similar to: help with lme function

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

Hello, I have a simulated data structure in which students are nested within teachers, and with each student are associated two test scores. There are 20 classrooms and 25 students per classroom, for a total of 500 students and two scores per student. Here are the first 10 lines of my dataframe "d": studid tchid Y time 1 1 1 -1.0833222 0 2 1 1

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 List: My question is more statistical than R oriented (although it originates from my work with nlme). I know statistical questions are occasionally posted, so I hope my question is relevant to the list as I cannot turn up a solution anywhere else. I will frame it in the context of an R related issue. To illustrate the problem, consider student achievement test score data with multiple

Hi R-helpers, I'm new to time series modelling, but my requirement seems to fall just outside the capabilities of the arima function in R. I'd like to fit an ARMA model where the variance of the disturbances is a function of some exogenous variable. So something like: Y_t = a_0 + a_1 * Y_(t-1) +...+ a_p * Y_(t-p) + b_1 * e_(t-1) +...+ b_q * e_(t-q) + e_t, where e_t ~ N(0, sigma^2_t),

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

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 have constructed the function mml2 (below) based on the likelihood function described in the minimal latex I have pasted below for anyone who wants to look at it. This function finds parameter estimates for a basic Rasch (IRT) model. Using the function without the gradient, using either nlminb or optim returns the correct parameter estimates and, in the case of optim, the correct standard

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

I have a dataset which I want to model using a Poisson distribution, with a given parameter. I would like to know what is the proper way to do a ''goodness of fit'' test using R. I know the steps I''d take if I were to do it ''manually'': grouping the numbers into classes, calculating the expected frequencies using ''ppois'', then

Greetings, The following question has come up in an off-list discussion. Is it possible to construct a regular expression 'rex' out of two given regular expressions 'rex1' and 'rex2', such that a character string X matches 'rex' if and only if X matches 'rex1' AND X does not match 'rex2'? The desired end result can be achieved by logically combining

I'm fitting a GLMM to some questionnaire data. The structure is J individuals, nested within I areas, all of whom answer the same K (ordinal) questions. The model I'm using is based on so-called continuation ratios, so that it can be fitted using the lme4 package. The lmer function fits the model just fine, but using mcmcsamp to judge the variability of the parameter estimates produces

Dear all, Given a LME model (following the notation of Pinheiro and Bates 2000) y_i = X_i*beta + Z_i*b_i + e_i, is it possible to extract the variance-covariance matrix for the estimated beta_i hat and b_i hat from the lme fitted object? The reason for needing this is because I want to have interval prediction on the predicted values (at level = 0:1). The "predict.lme" seems to

Hello! I would like to use lmer() to fit data, which are some estimates and their standard errors i.e kind of a "meta" analysis. I wonder if weights argument is the right one to use to include uncertainty (standard errors) of "data" into the model. I would like to use lmer(), since I would like to have a "freedom" in modeling, if this is at all possible. For

hi! i want to change the "robustness weights" used by loess. these are described on page 316 of chambers and hastie's "statistical models in S" book as r_i = B(e_i,6m) where B is tukey's biweight function, e_i are the residulas, and m is the median average distance from 0 of the residuals. i want to change 6m to, say, 3m. is there a way to do this? i cant

I've been using lme4 to fit Poisson GLMMs with crossed random effects. The data are counts(y) sampled at 55 sites over 4 (n=12) or 5 (n=43) years. Most models use three fixed effects: x1 is a two level factor; x2 and x3 are continuous. We are including random intercepts for YEAR and SITE. On subject-matter considerations, we are also including a random coefficient for x3 within YEAR.

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 list: I'm hoping to tap in to the statistical expertise in the group, especially those familiar with simulation techniques. I'm finalizing a study where I obtain standard errors from two sources. The first source is a monte carlo simulation and the other source is an analytical model I have developed that appears to recover the standard errors from the simulation. All analysis are

Hello, I am interested in estimating this type of random effects panel: y_it = x'_it * beta + u_it + e_it u_it = rho * u_it-1 + d_it rho belongs to (-1, 1) where: u and e are independently normally zero-mean distributed. d is also independently normally zero-mean distributed. So, I want random effects for group i to be correlated in t, following an AR(1) process. Any idea of how

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