similar to: Reversing data transformation

From: aguitatierra@hotmail.com Sent: Friday, April 05, 2013 11:47 PM To: r-help@r-project.org ; R Help Subject: Reversing data transformation Hi everybody, I would be very grateful if you could give me your thoughts on the following issue. I need to perform Box-Cox (bcPower€) transformation on my data. To do this, I calculated lambda using the function '€powerTransform'€.

Dear all, I’m finding difficulties to normalize this data. Could you provide some input? DATA: c(0.000103113, 0.000102948, 0.000104001, 0.000103794, 0.000104628, 9.2765e-05, 9.4296e-05, 9.5025e-05, 9.4978e-05, 9.8821e-05, 9.7586e-05, 9.6285e-05, 0.00010158, 0.000100919, 0.000103535, 0.000103321, 0.000102842, 0.000102315, 0.0001033, 0.000103691, 0.000102689, 0.000103248, 0.000101216,

Hello, I would like to perform a Box-Cox (“bcPower”) transformation on my data. For this, I am determining lambda using the “powerTransform” function. However, with one of my variables I get the following Warning Message: In estimateTransform(x, y, NULL, ...) : Convergence failure: return code = 52 My variable is: > x [1] 0.0001031130 0.0001029480 0.0001040010 0.0001037940 0.0001046280

Dear Ron and Bert, First (and without considering why one would want to do this, e.g., adding a start of 1 to the data), the following works for me: ------ snip ------ > library(MASS) > BoxCoxLambda <- function(z){ + b <- boxcox(z + 1 ~ 1, + lambda = seq(-5, 5, length.out = 101), + plotit = FALSE) + b$x[which.max(b$y)] + } > mrow <- 500

Thanks John. ?boxcox says: ************************* Arguments object a formula or fitted model object. Currently only lm and aov objects are handled. ************************* I read that as saying that boxcox(lm(z+1 ~ 1),...) should run without error. But it didn't. And perhaps here's why: BoxCoxLambda <- function(z){ b <- MASS:::boxcox.lm(lm(z+1 ~ 1), lambda = seq(-5, 5,

No, I'm afraid I'm wrong. Something went wrong with my R session and gave me incorrect answers. After restarting, I continued to get the same error as you did with my supposed "fix." So just ignore what I said and sorry for the noise. -- Bert On Sat, Jul 8, 2023 at 8:28?AM Bert Gunter <bgunter.4567 at gmail.com> wrote: > Try this for your function: > >

Hi Bert, On 2023-07-08 3:42 p.m., Bert Gunter wrote: > Caution: This email may have originated from outside the organization. Please exercise additional caution with any links and attachments. > > > Thanks John. > > ?boxcox says: > > ************************* > Arguments > > object > > a formula or fitted model object. Currently only lm and aov objects

Hi, I am new to R. I need help with regards to box cox transformation. I have phenotypic data for e.g. plant height. data is non-normal. Skewness is 0.34. Could you please help me? Regards, Yogi -- View this message in context: http://r.789695.n4.nabble.com/boxcox-transformations-tp4682077.html Sent from the R help mailing list archive at Nabble.com. [[alternative HTML version deleted]]

Hello, I would like to perform a Box-Cox (“bcPower”) transformation on my data. For this, I am determining lambda using the “powerTransform” function. However, with one of my variables I get the following Warning Message: In estimateTransform(x, y, NULL, ...) : Convergence failure: return code = 52 My variable is: > x [1] 0.0001031130 0.0001029480 0.0001040010 0.0001037940 0.0001046280

Georges Monette and I would like to announce a new package, cv, now on CRAN, which implements cross-validation of regression models. Some of the functions supplied by the package: - cv() is a generic function with a default method and computationally efficient "lm" and "glm" methods, along with a method for a list of competing models. There are also experimental

Georges Monette and I would like to announce a new package, cv, now on CRAN, which implements cross-validation of regression models. Some of the functions supplied by the package: - cv() is a generic function with a default method and computationally efficient "lm" and "glm" methods, along with a method for a list of competing models. There are also experimental