Computes the Box-Tidwell power transformations of the predictors in a linear model.
box.tidwell(y, ...) box.tidwell.formula(formula, other.x=NULL, data=NULL, subset, na.action=options()$na.action, verbose=F, tol=0.001, max.iter=25, ...) box.tidwell.default(y, x1, x2=NULL, max.iter=25, tol=0.001, verbose=F, ...)
- two-sided formula, the right-hand-side of which gives the predictors to be transformed.
- one-sided formula giving the predictors that are not candidates for transformation, including (e.g.) factors.
- an optional data frame containing the variables in the model.
By default the variables are taken from the environment from which
- an optional vector specifying a subset of observations to be used.
- a function that indicates what should happen when the data contain
NAs. The default is set by the
TRUEa record of iterations is printed.
- if maximum relative change in coefficients is less than
tolthen convergence is declared.
- maximum number of iterations.
- response variable.
- matrix of predictors to transform.
- matrix of predictors that are not candidates for transformation.
- not for the user.
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.
- an object of class
box.tidwell, which is normally just printed.
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.