After trying this with the function "estimateTransform" from {car}, it returns values similar to my solution rather than the one from MASS::boxcox:
# Toy data ################ set.seed(13241089) x <- rnorm(1000, 10) x2 <- x**2 # we want to transform x2 to something more normal # using MASS::boxcox ################ mle <- function(BC) { with(BC, x[which.max(y)]) } ONES <- rep(1, length(x2)) a <- MASS::boxcox(lm(x2 ~ ONES)) mle(a) # lambda: # 0.42424 # using estimateTransform from car ################ # Same result as the paper: ! library(car) ONES <- rep(1, length(x2)) estimateTransform(X=data.frame(x = ONES), Y = x2) # lambda: # 0.40782 (just as with my own function in the previous email) What am I missing? ----------------Contact Details:------------------------------------------------------- Contact me: tal.gal...@gmail.com | Read me: www.talgalili.com (Hebrew) | www.biostatistics.co.il (Hebrew) | www.r-statistics.com (English) ---------------------------------------------------------------------------------------------- On Mon, Oct 12, 2015 at 12:17 PM, Tal Galili <tal.gal...@gmail.com> wrote: > Hello all, > > Given a set of observations, I would like to find lambda for a boxcox > transformation so to get a symmetric/normal result as possible. > > I try to use MASS::boxcox, but get different results than when using the > formula from the original box-cox paper (link > <http://www.jstor.org/stable/2984418?seq=1#page_scan_tab_contents>). > > I probably have made an error somewhere, but I can't figure out where. > > Here is an example in which the lambda by MASS::boxcox is 0.42424, while > by the formula from the paper I get 0.40782. > > > > > > > > > # Toy data > ################ > set.seed(13241089) > x <- rnorm(1000, 10) > x2 <- x**2 # we want to transform x2 to something more normal > plot(density(x2)) > > # using MASS::boxcox > ################ > > zpoints <- function(y) { > n <- length(y) > qnorm(ppoints(n))[order(order(y))] > } > mle <- function(BC) { > with(BC, x[which.max(y)]) > } > > a <- MASS::boxcox(x2 ~ zpoints(x2)) > mle(a) > # lambda: > # 0.42424 > > > > # using formula from the paper > ################ > > loglik_lambda <- function(l, y) { > GM <- exp(mean(log(y))) > if(l==0) x <- log(y)*GM else x <- (y^l-1)/ (l * GM^(l-1) ) > # if(l==0) x <- log(y) else x <- (y^l-1)/ (l ) > sd(x) > } > fo <- function(l) loglik_lambda(l, y = x2) > V_fo <- Vectorize(fo) > V_fo(2) > curve(V_fo, -.5,1.5) > optimize(V_fo, c(-3,3)) > # lambda: > # 0.40782 > > > > > > > > > > > > > [[alternative HTML version deleted]] ______________________________________________ R-help@r-project.org mailing list -- To UNSUBSCRIBE and more, see https://stat.ethz.ch/mailman/listinfo/r-help PLEASE do read the posting guide http://www.R-project.org/posting-guide.html and provide commented, minimal, self-contained, reproducible code.