Box-Cox and Exponential Transformations

These classes can be used to estimate transformations and apply them to existing and future data

BoxCoxTrans(y, ...)

# S3 method for default
BoxCoxTrans(y, x = rep(1, length(y)), fudge = 0.2,
  numUnique = 3, na.rm = FALSE, ...)

# S3 method for BoxCoxTrans
print(x, newdata, digits = 3, ...)

# S3 method for BoxCoxTrans
predict(object, newdata, ...)

Arguments

y	a numeric vector of data to be transformed. For BoxCoxTrans, the data must be strictly positive.
...	for BoxCoxTrans: options to pass to boxcox. plotit should not be passed through. For predict.BoxCoxTrans, additional arguments are ignored.
x	an optional dependent variable to be used in a linear model.
fudge	a tolerance value: lambda values within +/-fudge will be coerced to 0 and within 1+/-fudge will be coerced to 1.
numUnique	how many unique values should y have to estimate the transformation?
na.rm	a logical value indicating whether NA values should be stripped from y and x before the computation proceeds.
newdata	a numeric vector of values to transform.
digits	minimal number of significant digits.
object	an object of class BoxCoxTrans or expoTrans.

Value

Both functions returns a list of class of either BoxCoxTrans or expoTrans with elements

lambda

estimated transformation value

fudge

value of fudge

n

number of data points used to estimate lambda

summary

the results of summary(y)

ratio

max(y)/min(y)

skewness

sample skewness statistic

BoxCoxTrans also returns:

fudge

value of fudge

The predict functions returns numeric vectors of transformed values

Details

BoxCoxTrans function is basically a wrapper for the boxcox function in the MASS library. It can be used to estimate the transformation and apply it to new data.

expoTrans estimates the exponential transformation of Manly (1976) but assumes a common mean for the data. The transformation parameter is estimated by directly maximizing the likelihood.

If any(y <= 0) or if length(unique(y)) < numUnique, lambda is not estimated and no transformation is applied.

References

Box, G. E. P. and Cox, D. R. (1964) An analysis of transformations (with discussion). Journal of the Royal Statistical Society B, 26, 211-252. Manly, B. L. (1976) Exponential data transformations. The Statistician, 25, 37 - 42.

See also

boxcox, preProcess, optim

Examples

data(BloodBrain)

ratio <- exp(logBBB)
bc <- BoxCoxTrans(ratio)
bc
#> Box-Cox Transformation
#> 
#> 208 data points used to estimate Lambda
#> 
#> Input data summary:
#>    Min. 1st Qu.  Median    Mean 3rd Qu.    Max. 
#>  0.1165  0.6554  1.0204  1.2817  1.6989  5.1552 
#> 
#> Largest/Smallest: 44.3 
#> Sample Skewness: 1.46 
#> 
#> Estimated Lambda: 0.2 
#> 

predict(bc, ratio[1:5])
#> [1]  1.2055119 -0.3844183  0.2249118  0.1419784  0.7398778

ratio[5] <- NA
bc2 <- BoxCoxTrans(ratio, bbbDescr$tpsa, na.rm = TRUE)
bc2
#> Box-Cox Transformation
#> 
#> 207 data points used to estimate Lambda
#> 
#> Input data summary:
#>    Min. 1st Qu.  Median    Mean 3rd Qu.    Max. 
#>  0.1165  0.6538  1.0000  1.2783  1.6905  5.1552 
#> 
#> Largest/Smallest: 44.3 
#> Sample Skewness: 1.47 
#> 
#> Estimated Lambda: 0.2 
#> 

manly <- expoTrans(ratio)
manly
#> Exponential Transformation
#> 
#> 207 data points used to estimate Lambda
#> 
#> Input data summary:
#>    Min. 1st Qu.  Median    Mean 3rd Qu.    Max.    NA's 
#>  0.1165  0.6538  1.0000  1.2783  1.6905  5.1552       1 
#> 
#> Largest/Smallest: 44.26 
#> Sample Skewness: 1.467 
#> 
#> Estimated Lambda: -0.5726 
#>