2 and D3
  if (is.null(out)) {
    out <- list(`1 ~~ 2` = list(
      result = object$result,
      dfcom = object$dfcom
    ))
  }

  test <- names(out)
  dfcom <- vapply(out, function(x) x$dfcom, numeric(1))
  results <- t(vapply(out, function(x) x$result, numeric(5)))
  rf <- data.frame(
    test = test,
    statistic = results[, 1],
    df1 = results[, 2],
    df2 = results[, 3],
    dfcom = dfcom,
    p.value = results[, 4],
    riv = results[, 5],
    row.names = NULL
  )

  formulas <- object$formulas
  ff <- data.frame(
    model = names(formulas),
    formula = as.character(formulas)
  )

  structure(
    list(
      models = ff, comparisons = rf,
      m = object$m, method = object$method, use = object$use
    ),
    class = c("mice.anova.summary", class(object))
  )
}