I've read that Box-Cox can help determine the appropriate exponent to use when transforming data, and I need to convert some data into a "normal shape."

From what I can tell

In linear models, the response variables are represented by car::boxCoxVariable(y), and

For a formula or fitted model object, use MASS::boxcox(object). In light of the fact that my data are variables in a dataframe, the only function I could find to utilise is:

dataframe\$variable, family="bcPower", car::powerTransform
Is that accurate? Or have I overlooked something?

The second query concerns what to do following the acquisition of

Parameters for the estimated transformation dataframe\$variable 0.6394806
Should I just multiply this by the variable? I did this:

Dataframe\$variable2 = (dataframe\$variable)*aaa; aaa = 0.6394806
the Shapiro-Wilks test for normalcy is next performed, but once more, my data don't appear to be
Jul 6, 2022 669 views

## 1 answer to this question.

Yes, you are on the right track regarding the Box-Cox transformation for determining an appropriate exponent to achieve a "normal shape" for your data. However, there are a few points to clarify and address:

1. car::boxCoxVariable(y) is not a valid syntax. Instead, you can use the car::boxCox() function to perform the Box-Cox transformation on a response variable y within a linear model.

2. If you have a formula or a fitted model object, you can use the MASS::boxcox() function to estimate the appropriate lambda value for the Box-Cox transformation.

Regarding the implementation for a variable in a dataframe:

1. To apply the Box-Cox transformation to a variable variable within a dataframe dataframe, you can use the car::powerTransform() function as follows:

```transformed_variable <- car::powerTransform(dataframe\$variable, family = "bcPower")
```

2. After obtaining the lambda value for the transformation (in your case, 0.6394806), multiplying the variable by this value won't produce the desired transformation. Instead, you need to use the boxcox() function from the MASS package to apply the transformation. Here's an example:

```transformed_variable <- MASS::boxcox(dataframe\$variable, lambda = 0.6394806)
dataframe\$variable2 <- transformed_variable\$x

```

The boxcox() function performs the Box-Cox transformation using the provided lambda value and returns an object transformed_variable that contains the transformed values (x). You can assign these transformed values to a new column `variable

3. After performing the transformation, you can then proceed with the Shapiro-Wilk test or other methods to assess the normality of your data. Keep in mind that the Box-Cox transformation aims to approximate normality but does not guarantee it in all cases.

4. Remember to replace data frame and variable with the appropriate names from your dataset.

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answered Jun 22, 2023 by anonymous
• 1,180 points

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