estim_lme: Wrapper function for estimation methods - linear mixed models

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

Description

Wrapper function for estimation methods - linear mixed models

Usage

estim_lme(lambda, y, formula, data, rand_eff, method, trafo, custom_func,
  custom_func_std)

Arguments

lambda	transformation parameter
y	vector of response variables
formula	a formula object that contains the dependent and the explanatory measures
data	the data.frame that is given to function nlme and that contains the regression variables.
rand_eff	the random effect extracted from the lme object.
method	a character string. In order to determine the optimal parameter for the transformation five different estimation methods can be chosen (i) Maximum-Likelihood ("ml"); (ii) skewness minimization ("skew"); (iii) minimization of Kolmogorov-Smirnov divergence ("div.ks"); (iv) minimization of Cramer von Mises divergence ("div.cvm"); (v) minimization of Kullback Leibler divergence ("div.kl"). In case of no and log transformation "NA" can be selected since no optimization is necessary for these two transformation types.
trafo	a character string that selects the transformation.
custom_func	a function that determines a customized transformation.
custom_func_std	a function that determines a customized standard transformation.

Value

Depending on the selected method the return is a log likelihood, a skewness, a pooled skewness or a Kolmogorov-Smirnov, Cramer von Mises or Kullback Leibler divergence.

akreutzmann/trafo documentation built on Sept. 14, 2020, 9:03 p.m.