R/print_dcca.R

In douconca: Double Constrained Correspondence Analysis for Trait-Environment Analysis in Ecology

#' Print a summary of a dc-CA object
#' 
#' @param x a dc-CA object from \code{\link{dc_CA}}
#' @param ...  Other arguments passed to the function (currently ignored).
#' 
#' @details
#' \code{x <- print(x)} is more efficient for \code{\link{scores.dcca}} than 
#' just \code{print(x)} if \code{\link{dc_CA}} is called without argument
#' \code{verbose} (or called with \code{verbose = FALSE}).
#' 
#' @example demo/dune_dcCA.r
#' 
#' @noRd
#' @keywords internal
print_dcca <- function(x, 
                       ...) {
  if (length(x$eigenvalues)) {
    if (inherits(x, "dcca")) {
      if (!"species_axes" %in% names(x)) {
        x$site_axes <- f_env_axes(x)
        x$species_axes <- f_trait_axes(x)
        x$c_env_normed <- x$site_axes$c_env_normed
        x$c_traits_normed <- x$species_axes$c_traits_normed
      }
    } else if (any(inherits(x, c("wrda", "cca0"), which = TRUE) == 1)) {
      x$site_axes <- f_env_axes(x)
      x$c_env_normed <- x$site_axes$c_env_normed
    } else {
      stop("The first argument must be of class 'dcca', 'wrda',",
     	   "or 'cca0' the result of the function dc_CA, wrda or cca0.\n")
    }
    choices <- 1:4
    if (inherits(x, "dcca")) {
      cat("Step 1: the CCA ordination of the transposed matrix with", 
          "trait constraints,\n")
      cat("        useful in itself and also yielding CWMs of the", 
          "orthonormalized traits for step 2.\n")
      print(x$CCAonTraits)
      cat("Step 2: the RDA ordination of CWMs of the orthonormalized traits \n", 
          "       of step 1 with environmental constraints:\n")
      print(x$RDAonEnv)
      cols <- c(choices, 4 + rank_mod(x))
      cols <- cols[seq_len(min(c(ncol(x$c_traits_normed), length(cols))))]
      c_t <- x$c_traits_normed[, cols, drop = FALSE]
    } else {
      print.cca(x)
      c_t <- NULL
    }
    if (any(inherits(x, c("dccav", "wrda", "cca0"), which = TRUE) == 1)) {
      c_e <- x$c_env_normed[, c(choices, 4 + rank_mod(x)), drop = FALSE]
      cat("mean, sd, VIF and canonical coefficients with their optimistic [!]", 
          "t-values:\n")
      print(round(c_e, 4))
    }
    if (!is.null(c_t)) {
      print (round(c_t, 4))
    }
    cat("\n")
  } # length eigenvalues
  if (inherits(x, "dcca")) {
    print(round(x$inertia, 3))
  }
  return(invisible(x))
}