Sandbox/Old estimation functions/bcxEst.R

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

#' Box Cox Estimation
#' 
#' @param x matrix of regressors
#' @param y vector of response variables
#' @param lambdarange range for the estimation parameter expr(lambda) - default c(-2, 2)
#' @param tr logical value. if tr = TRUE warning messages for the likelihood functions are suppressed - default FALSE
#' @return An object of class \code{transformation} with the following arguments
#' @return llike The value of \code{profile log-likelihood} at its maximum
#' @return logvector The profile log-likelihood evaluated at \code{lambdavector}
#' @return lambdavector Employed family of transformations
#' @return A sequence with optional values for \code{lambda}
#' @return family Employed family of transformations
#' @return yt Vector of the transformed response variable \code{y}
#' @return modelt An object of type \code{lm} employing the transformed vector \code{yt} as the response variable
#' @keywords internal
bcxEst <- function(y, x , lambdarange = c(-2, 2), tr = FALSE, ...) {
  qr <- qr(x)
  n <- length(y)
  k <- ncol(x)
  yt <- rep(NA, n)
  lglike <- function(lambda, ...) {
      if (abs(lambda) != 0) {
        yt <- (y^lambda - 1)/lambda
   
        }
      else {
        yt <- log(y) 
        }
    zt <- yt/exp((lambda - 1)*mean(log(y)))
      llike <- -n/2 * log((sum(qr.resid(qr, zt)^2))/n)
      llike
} 
  res <-suppressWarnings( optimize(f = function(lambda) lglike(lambda), lambdarange, tol = 0.0001, maximum = TRUE) )
  lambdaoptim <-  res$maximum
  logoptim <-res$objective
  lambdavector <- seq(lambdarange[1], lambdarange[2], 0.01)
  l <- length(lambdavector)
  lambdavector[l + 1]  <- lambdaoptim
  lambdavector <- sort(lambdavector)
  logvector <- sapply(lambdavector, lglike)
  if (abs(lambdaoptim) > 0.05)  
    yt <- (y^lambdaoptim - 1)/lambdaoptim
  else 
    yt <- log(y) 
  zt <- yt/exp((lambdaoptim - 1)*mean(log(y)))
  suppressWarnings( modelt <- lm(zt ~ ., data.frame(zt, x[, 2:k] )))
  
  ans <- list()
  if(is.infinite(ans$llike <- logoptim ) & tr !=TRUE) 
    stop(("log-likelihood is infinite or not defined for components y and x"))
  ans$lambdahat <- lambdaoptim
  ans$logvector <- logvector
  ans$lambdavector <- lambdavector
  ans$family <- "Box-Cox"
  ans$yt <- yt
  ans$zt <- zt
  ans$modelt <- modelt
  class(ans) <- "transformation"
  ans
}