# box.tidwell

From car v1.2-16
by John Fox

##### Box-Tidwell Transformations

Computes the Box-Tidwell power transformations of the predictors in a linear model.

- Keywords
- regression

##### Usage

```
box.tidwell(y, ...)
## S3 method for class 'formula':
box.tidwell(formula, other.x=NULL, data=NULL, subset,
na.action=options()$na.action, verbose=FALSE, tol=0.001,
max.iter=25, ...)
## S3 method for class 'default':
box.tidwell(y, x1, x2=NULL, max.iter=25, tol=0.001,
verbose=FALSE, ...)
## S3 method for class 'box.tidwell':
print(x, digits, ...)
```

##### Arguments

- formula
- two-sided formula, the right-hand-side of which gives the predictors to be transformed.
- other.x
- one-sided formula giving the predictors that are
*not*candidates for transformation, including (e.g.) factors. - data
- an optional data frame containing the variables in the model.
By default the variables are taken from the environment from which
`box.tidwell`

is called. - subset
- an optional vector specifying a subset of observations to be used.
- na.action
- a function that indicates what should happen when the data contain
`NA`

s. The default is set by the`na.action`

setting of`options`

. - verbose
- if
`TRUE`

a record of iterations is printed. - tol
- if maximum relative change in coefficients is less than
`tol`

then convergence is declared. - max.iter
- maximum number of iterations.
- y
- response variable.
- x1
- matrix of predictors to transform.
- x2
- matrix of predictors that are
*not*candidates for transformation. - ...
- not for the user.
- x
`box.tidwell`

object.- digits
- number of digits for rounding.

##### Details

The maximum-likelihood estimates of the transformation parameters are computed by Box and Tidwell's (1962) method, which is usually more efficient than using a general nonlinear least-squares routine for this problem. Score tests for the transformations are also reported.

##### Value

- an object of class
`box.tidwell`

, which is normally just printed.

##### References

Box, G. E. P. and Tidwell, P. W. (1962)
Transformation of the independent variables.
*Technometrics* **4**, 531-550.
Fox, J. (1997)
*Applied Regression, Linear Models, and Related Methods.* Sage.

##### Examples

```
box.tidwell(prestige~income+education, ~ poly(women,2), data=Prestige)
## income education
## Initial Power -0.91030 2.24354
## Score Statistic -5.30129 2.40556
## p-value 0.00000 0.01615
## MLE of Power -0.03777 2.19283
```

*Documentation reproduced from package car, version 1.2-16, License: GPL (>= 2)*

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