similar to: PROC MIXED user trying to use (n)lme...

Hi all, I have some difficulties with the lme function and so this is my problem. Supoose i have the following model y_(ijk)=beta_j + e_i + epsilon_(ijk) where beta_j are fixed effects, e_i is a random effect and epsilon_(ijk) is the error. If i want to estimate a such model, i execute >lme(y~vec.J , random~1 |vec .I ) where y is the vector of my data, vec.J is a factor object

HI, I would like to extract the variance components estimation in lme function like a.fit<-lme(distance~age, data=aaa, random=~day/subject) There should be three variances \sigma_day, \sigma_{day %in% subject } and \sigma_e. I can extract the \sigma_e using something like a.fit$var. However, I cannot manage to extract the first two variance components. I can only see the results in

Dear all I need to run an anova from a factorial model y_{ijk}=\alpha_i+\beta_j+(\alpha\beta)_{ij}+e_{ijk} and calculate type II and III sums of square, but I have an empty cells, so anova function from package car fail. (I believe) y<-c(7,13,6,10,8,11,8,3,7,5,65) a<-as.factor(c(1,1,2,2,3,3,3,1,1,1,2)) b<-as.factor( c(rep(1,7),rep(2,4)) ) table(b,a) # cell (2,3) is empty

Dear helpers in this forum, I have the following question: Suppose I have the following data set: id x y 023 1 2 023 2 5 023 4 6 023 5 7 412 2 5 412 3 4 412 4 6 412 7 9 220 5 7 220 4 8 220 9 8 ...... and i want to calculate sum_{i=1}^k sum_{j=1}^{n_i}x_{ij}*y_{ij} is there a simple way to do this within and between subject summation in R?

Dear all, I am trying to repeat an example from Sokal and Rohlfs "Biometry" -- Box 11.4, example of a randomized-complete-blocks experiment. The data is fairly simple: series genotype weight 1 pp 0.958 1 pb 0.985 1 bb 0.925 2 pp 0.971 2 pb 1.051 2 bb 0.952 3 pp 0.927 3 pb 0.891 3 bb 0.892 4

Dear helpers in this forum, This is a clarified version of my previous questions in this forum. I really need your generous help on this issue. > Suppose I have the following data set: > > id x y > 023 1 2 > 023 2 5 > 023 4 6 > 023 5 7 > 412 2 5 > 412 3 4 > 412 4 6 > 412 7 9 > 220 5 7 > 220 4 8 > 220 9 8 > ...... > Now I want to compute the

Dear R developers, I was looking for a function analogous to base::diag() for getting and setting bands of a matrix. The closest I could find was Matrix::band(), but this was not exactly what I wanted for two reasons. Firstly, Matrix::band() returns a matrix rather than just the specified band. Secondly, Matrix::band() cannot be used for setting the values for a matrix band. Setting or

Dear all, As our previous email did not get any response, we try again with a reformulated question! We are trying to do something which needs an efficient loop over a huge array, possibly functions such as apply and related (tapply, lapply...?), but can't really understand syntax and examples in practice...i.e. cant' make it work. to be more specific: we are trying to apply a mask

I am out of my league with this question. The following code starts the java imaging program ImageJ from within R, and displays an image (assuming ImageJ is installed on your computer). library(RImageJ) img <- IJ$openImage( file.choose() ) #pick an available .tif file img$show() # make the image object visible # An image is now displayed # find out about the objects involved >

I need to refresh my memory on Probability Theory, especially on conditional probability. In particular, I want to solve the following two problems. Can somebody point me some good books on Probability Theory? Thank you! 1. Z=X+Y, where X and Y are independent random variables and their distributions are known. Now, I want to compute E(X | Z = z). 2.Suppose that I have $I \times J$ random number

