| box.cox.powers {car} | R Documentation |
Estimates multivariate unconditional power transformations to multinormality by the method of maximum likelihood. The univariate case is obtained when only one variable is specified.
box.cox.powers(X, start=NULL, hypotheses=NULL, ...) ## S3 method for class 'box.cox.powers': print(x, digits=4, ...) ## S3 method for class 'box.cox.powers': summary(object, digits=4, ...)
X |
a numeric matrix of variables (or a vector for one variable) to be transformed. |
start |
start values for the power transformation parameters;
if NULL (the default), univariate Box-Cox transformations will
be computed and used as the start values. |
hypotheses |
if non-NULL, a list of hypotheses to be tested;
each hypothesis should be a vector of values giving the power for each
column of X. Note that the hypotheses that all powers are 1 and
that all powers are 0 (log) are always tested. |
... |
optional arguments to be passed to the optim function. |
digits |
number of places to round result. |
x, object |
box.cox.powers object. |
Note that this is unconditional Box-Cox. That is, there is
no regression model, and there are no predictors. The object is to
make the distribution of the variable(s) as (multi)normal as possible.
For Box-Cox regression, see the boxcox function in the
MASS package.
The function estimates the Box-Cox powers, x' = (x^p - 1)/p for p != 0 and x' = log(x) for p = 0. Subsequently using ordinary power transformations (i.e., x^p for p != 0) achieves the same result.
returns an object of class box.cox.powers, which may be printed
or summarized. the print and summary methods are now identical; I've
retained the latter for backwards compatibility.
John Fox jfox@mcmaster.ca
Box, G. E. P. and Cox, D. R. (1964) An analysis of transformations. JRSS B 26 211–246.
Cook, R. D. and Weisberg, S. (1999) Applied Regression, Including Computing and Graphics. Wiley.
boxcox, box.cox, box.cox.var,
box.cox.axis
attach(Prestige) box.cox.powers(cbind(income, education)) ## Box-Cox Transformations to Multinormality ## ## Est.Power Std.Err. Wald(Power=0) Wald(Power=1) ## income 0.2617 0.1014 2.580 -7.280 ## education 0.4242 0.4033 1.052 -1.428 ## ## L.R. test, all powers = 0: 7.694 df = 2 p = 0.0213 ## L.R. test, all powers = 1: 48.8727 df = 2 p = 0 plot(income, education) plot(box.cox(income, .26), box.cox(education, .42)) box.cox.powers(income) ## Box-Cox Transformation to Normality ## ## Est.Power Std.Err. Wald(Power=0) Wald(Power=1) ## 0.1793 0.1108 1.618 -7.406 ## ## L.R. test, power = 0: 2.7103 df = 1 p = 0.0997 ## L.R. test, power = 1: 47.261 df = 1 p = 0 qq.plot(income) qq.plot(income^.18)