similar to: help with pseudo-random numbers

The compositional data xi=(x_i1,x_i2,...,x_in), for each fixed i , xij>0, and sum(xij)=1; I want to compare the mean( u_i) of several groups i.e. H0: u_1=u_2=...=u_N or H0: u_11=u_21=...=u_N1 Are there any ANOVA tpye tools to do this work in R? Thanks, WEN S Q [[alternative HTML version deleted]]

The compositional data xi=(x_i1, x_i2,..., x_in), for each fixed i , xij>0, and sum(xij)=1; I want to compare the mean( u_i) of several groups i.e. H0: u_1=u_2=...=u_N or Hj0: u_1j=u_2j=...=u_Nj Are there any ANOVA tpye tools to do this work in R? Thanks, WEN S Q [[alternative HTML version deleted]]

Dear R users, I am a mathematics postdoc at UC Berkeley. I have written a package in a Computational Algebra System named Singular http://www.singular.uni-kl.de to compute the Maximum Likelihood of a given probability distribution over several discrete random variables. This package gives exact answers to the problem. But more importantly, it gives All MLE solutions. My understanding is that

Dear R-users Coming from a proc mixed (SAS) background I am trying to get into the use of (n)lme. In this connection, I have some (presumably stupid) questions which I am sure someone out there can answer: 1) With proc mixed it is easy to get a hold on the estimated variance parameters as they can be put out into a SAS data set. How do I do the same with lme-objects? For example, I can see the

Hi Folks, I am dealing with data which have been presented as at each x_i, mean m_i of the y-values at x_i, sd s_i of the y-values at x_i number n_i of the y-values at x_i and I want to linearly regress y on x. There does not seem to be an option to 'lm' which can deal with such data directly, though the regression problem could be algebraically

*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 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

Greetings, I have the following? Problem: Given k (=10) discrete independent random variables X_i with n_i (= 5 to 20) values each,compute quantiles of the distribution of the sum X = X_1+...+X_k. Here X has n=n_1 x n_2 ... n_k distinct values which is too large to list them all together with their probabilities. I tried several approaches: (A) Convolution: each X_j is approximated with

Hello, Great to see the new metafor package for meta-analysis. I would like to perform a meta-analysis in which I initially calculate the intercept of the model with a nested random-effects structure. In lme, this would be model<- lme(v3~1, random=~1|species/study, weights = varFixed(~Wt), method = "REML") where multiple effects sizes are measured for some studies and more than

Dear Carlos, please refrain from posting the same question umpteen times. Please consider that code is hard to read and people might not have the time to run your simulation etc. etc.. As I told you privately in response to your message on 18/1, > Re: generating correlated effects, I tried this only once, but I > didn't get it right. Simulations using this are, e.g., Hansen (2007)

In a classical meta analysis model y_i = X_i * beta_i + e_i, data {y_i} are assumed to be independent effect sizes. However, I'm encountering the following two scenarios: (1) Each source has multiple effect sizes, thus {y_i} are not fully independent with each other. (2) Each source has multiple effect sizes, each of the effect size from a source can be categorized as one of a factor levels

Hi Is there any way to fit a bivaraite longitudinal mixed model using R. I have a data set with col names resp1 (Y_ij1), resp2 (Y_ij2), timepts (t_ij), unit(i) j=1,2,..,m and i=1,2,..n. I want to fit the following two models Model 1 Y_ij1, Y_ij2 | U_i = u_i ~ N(alpha + u_i + beta1*t_ij, Sigma) U_i ~ iid N(0, sigu^2) Sigma = bivariate AR structure alpha and beta are vectors of order 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

i want to ask 2 questions. 1) can R do Random-effects GLS regression which i can get from Stata? the following result is frome Stata.can I get the alike result from R? xtreg lwage educ black hisp exper expersq married union, re Random-effects GLS regression Number of obs = 4360 Group variable (i) : nr Number of groups = 545 R-sq:

Dear R users, I am using the function "constrOptim" to minimize the -1*log-likelihood where \beta_i>=0 i=1,...,p and \beta_0 is unconstrained. I construct u_i as 0 0 0 ... 0 0 1 0 ... 0 0 0 1 ... 0 . . . ... 0 . . . ... 0 .

Dear R-users: Recently I found my simulation run into an apparently infinite loop. After a few days of tracing and chasing, I believe it is caused by the built-in unif_rand() and exp_rand() functions: unif_rand() can produce a value of 0 which causes the following part of exp_rand() running into an infinity loop u = unif_rand(); for (;;) { u += u; if (u > 1.0) break;

Hallo everybody, I'm new in r and I"ll appreciate some help! I have a matrix of nrow=30 and ncoll=54,and I would like to generate 50 simulations with tha same size of the matrix!!!That is to say that I want to generate 50 matrices -for my 50 simulations - with the same dimensions! I took my 1st matrix according to the formula that I want to implement: D<-mean_m + U_i*mat_DELTA

This is a relatively recent article that is somewhat accessible. Jensen, D. R., and Solomon, Herbert (1994), "Approximations to joint distributions of definite quadratic forms", Journal of the American Statistical Association, 89 , 480-486 It has references to previous work. I also have an old paper that is so old I can't tell what journal it came out of:( Grad, Arthur and Solomon,

Dear R users, I am writing to you because I have a few question on how to fix the error term variances in lme in the hope that you could help me. To my knowledge, the closest possibility is to fix the var-cov structure, but not the whole var-cov matrix. I found an old thread (a few years ago) about this, and it seems that the only alternative is to write the likelihood down and use optim or a