est_lm

a character string. Different estimation methods can be used 
for the estimation of the optimal transformation parameter: 
(i) Maximum likelihood approach ("ml"), (ii) Skewness minimization ("skew"),
(iii) Kurtosis optimization ("kurt"), (iv) Divergence minimization by 
Kolmogorov-Smirnov ("div.ks"), by Cramer-von-Mises ("div.cvm") or by 
Kullback-Leibler ("div.kl"). Defaults to "ml".

method

range for the estimation parameter expr(lambda) - default c(-2, 2)

lambdarange

Estimation of optimal transformation parameter - lm

internal

Estimation, selection and comparison of several families of
transformations. The families of transformations included in the package are
the following: Bickel-Doksum (Bickel and Doksum 1981 <doi:10.2307/2287831>),
Box-Cox, Dual (Yang 2006 <doi:10.1016/j.econlet.2006.01.011>),
Glog (Durbin et al. 2002 <doi:10.1093/bioinformatics/18.suppl_1.S105>),
gpower (Kelmansky et al. 2013 <doi:10.1515/sagmb-2012-0030>),
Log, Log-shift opt (Feng et al. 2016 <doi:10.1002/sta4.104>),
Manly, modulus (John and Draper 1980 <doi:10.2307/2986305>),
Neglog (Whittaker et al. 2005 <doi:10.1111/j.1467-9876.2005.00520.x>),
Reciprocal and Yeo-Johnson. The package simplifies to
compare linear models with untransformed and transformed dependent variable
as well as linear models where the dependent variable is transformed with
different transformations. Furthermore, the package employs maximum likelihood
approaches, moments optimization and divergence minimization to estimate the optimal
transformation parameter.

est_lm: Estimation of optimal transformation parameter - lm

Description

Usage

Arguments

Value