Sandbox/Old transformation structure/bickeldocksum.lme.R

In akreutzmann/trafo: Estimation, Comparison and Selection of Transformations

#' Bickel-Doksum transformation for linear mixed models
#'
#' The function transforms the dependent variable of a linear mixed model with 
#' one random intercept using the Bickel-Doksum transformation. The 
#' transformation parameter can either be estimated using different estimation 
#' methods or given. 
#'
#' @param object an object of type lme. 
#' @param lambda either a character named "estim" if the optimal transformation
#' parameter should be estimated or a numeric value determining a given 
#' transformation parameter. Defaults to "estim".
#' @param method a character string. Different estimation methods can be used 
#' for the estimation of the optimal transformation parameter: 
#' (i) Restricted maximum likelihood approach ("reml"), 
#' (ii) Skewness minimization ("skew") and pooled skewness minimization ("pskew"), 
#' (iii) Divergence minimization by Kolmogorov-Smirnoff ("div.ks"), 
#' by Cramer-von-Mises ("div.cm") or by Kullback-Leibler ("div.kl").
#' @param lambdarange a numeric vector with two elements defining an interval 
#' that is used for the estimation of the optimal transformation parameter. 
#' Defaults to \code{c(1e-11, 2)}.
#' @param plotit logical. If TRUE, a plot that illustrates the optimal 
#' transformation parameter or the given transformation parameter is returned.
#' @param ... other parameters that can be passed to the function.
#' @return an object of class \code{trafo}.
#' @references
#' Battese, G.E., Harter, R.M. and Fuller, W.A. (1988). An Error-Components
#' Model for Predictions of County Crop Areas Using Survey and Satellite Data.
#' Journal of the American Statistical Association, Vol.83, No. 401, 28-36. \cr \cr
#' Gonzalez-Manteiga, W. et al. (2008). Bootstrap mean squared error of
#' a small-area EBLUP. Journal of Statistical Computation and Simulation,
#' 78:5, 443-462.
#' @examples
#' # Load data
#' data("eusilcA_Vienna")
#' 
#' # Fit linear mixed model
#' require(nlme)
#' lme_Vienna <- lme(eqIncome ~ eqsize + gender + cash + unempl_ben + age_ben +
#' rent + cap_inv + tax_adj + dis_ben + sick_ben + surv_ben + fam_allow + 
#' house_allow, random = ~ 1 | county, data = eusilcA_Vienna, 
#' na.action = na.omit)
#' 
#' # Transform dependent variable using restricted maximum likelihood
#' bickeldoksum(object = lme_Vienna, lambda = "estim", method = "reml",
#' plotit = FALSE)
#' @export

bickeldoksum_lme <- function(object, lambda = "estim", method = "reml", 
                             lambdarange = c(1e-11, 2), plotit = TRUE, ...) {
  
  
  trafo <- "bickeldoksum"
  oneparam(object = object, trafo = trafo, lambda = lambda, method = method, 
           lambdarange = lambdarange, plotit = plotit)
  # # Get model variables: dependent variable y and explanatory variables x
  # formula <- formula(object)
  # rand_eff <- names(object$coefficients$random)
  # data <- object$data
  # x <- model.matrix(formula, data = object$data)
  # y <- as.matrix(object$data[paste(formula[2])])
  # 
  # 
  # # For saving returns
  # ans <- list()
  # 
  # # Get the optimal transformation parameter
  # if (lambda == "estim") {
  #   optim <- est_lme(y = y, x = x, formula = formula, data = data, 
  #                                rand_eff = rand_eff, method = method, 
  #                                lambdarange = lambdarange, trafo = trafo) 
  #   
  #   lambdaoptim <- optim$lambdaoptim
  #   measoptim <- optim$measoptim
  #   
  # } else if (is.numeric(lambda)) {
  #   lambdaoptim <- lambda
  #   measoptim <- estim_lme(lambda = lambda, y = y, formula = formula, 
  #                          data = data, rand_eff = rand_eff, method = method, 
  #                          trafo = trafo)
  # }
  # 
  # # Plot the curve of the measure with line at the optimal transformation 
  # # parameter
  # if (plotit == TRUE) {
  #   plot_meas <- plot_trafolme(lambdarange = lambdarange, lambdaoptim = lambdaoptim,
  #                              measoptim = measoptim, y = y, formula = formula, 
  #                              data = data, rand_eff = rand_eff, trafo = trafo, 
  #                              method = method)
  #   
  #   if (!is.character(plot_meas)) {
  #     # Get plot measures
  #     ans$lambdavector <- plot_meas$lambdavector
  #     ans$measvector <- plot_meas$measvector 
  #   } else {
  #     ans$lambdavector <- NULL
  #     ans$measvector <- NULL
  #   }
  # } else if (plotit == FALSE) {
  #   ans$lambdavector <- NULL
  #   ans$measvector <- NULL
  # }
  # 
  # 
  # # Get vector of transformed and standardized transformed variable
  # #ans$yt <- Bick_dok(y = y, lambda = lambdaoptim)
  # #ans$zt <- Bick_dok_std(y = y, lambda = lambdaoptim)
  # 
  # # Save transformation family and method
  # #ans$family <- "Bickel-Doksum"
  # 
  # ans <- get_transformed(trafo = trafo, ans = ans, y = y, lambda = lambdaoptim)
  # 
  # # Save estimation method
  # ans$method <- method
  # 
  # ans$lambdahat <- lambdaoptim
  # ans$measoptim <- measoptim
  # 
  # # Get transformed model
  # ans$modelt <- get_modelt(object = object, trans_mod = ans, std = FALSE)
  # 
  # # New class trafo
  # class(ans) <- "trafo"
  # ans
}