similar to: mlpowsim

Dear All, I'm trying to model heteroscedasticity using a multilevel model. To do so, I make use of the nlme package and the weigths-parameter. Let's say that I hypothesize that the exam score of students (normexam) is influenced by their score on a standardized LR test (standLRT). Students are of course nested in "schools". These variables are contained in the

Hi, I would like to apply the L-BFGS optimization algorithm to compute the MLE of a multilevel multinomial Logistic Regression. The likelihood formula for this model has as one of the summands the formula for computing the likelihood of an ordinary (single-level) multinomial logit regression. So I would basically need the R implementation for this formula. The L-BFGS algorithm also requires

How does one implement a likelihood-ratio test, to test whether the variances of the random effects differ between two groups of subjects? Suppose your data consist of repeated measures on subjects belonging to two groups, say boys and girls, and you are fitting a linear mixed-effects model for the response as a function of time. The within-subject errors (residuals) have the same variance in

Despite lots of investigation, I haven't found any R packages might be suitable for the following problem. I'd be very grateful for suggestions. I have three-way nested data, with a series of measures (obs) taken in quick succession (equal time spacing) from each subject on different days. The measures taken on the same day are temporally correlated, so I'd like to use an AR1

Dear friends. Please see the program below and answer if it does simulate a population of 1.000.000 families, each with at max 20000 children (typical in Denmark, you know), constructed such that each family stops having children when more boys than girls are present ? Equal numbers of boys and girls are got in the population, according to the simulation, is that obvious ? ND <- NP <-

Dear R-users, Is there an R function to compute the multinomial beta function? That is, the normalizing constant that arises in a Dirichlet distribution. For example, with three parameters the beta function is Beta(n1,n2,n2) = Gamma(n1)*Gamma(n2)*Gamma(n3)/Gamma(n1+n2+n3) Thanks in advance for any assisstance. Regards, Greg [[alternative HTML version deleted]]

Hi dear R users, 1) I would like to know if there is a simple way to define a vglm family which would be a mix of poisson variables and bernoulli variables (0/1 response) for idea this would be invoked like this: vglm(...,family=mixpoissonmultinom(npoisson,n01response)) where the n's give the number of each type of response. 2) and a simpler question : How to use constraints in rrvglm?

Hi, i am trying to learn something about multilevel analysis using a great "Discovering statistics using R". I constructed some sample data and then tried to fit a model. Generally model fits well, however when trying to fit the same model using z-score (standarizded) variables i got an error: Error in lme.formula(z_wyn ~ z_IQ + Kasa, data = la, random = ~z_IQ | : nlminb problem,

I am new to R and would like to get some practice analyzing multilevel data. I wonder if anyone can point me to a sample data set and command lines that I might replicate for a sample session. I would then compare my output with HLM output. Any help is appreciated. ------ Harold C. Doran Director of Research and Evaluation New American Schools 675 N. Washington Street, Suite 220 Alexandria,

I'm using an R program (which I did not write) to simulate multilevel data (subjects in locations) used in power calculations. It uses lmer to fit a mixed logistic model to the simulated data based on inputs of means, variances, slopes and proportions: ? (fitmodel <- lmer(modelformula,data,family=binomial(link=logit),nAGQ=1)) where modelformula is set up in another part of the program.?

Hi, lme() users, Can some one tell me how to do this. I model Orthodont with the same G for random variables, but different R{i}'s for boys and girls, so that I can get sigma1_square_hat for boys and sigma2_square_hat for girls. The model is Y{i}=X{i}beta + Z{i}b + e{i} b ~ iid N(0,G) and e{i} ~ iid N(0,R{i}) i=1,2 orth.lme <- lme(distance ~ Sex * age, data=Orthodont, random=~age|Subject,

Dear R-group, Could somebody recommend a package that can deal with a multinomial response variable (choice of breeding tactic in mice, which has four unordered levels), multiply-imputed data (generated using the Amelia package) and two non-nested random effects: individual identity (133 individuals made up to four choices each) and year (for which there are six levels and sample size varies

Hi, I am working with a multiple-item questionnaire. I have previously done item-level multiple imputation using MICE in R and right now I am attempting duplicate-scale imputation based on the guidelines listed in Enders's applied missing data analysis book. I use MICE to do MI as it allows me to specify school effect as I am working with multilevel data; my respondents come from different

The Institute for Social Research (ISR) and its Statistical Consulting Service (SCS) at York University are pleased to announce our Summer Program In Data Analysis (SPIDA) for 2012. The Program runs from May 24th to June 1st, 2012.This year?s Program focuses on the theory and practice of linear models and mixed [or multilevel] models, as they are applied to hierarchical and longitudinal data.

Dear R help, I am trying solve an MLE convergence problem: I would like to estimate four parameters, p1, p2, mu1, mu2, which relate to the probabilities, P1, P2, P3, of a multinomial (trinomial) distribution. I am using the mle2() function and feeding it a time series dataset composed of four columns: time point, number of successes in category 1, number of successes in category 2, and

In our recent workshop on "Multilevel Modeling in R" we discussed handling data for multilevel modeling. An classic example of such data are test scores of students grouped into schools. We may wish to model the scores as functions of both student-level covariates and school-level covariates. Such data are often organized in a multi-table format with a separate table for each level of

Hello R users, I have a puzzle with the VGAM package, on my first excursion into generalized additive models, in that this very nice package seems to want to do either more or less than what I want. Precisely, I have a 4-component outcome, y, and am fitting multinomial logistic regression with one predictor x. What I would like to find out is, is there a single nonlinear function f(x) which acts

Hello, I am using R to analyze a large multilevel data set, using lmer() to model my data, and using anova() to compare the fit of various models. When I run two models, the output of each model is generated correctly as far as I can tell (e.g. summary(f1) and summary(f2) for the multilevel model output look perfectly reasonable), and in this case (see below) predictor.1 explains vastly more

Dear All, Could we specify the multinomial distribution in R when doing Bayesian data analysis through R2WinBUGS? See below for my issues. t2[i,1:2] ~ dmulti(p2[i,1:2],n2[i]) #t2[] is a matrix with two columns, the problem is how to specify it in R p2[i,1] <- (p[i] * s1[t[i]] * s2[t[i]] + (1 - p[i]) * (1 - c1[t[i]]) * (1 - c2[t[i]]))/p1[i] p2[i,2] <- (p[i] * s1[t[i]] * (1 - s2[t[i]]) +(1 -

hi, I've been developing vorbis Library for Java. I decided to do my own rather than use Jorbis. But i haven't found good Documentation from third vorbis header were those books etc. are. What should i do with them. What data can i find from there and how is it useful to me:) i have readed code from jorbis to fullfill some lacks of documetation but this is too difficult to figure out..