| boxcoxmeta {AID} | R Documentation |
Ensemble Based Box-Cox Transformation via Meta Analysis for Normality of a Variable
Description
boxcoxmeta performs ensemble based Box-Cox transformation via meta analysis for normality of a variable and provides graphical analysis.
Usage
boxcoxmeta(data, lambda = seq(-3,3,0.01), nboot = 100, lambda2 = NULL, plot = TRUE,
alpha = 0.05, verbose = TRUE)Arguments
data |
a numeric vector of data values. |
lambda |
a vector which includes the sequence of candidate lambda values. Default is set to (-3,3) with increment 0.01. |
nboot |
a number of Bootstrap samples to estimate standard errors of lambda estimates. |
lambda2 |
a numeric for an additional shifting parameter. Default is set to lambda2 = 0. |
plot |
a logical to plot histogram with its density line and qqplot of raw and transformed data. Defaults plot = TRUE. |
alpha |
the level of significance to check the normality after transformation. Default is set to alpha = 0.05. |
verbose |
a logical for printing output to R console. |
Details
Denote y the variable at the original scale and y' the transformed variable. The Box-Cox power transformation is defined by:
y' = \left\{ \begin{array}{ll}
\frac{y^\lambda - 1}{\lambda} \mbox{ , if $\lambda \neq 0$} \cr
log(y) \mbox{ , if $\lambda = 0$}
\end{array} \right.
If the data include any nonpositive observations, a shifting parameter \lambda_2 can be included in the transformation given by:
y' = \left\{ \begin{array}{ll}
\frac{(y + \lambda_2)^\lambda - 1}{\lambda} \mbox{ , if $\lambda \neq 0$} \cr
log(y + \lambda_2) \mbox{ , if $\lambda = 0$}
\end{array} \right.
Value
A list with class "boxcoxmeta" containing the following elements:
method |
name of method |
lambda.hat |
estimate of Box-Cox Power transformation parameter |
lambda2 |
additional shifting parameter |
result |
a data frame containing the result |
alpha |
the level of significance to assess normality. |
tf.data |
transformed data set |
var.name |
variable name |
Author(s)
Muhammed Ali Yilmaz, Osman Dag
References
Yilmaz, M. A., Dag, O. (2022). Ensemble Based Box-Cox Transformation via Meta Analysis. Journal of Advanced Research in Natural and Applied Sciences, 8:3, 463–471.
Examples
library(AID)
data(textile)
out <- boxcoxmeta(textile[,1])
out$lambda.hat # the estimate of Box-Cox parameter
out$tf.data # transformed data set