Re: [R] How can I estimate a Box-Cox function with R?

[Ikerne del Valle]

	Thanks Gregory. I see that that with 
boxcox.lm() the optimal lambda is obtained and 
plotted against log-likelihood.

library(MASS)
boxcox(Volume ~ log(Height) + log(Girth), data = trees,
       lambda = seq(-0.25, 0.25, length = 10))

But has how can I see the fit of the same linear 
model with the optimal BoxCox transformation, the 
standard error for lambda etc.?

        Best. Ikerne.



Have you looked at the boxcox function in the 
MASS package?  That may do what you want.

--
Gregory (Greg) L. Snow Ph.D.
Statistical Data Center
Intermountain Healthcare
greg.s...@imail.org
801.408.8111


 -----Original Message-----
 From: r-help-boun...@r-project.org [mailto:r-help-boun...@r-
 project.org] On Behalf Of Ikerne del Valle
 Sent: Thursday, May 21, 2009 4:29 AM
 To: fernando.tus...@ehu.es
 > Cc: r-help@r-project.org
 Subject: [R] How can I estimate a Box-Cox function with R?


        Dear Fernando and all:

        Thanks for your help. Now works. This is
 a training example to learn how to estimate a
 Box-Cox (right and/or left side transformations)
 with R (as LIMDEP does) in order to compare these
 estimations with the ones derived by applying
 NLS, ones the dependent variable has been divided
 by its geometric mean (see below) as suggested by
 (Zarembka (1974) and Spitzer (1984). However the
 example of the demand of money seems not to work.
 Any idea to face the error messages or how to
 estimate a Box-Cox function with R?

        Best regards,
        Ikerne

 library(nlrwr)
 r<-
 c(4.50,4.19,5.16,5.87,5.95,4.88,4.50,6.44,7.83,6.25,5.50,5.46,7.46,10.2
 8,11.77,13.42,11.02,8.50,8.80,7.69)
 Lr<-log(r)
 M<-
 c(480.00,524.30,566.30,589.50,628.20,712.80,805.20,861.00,908.40,1023.1
 0,1163.60,1286.60,1388.90,1497.90,1631.40,1794.40,1954.90,2188.80,2371.
 70,2563.60)
 LM<-log(M)
 Y<-
 c(2208.30,2271.40,2365.60,2423.30,2416.20,2484.80,2608.50,2744.10,2729.
 30,2695.00,2826.70,2958.60,3115.20,3192.40,3187.10,3248.80,3166.00,3277
 .70,3492.00,3573.50)
 LY<-log(Y)
 gmM<-exp((1/20)*sum(LM))
 GM<-M/gmM
 Gr<-r/gmM
 GY<-Y/gmM
 money<-data.frame(r,M,Y,Lr,LM,LY,GM,Gr,GY)
 attach(money)
 ols1<-lm(GM~r+Y)
 output1<-summary(ols1)
 coef1<-ols1$coefficients
 a1<-coef1[[1]]
 b11<-coef1[[2]]
 b21<-coef1[[3]]
 ols2<-lm(GM~Gr+GY)
 output2<-summary(ols2)
 coef2<-ols2$coefficients
 a2<-coef2[[1]]
 b12<-coef2[[2]]
 b22<-coef2[[3]]
 money.m1<-
 nls(GM~a+b*r^g+c*Y^g,data=money,start=list(a=a1,b=b11,g=1,c=b21))
 money.m2<-
 nls(GM~a+b*Gr^g+c*GY^g,data=money,start=list(a=a2,b=b12,g=1,c=b22))


                Ikerne del Valle Erkiaga
                Department of Applied Economics V
                Faculty of Economic and Business Sciences
                University of the Basque Country
                Avda. Lehendakari Agirre, Nº 83
                48015 Bilbao (Bizkaia) Spain

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