# boxTidwell

##### Box-Tidwell Transformations

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

- Keywords
- regression

##### Usage

`boxTidwell(y, ...)`# S3 method for formula
boxTidwell(formula, other.x=NULL, data=NULL, subset,
na.action=getOption("na.action"), verbose=FALSE, tol=0.001,
max.iter=25, ...)

# S3 method for default
boxTidwell(y, x1, x2=NULL, max.iter=25, tol=0.001,
verbose=FALSE, ...)
# S3 method for boxTidwell
print(x, digits=getOption("digits") - 2, ...)

##### 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

`boxTidwell`

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; default is`FALSE`

.- tol
if the 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
`boxTidwell`

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 `boxTidwell`

, 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. (2016)
*Applied Regression Analysis and Generalized Linear Models*,
Third Edition. Sage.

Fox, J. and Weisberg, S. (2019)
*An R Companion to Applied Regression*, Third Edition, Sage.

##### Examples

```
# NOT RUN {
boxTidwell(prestige ~ income + education, ~ type + poly(women, 2), data=Prestige)
# }
```

*Documentation reproduced from package car, version 3.0-3, License: GPL (>= 2)*