What is the Box-Cox transformation applied during PCA in R using the caret package



As can be seen in the above image,while doing PCA box-cox transformation has been applied.Looking at the options of center and scale I think this has something to do with standardising the variables prior to applying the PCA.
If someone can please shed more light on this,it would be knowledge-ful. :stuck_out_tongue:



As we do standardization of variables before applying PCA to get the right principal components. Similarly, skewness can also influence the resulting PCs, it is good practice to apply skewness transformation also prior to the application of PCA. Here Box-Cox transformation is used to correct the skewness of the variables.

For more detail on Box-Cox transformation, you can look at this article.



The Box-Cox (method = “BoxCox”), are being used to transform the predictor variables.
The Box-Cox transformation was developed for transforming the response variable while another method,

However, the Box-Cox method is simpler, more computationally efficient and is equally effective for estimating power transformations.
The Yeo-Johnson transformation is similar to the Box-Cox model but can accommodate predictors with zero and/or negative values

(while the predictors values for the Box-Cox transformation must be strictly positive.)

method = “center” subtracts the mean of the predictor’s data (again from the data in x)
from the predictor values while.

method = “scale” divides by the standard deviation.