similar to: labels

Dear Spencer and Prof. Fox, Thank you for your replies. I'll very appreciate, if you have any ideas concerning the problem described below. First, I'd like to describe the model in brief. In general I consider a model with three equations. First one is for annual GRP growth - in general it looks like: 1) GRP growth per capita = G(investment, migration, initial GRP per

Hi, I wanted to know how to compute the correlation ratio (eta) between two variables using R. Is there any function to compute the correlation ratio. Any help will be very much appreciated. Thanks, Suman [[alternative HTML version deleted]]

Salut alex. J''ai une question a propos de wx::statictext(wxruby2) est-il possible de mettre les accents pour la langue française?, car pour l''instant je ne peut pas mettre l''accentuation alors que ça marche bien avec "wxruby". merci Sebastien _______________________________________________ wxruby-users mailing list wxruby-users@rubyforge.org

Un texte encapsul? et encod? dans un jeu de caract?res inconnu a ?t? nettoy?... Nom : non disponible Url : https://stat.ethz.ch/pipermail/r-help/attachments/20080302/86a07850/attachment.pl

hi everybody, when trying to add rows to a join table i get the following error : sequence doesn''t exist here are the models used class Photo < ActiveRecord::Base has_many :endroits_photos, :dependent => true has_many :endroits_connus, :through => :endroits_photos end class EndroitsPhoto < ActiveRecord::Base belongs_to :photo belongs_to

Dear R Help, I am trying to fit a nonlinear model for a mean function $\mu(Data_i, \beta)$ for a fixed covariance matrix where $\beta$ and $\mu$ are low- dimensional. More specifically, for fixed variance-covariance matrices $\Sigma_{z=0}$ and $\Sigma_{z=1}$ (according to a binary covariate $Z $), I am trying to minimize: $\sum_{i=1^n} (Y_i-\mu_(Data_i,\beta))' \Sigma_{z=z_i}^{-1} (Y_i-

Hello, I have a n*2 matrix, called "plan", which contains n observations from 2 variates. I want a kernel density estimate of the joint distribution of these 2 variates. I try : density(plan). Unfortunately, R thinks there is 2n observations (if n=10, 20 observations), where there is only n. How to to make a multivariate kernel density estimate ? Thank you very much.

dear R wizards: I would like to write a function that roughly places the equivalent of the following latex text into the current plot: \newcommand{ \placesigma }[4]{ \put(\#1,\#2){ \sigma_{A , #3} = #4 } I cannot figure out how to do this. I know I have to use a function that uses expressions in a text() invoke. But passing arguments and nesting strings and expressions has so far not

Hi, I am working with the lattice package and I want to label to groups in the xyplots with mathematical expressions. I short example for this library(lattice) Case<-factor(rep(1:2,each=300)) xx<-rnorm(600,0,1) yy<-rnorm(600,0,1) xyplot(yy~xx|Case) This results in two scatter plots with label "1" and "2". I would like to substitute this labels by math expression. I

Hello, I need to import a data set. I have never imported data files with R. I have always worked on simulated data. I have looked at R Data Import/Export manual. It is a bit peculiar because my data base is already an R object called "japan". I guess it is not yet a data set, and I don't know how to manipulate variables from it. When I type "japan", here is an extract

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

Hello - Here's what I'm trying to do. I want to fit a time series y with ARMA(1,1) + GARCH(1,1), there are also an exogeneous variable x which I wish to include, so the whole equation looks like: y_t - \phi y_{t-1} = \sigma_t \epsilon_t + \theta \sigma_{t-1} \epsilon_{t-1} + c x_t where \epsilon_t are i.i.d. random variables \sigma_t^2 = omega + \alpha \sigma_{t-1}^2 + \beta

I use smooth.spline(x, y) in package modreg and I would like to get value of penalized log likelihood and preferable also its two parts. To make clear what I am asking for (and make sure that I am asking for the right thing) I clarify my problem trying to use the same notation as in help(smooth.spline): I want to find the natural cubic spline f(x) such that L(f) = \sum_{k=1}{n} w[k](y[k] -

Please pardon an extremely naive question. I see related earlier posts, but no responses which answer my particular question. In general, I'm very confused about how to do variance decomposition with random and mixed effects. Pointers to good tutorials or texts would be greatly appreciated. To give a specific example, page 193 of V&R, 3d Edition, illustrates using raov assuming pure

Hi everybody, I am currently working with R and I would like to create jpeg graphs with it. I am working on Windows and Unix but I would like to be able to create graphs (jpeg, png, bitmap...) under Unix. I am working on Solaris version 8. The documentation for R states that the latest version of or R known to compile on Solaris 8 is version 2.6.2. I have been able to compile and install R

*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} ~

hi R-help! I would like to know a simple and easy way (if posible) to do the following data manipulation. I have a matrix of experimental data (with replicae in time) >experiment<-data.frame(times=c(0,0,10,10,20,20,30,30),expval=c(1,1,2,2,3,3,4,4)) > experiment times expval 1 0 1 2 0 1 3 10 2 4 10 2 5 20 3 6 20 3 7 30 4 8

I am trying to run this loop but it keeps coming up with the following error: 'Error: no function to return from, jumping to top level' n.simul <- 1000 A <- {for(i in 1:12){{ nT <- rpois(n.simul, T[i]) nP <- rpois(n.simul, P[i]) nD <- rpois(n.simul, D[i]) nC <- rbinom(n.simul, nT, C[i]) Score <- (5*nT + 2*nC + 3*nP + 3*nD) } return(Score)}} My aim is to calculate

Hello, I have a question on export and import data. Thank you for any suggestions. data 'simul' is generated as follows: N <- 20 n <- N/2 nsets <- 10 simul <- matrix(0,nsets,N) th <- c(0,1, 1) for(i in 1:nsets){ simul[i,] <- rnorm(N,mean= rep(th[1:2],N/2),sd=th[3]) } I exported data as follows: write.table(simul,