R powerTransform fails on seemingly okay data I'm having problems with powerTransform - for example it has just failed to transform a perfectly ordinary (to me) looking variable, with the following error
Error in optim(start, llik, hessian = TRUE, method = method, ...) :
  L-BFGS-B needs finite values of 'fn'

Here is the data: http://www.tropic.org.uk/~crispin/boxcoxerror
> require(car)
> data = read.table("boxcoxerror")
> mean(data)
      V1
39401.55
> sd(data)
     V1
5381.04
> powerTransform(data$V1)
Error in optim(start, llik, hessian = TRUE, method = method, ...) :
  L-BFGS-B needs finite values of 'fn'

Any hints?
 A: I'm not really sure what's so funny about this data -- it doesn't look odd in any particular ways (the coefficient of variation is reasonable, there's nothing glaringly weird in the histogram ...) However, here are some possible routes forward.
require(car)
bcdata = unlist(read.table(url("http://www.tropic.org.uk/~crispin/boxcoxerror")))
mean(bcdata)
sd(bcdata)
hist(bcdata,freq=FALSE)
lines(density(bcdata))

Reproduce the error (R 2.14.1, 32-bit Linux)
powerTransform(bcdata)

L-BFGS-B, the optimizer used internally, is notoriously sensitive to scaling issues.  This appears to work:
powerTransform(bcdata/1000)

Oddly enough, boxcox gives quite different answers based on scaling too:
m <- MASS::boxcox(lm(bcdata~1),lambda=seq(-4,2,by=0.05))
m2 <- MASS::boxcox(lm(bcdata/1000~1),lambda=seq(-8,2,by=0.05))
m2$x[which.max(m2$y)]  ## agrees pretty well with powerTransform()

The other thing that worries me is that the power transformation being suggested is so extreme -- do we really need a power of nearly -5 to normalize these data?
Perhaps someone who's thinking more carefully about the actual analytical details of the Box-Cox/power transformations can explain what's happening here.
