R/estimateContrasts.R

In s20x: Functions for University of Auckland Course STATS 201/208 Data Analysis

#' Contrast Estimates
#'
#' Calculates and prints Tukey multiple confidence intervals for contrasts in
#' one or two-way ANOVA.
#'
#' @param contrast.matrix A matrix of contrast coefficients. Separate rows of
#'   the matrix contain the contrast coefficients for that particular contrast,
#'   and a column for each level of the factor.
#' @param fit Output from the [lm()] function.
#' @param row If `TRUE`, and the ANOVA is two-way, then contrasts in the row
#'   effects are printed, otherwise contrasts in the column effects are printed.
#'   Ignored if the ANOVA is one-way.
#' @param alpha The nominal error rate for the multiple confidence intervals.
#' @param L Number of contrasts. If `NULL`, `L` will be set to the number of rows
#'   in the contrast matrix, otherwise `L` will be as specified.
#' @param FUN Optional function to be applied to estimates and confidence
#'   intervals. Typically used for back-transformation operations.
#'
#' @return Returns a matrix whose rows correspond to the different contrasts
#'   being estimated and whose columns correspond to the point estimate of the
#'   contrast, the Tukey lower and upper limits of the confidence interval, the
#'   unadjusted p-value, and the Tukey and Bonferroni p-values.
#' @seealso [summary1way()], [summary2way()], [multipleComp()]
#' @export
#' @keywords models
#'
#' @examples
#' ## computer data:
#' data(computer.df)
#' computer.df = within(computer.df, {selfassess = factor(selfassess)})
#' computer.fit = lm(score ~ selfassess, data = computer.df)
#' contrast.matrix = matrix(c(-1 / 2, -1 / 2, 1), byrow = TRUE, nrow = 1, ncol = 3)
#' contrast.matrix
#' estimateContrasts(contrast.matrix, computer.fit)
estimateContrasts = function(contrast.matrix, fit, row = TRUE, alpha = 0.05, L = NULL, FUN = identity) {
  FUN = match.fun(FUN)

  if (!inherits(fit, "lm")) {
    stop("Second input is not an \"lm\" object")
  }

  if (length(dimnames(fit$model)[[2]]) == 2) {
    estimateContrasts1(contrast.matrix, fit, alpha = alpha, L, FUN = FUN)
  } else {
    estimateContrasts2(contrast.matrix, fit, alpha = alpha, row, L, FUN = FUN)
  }
}