#' Log shift opt transformation for linear mixed models
#'
#' The function transforms the dependent variable of a linear mixed model with
#' one random intercept using the Log shift opt 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(0, 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 pooled skewness minimization
#' boxcox(object = lme_Vienna, lambda = "estim", method = "pskew",
#' plotit = FALSE)
#' @export
sqrtshift.lme <- function(object, lambda = "estim", method = "reml",
lambdarange = c(0,2), plotit = TRUE, ...) {
trafo <- "sqrtshift"
# 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 <- box_cox(y = y, lambda = lambdaoptim)$y
#ans$zt <- box_cox_std(y = y, lambda = lambdaoptim)
# Save transformation family and method
#ans$family <- "Box-Cox"
ans <- get_transformed(trafo = trafo, ans = ans, y = y, lambda = lambdaoptim)
ans$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
}
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