similar to: Double Sided Box-Cox models?

Dear R-list, I?m trying to use the lmer of the lme4 package to fit a linear mixed model of the form Y = Xb + Zu + e and I can?t figure out how to control the covariance structure of u. I want u ~ N(0,sigma^2*I). More precisely I?m trying to smooth a curve through data using the "Penalized Splines as BLUPs" method as described in Ruppert, Wand & Carroll (2003). So I have Z = [Z1

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

Dear Rlist, Using an example in box.cox.powers() help, I have the following warning message. example: library(car) >attach(Prestige) > box.cox.powers(income) Box-Cox Transformation to Normality Est.Power Std.Err. Wald(Power=0) Wald(Power=1) 0.1793 0.1108 1.6179 -7.4062 L.R. test, power = 0: 2.7103 df = 1 p = 0.0997 L.R. test, power = 1: 47.261 df = 1 p = 0

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]]

Dear R-users, I am using R version 1.8.0-1 under Suse 8.2. I need to use the boxcox command because I want to apply a Cox-Box transformation to a vector of rainfall values. Within the libraries, the MASS library is present, but I don't know whether this means that is automatically installed or not. The command doesn't work. What do I have to do in order to make it working? Is just a

Hi: Recently, I want to perform a transformation on my data to make it more normal, meanwhile the order statistics is unchanged. So I decided to use a box-cox transformation. below is the qq-plot of the original data http://n4.nabble.com/file/n1011015/start%2Bvalue%2Bproblem%2B02.jpeg Note that the min of my data is -1099, so I add a fix value 1200 to the original sample. I choose the

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

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 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)

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

This is a question about the use of the Cox proportional hazards model to analyze event histories. I am looking at the responses of sympathetic nervous system activity to a stimulus. The activity I observe is a burst that can only occur once per heart beat cycle (e.g., a binary count). Typically bursts occur in 60-80% of the heart cycles * sensory stimuli can modify these burst probabilities.

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, 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 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,

Hello, i'm a french student of medical oncology and i'm working on breast cancer. I have a variable with the histologic type of tumor wich is between 1 and 5. I use as.factor function to make some variable with level between 1 and 5. When i put it in the cox model i have only the level between 2 and 5. The level 1 doesn't appear. I think i have to change the number of level but i

Hi, I'm reading van Belle et al "Biostatistics" and trying to run a cox test using a dataset from: http://faculty.washington.edu/~heagerty/Books/Biostatistics/chapter16.html (Primary Biliary Cirrhosis data link at top of the page), I'm using the following code: --------------- start of code library(survival) liver <-

Hello All, Does anyone know where I can find information about how to do a power analysis for Cox regression with a time-varying covariate using R or some other readily available software? I've done some searching online but haven't found anything. Thanks, Paul

Hi, Storer and Crowley (JASA 1985) presented an approach for approximating the changes in maximum partial-likelihood parameter estimates for the Cox model when a single observation is deleted. Is there an R implementation of this approach? Any help is greatly appreciated. Thanks. Best, Ravi. ---------------------------------------------------------------------------- ------- Ravi

dears, I like two write a program with R to estimate the coefficients of covariate,I like two know the original program for this programing for partial maximum likelihood for cox models with two co variate. I did it with coxph command, thanks [[alternative HTML version deleted]]

Hi, I'm using a Cox-Regression to estimate hazard rates on prepayments. I'm using the "pspline" function to face non-linearity, but I have no clue how to interpret the result. Unfortunately I did not find enough information on the "pspline" function wether in the survival package nor using google.. I got following output: * library(survival)* > > > >