Hi, I am trying to parameterize the following mixed model (following Piepho and Ogutu 2002), to test for a trend over time, using multiple sites: y[ij]=mu+b[j]+a[i]+w[j]*(beta +t[i])+c[ij] where: y[ij]= a response variable at site i and year j mu = fixed intercept Beta=fixed slope w[j]=constant representing the jth year (covariate) b[j]=random effect of jth year, iid N(0,sigma2[b]) a[i]=random

*Dear R-help: My name is Rodrigo and I have a question with nlme package in R to fit a mixed beta regression model. The details of the model are: Suppose that:* *j in {1, ..., J}* *(level 1)* *i in {1, ..., n_j}* *(level 2)* *y_{ij} ~ Beta(mu_{ij} * phi_{ij}; (1 - mu_{ij}) * phi_{ij}) y_{ij} = mu_{ij} + w_{ij} * *with* *logit(mu_{ij}) = Beta_{0i} + Beta_{1i} * x1_{ij} + b2 * x2_{ij}

*Dear R-help: My name is Rodrigo and I have a question with nlme package in R to fit a mixed beta regression model. I'm so sorry. In the last email, I forgot to say that W is also a unknown parameter in the mixed beta regression model. In any case, here I send you the correct formulation. ** Suppose that:* *j in {1, ..., J}* *(level 1)* *i in {1, ..., n_j}* *(level 2)* *y_{ij} ~

Dear People, I have a vexing problem related to pseudo-random number generation, and would appreciate any help and advice. This problem is not directly related to R, and the only reason I am posting it to this list is that my implementation is using R. Let me describe my problem by giving an example, that is close to what I am trying to do. Suppose we are given a stream of pseudo-random numbers,

Hello, I have a R code for doing convolution of two functions: convolveSlow <- function(x, y) { nx <- length(x); ny <- length(y) xy <- numeric(nx + ny - 1) for(i in seq(length = nx)) { xi <- x[[i]] for(j in seq(length = ny)) { ij <- i+j-1 xy[[ij]] <- xy[[ij]] + xi * y[[j]] } } xy } How do I reduce the 2

How does one define PDFs as yet undefined in R, such as the ex- gaussian, the sum of two RVs, one exponential, one Gaussian? The PDF would then be the convolution of an exponential PDF, dexp(), and a normal, dnorm(). Kindly cc me in your reply to r-help. Thanks, _____________________________ Professor Michael Kubovy University of Virginia Department of Psychology USPS: P.O.Box 400400

Per the message from Alexander Nervedi, 29 April 2006: > I have to be making a riddiculously silly ommission. > when I run the fillowing i get the cloud plot ok. But I cant figure > out what I am missing out when I call wireframe. > Any help would be appreciated. > x<-runif(100) > y<-rnorm(100) > z<-runif(100) > temp <-data.frame(x,y,z) >

Dear all I would like to ask you if an assignment can be done inside a lapply statement. For example I would like to covert a double nested for loop for (i in c(1:dimx)){ for (j in c(1:dimy)){ Powermap[i,j] <- Pr(c(i,j),c(PRX,PRY),f) } } to something like that: ij<-expand.grid(i=seq(1:dimx),j=(1:dimy)) unlist(lapply(1:nrow(ij),function(rowId) { return

--- El lun, 12/9/11, Cristalina <pa100cia77@yahoo.es> escribió: De: Cristalina <pa100cia77@yahoo.es> Asunto: Re: [R-es] Cosinor Analysis Para: "Carlos Ortega" <coforfe@gmail.com> Fecha: lunes, 12 de septiembre, 2011 08:43 Hola,   Carlos, muchas gracias.   El método empleado en http://tolstoy.newcastle.edu.au/R/e6/help/09/01/0626.html (el url que se referencia

Hi there, I am looking to fit a two-way random effects model to an *unblalanced* layout, y_ijk = mu + a_i + b_j + eps_ijk, i=1,...,R, j=1,...,C, k=1,...,K_ij. I am interested first of all in estimates for the variance components, sigsq_a, sigsq_b and sigsq_error. In the balanced case, there are simple (MM, MLE) estimates for these; In the unbalanced setup,