similar to: about Cox-Box transformation

Fwiw the engine behind geom_raster needs explicit observation-per-row form for input (with no structural normalization), so conversion to points is perfectly proper here, albeit confusing in context. (It's closer to what graphics devices actually use ultimately, but the expansion is laid out very early in ggplot2 because there's no standard for intermediate forms.) Cheers, Mike On Wed,

Dear R users, I need to produce rainfall maps using R. I know that this is possible, I looked though the web, I found the example below reported (the author is Andrew Tredennick). I would ask you if this is the most performing way to make rainfall maps; if yes would someone be able to give me an example of how file.asc and pointfile.csv should be? If no would somebody please show me another way

Hi, You might get more help from the R-sig-geo list, which is devoted to spatial topics. However. The *.asc file is an ArcGIS raster export format. You should use whatever the appropriate import commands are for your own gridded rainfall data. If you have a different format, you might or might not be able to import it directly with raster. ?raster will tell you more about the kinds of formats

I am trying to model the observed leaching of wood preservative chemicals from treated wood during an outdoor experiment where leaching is caused by rainfall events. For each rainfall event, the amount of rainfall was recorded as well as the amount of preservative chemical leached. A number of climatic variables were measured, but the most important is the amount of rainfall. I have tried a

Thank you Sarah and Mike for your explanations. My final objective is to produce maps (png image or any kind of extension I can import in LaTeX) where rainfall data are interpolated, using the Inverse Distance method or Kriging. My input file (pointfile.csv in the reported example) reports the station code, lat and long of the meteorological station and the rainfall value (which might be the

Dear all: I'm trying to fit the optimal Box-Cox transformation related to nls (see the code below) for the demand of money data in Green (3th Edition) but in the last step R gives the next error message. Error en `[.data.frame`(eval(object$data), , as.character(formula(object)[[2]])[2]) : undefined columns selected. ?Any idea to solve the problem? Thanks in advance,

Dear r-help members I posted this message already yesterday, but don't know whether it reached you since I joined the group only yesterday. I would like to estimate the boxcox transformed model (y^t - 1)/t ~ b0 + b1 * x. Unfortunately, R returns with an error message when I try to perform this with the call nls( I((y^t - 1)/t) ~ I(b0 + b1*x), start = c(t=1,b0=0,b1=0), data = mydataframe)

Hi All, I'm analysing some data that is conventionally modelled as log(Y) = a + bX + e. However, using the boxcox function, it appears that the optimum value of lambda is approx 0.05. I have 40 data sets of differing sizes and for about half of these, lambda is significantly non-zero. So, it is worth looking into. The alternative model, Y^lambda = a + bX + e, has been explored before by

Dear R users, Is it reasonable to transform data (measurements of plant height) to the power of 1/4? I?ve used boxcox(response~A*B) and lambda was close to 0.25. Regards, Christoph

Hi all, Does anyone know of a function that return the best lambda in box-cox transformation? many thanks for any help. -- Cristiane S. Rocha Laboratorio Genoma Funcional - Bioinform?tica Centro de Biologia Molecular e Engenharia Genetica Universidade Estadual de Campinas Campinas - SP - Brasil Tel:(19)3788-1119

Dear all R users, I have a dataset 'data' and I want to see the effect of Box-Cox transformation on it Interactively for different lambda values. I already got a look on function "vis.boxcoxu" in package "TeachingDemos". But I didn't find any option to put user's own dataset. Can anyone tell me how to put my own dataset here i.e. "data"? Thanks and

Hi,   Could any one please help how I can transform data based on Box-Cox Transformations in R.   Any helps will be much appreciated.   thanks, Kagba [[alternative HTML version deleted]]

Hi, I can't seem to find a function in R that will reverse a BoxCox transformation. Can somebody help me locate one please? Thanks in advance. Best wishes, Des [[alternative HTML version deleted]]

I've got a vector of data (hours to drive from a to b) y. After a qqplot I know, that they don't fit the normal probability. I would like to transform these data with the boxcox transformation (MASS), that they fit the model. When I try ybx<-boxcox(y~1,0) qqnorm(ybx) the plot is different from library (TeachingDemos) ybct<-bct(y,0) // qqnorm(ybct) How can I transform

Dear r-helpers, Prior to analysis of variance, I ran the Boxcox function (MASS library) to find the best power transformation of my data. However, reading the Boxcox help file, I cannot figure out if this function (through its associated log-likelihood function) corrects for * normality only * or if it also induces * homogeneity of variances *. I found in Biometry (Sokal and Rohlf, p. 419)

Dear experts, I tried to learn about Box-Cox-transformation but found the following thing: When I had to add a constant to make all values of the original variable positive, I found that the lambda estimates (box.cox.powers-function) differed dramatically depending on the specific constant chosen. In addition, the correlation between the transformed variable and the original were not 1 (as I

Hello I am using box.cox() and I get this error message: Warning message: 'box.cox' is deprecated. Use 'bcPower' instead. See help("Deprecated") and help("car-deprecated"). I went to help but I did not understand the explanation, I am still wondering what is really happening. Thanks /R

Hi there, I wrote a function that wraps MASS::boxcox as: bc <- function(vec) { lam <- boxcox(lm(vec ~ 1)) lam <- lam$x[which.max(lam$y)] (vec^lam - 1)/lam } When I invoke it as: > x <- runif(20) > bc(x) Error in eval(predvars, data, env) : object 'vec' not found I have googled, and rewrote the above function as: bc <- function(vec) { dat <<-

Hi there, I wrote a function that wraps MASS::boxcox as: bc <- function(vec) { lam <- boxcox(lm(vec ~ 1)) lam <- lam$x[which.max(lam$y)] (vec^lam - 1)/lam } When I invoke it as: > x <- runif(20) > bc(x) Error in eval(predvars, data, env) : object 'vec' not found I have googled, and rewrote the above function as: bc <- function(vec) { dat <<-

Hello, I am doing a simple regression using lm(Y~X). As my response and my predictor seemed to be skewed and I can't meet the model assumptions. Therefore I need to transform my variables. I wanted to ask what is the preferred way to find out if predictor and/or response needs to be transformed and if yes how (log-transform?). I found a procedure in "A modern approach to Regressoin in