similar to: negative binomial lmer

To whom it may concern: I recently used lmer (for non-normally distributed data and mixed effects, using the Laplace method). All 3 of my fixed effects were categorical, including two ordered factors and one unordered factor. In my tables, I currently report the number of observations for the response variable, and both the degrees of freedom and Chi Square values from tests of reduced

Dear R users, When running the program below I receive the following error message: fit <- optim(parm, objective, yt = tyield, hessian = TRUE) Error in as.vector(data) : no method for coercing this S4 class to a vector I can't figure out what the problem is exactly. I imagine that it has something to do with "tyield" being a matrix. Any help on explaining what's going on

I''ve setup two models, 1 and 2, that are associated by has_and_belongs_to_many. I''m trying to get an attribute from model_1 to use in a method in model_2. When I use the code below, I get an error saying ''undefined method model_1_id''. What am I missing? Thanks! Model_2.rb Class Model_2 < ActiveRecord::Base ... has_and_belongs_to_many :model_1 def

Hi, I'm trying to estimate the parameters of a state space model of the following form measurement eq: z_t = a + b*y_t + eps_t transition eq y_t+h = (I -exp(-hL))theta + exp(-hL)y_t+ eta_{t+h}. The problem is that the distribution of the innovations of the transition equation depend on the previous value of the state variable. To be exact: y_t|y_{t-1} ~N(mu, Q_t) where Q is a diagonal

Dear R-helpers, I try to perform glm's with negative binomial distributed data. So I use the MASS library and the commands: model_1 = glm.nb(response ~ y1 + y2 + ...+ yi, data = data.frame) and predict(model_1, newdata = data.frame) So far, I think everything should be ok. But when I want to perform a glm with a subset of the data, I run into an error message as soon as I want to predict

Hello, For a simple problem with 1 predictor (x) and 2 classes (0 and 1), the linear discriminant function should be something like 2(mu_0 - mu_1)/var x + x-independent-terms where var is the common variance.

Dear all, I am trying to evaluate the optimisation behaviour of a function. Originally I have optimised a model with real data and got a set of parameters. Now I am creating simulated data sets based on these estimates. With these simulations I am estimating the parameters again to see how variable the estimation is. To this end I have written a loop which should generate a new simulated data

Hello, Is there a built-in test in R for hypothesis testing with samples of known variance? For example, I've got a set of data, a mean to compare against, and a known variance, and I want to determine the p-value for which I can reject the null hypothesis (mu_1 = mu_0) and accept the alternative (mu_1 > mu_0). I've found that JMP and Minitab can both do this (in JMP, it's a

Dear R-helpers, let us assume, that I have the following dataset: a <- rnbinom(200, 1, 0.5) b <- (1:200) c <- (30:229) d <- rep(c("q", "r", "s", "t"), rep(50,4)) data_frame <- data.frame(a,b,c,d) In a first step I run a glm.nb (full code is given at the end of this mail) and want to predict my response variable a. In a second step, I would

I discovered recently that the phrase "Oja median" produces no hits in Jonathan Baron's very valuable R search engine. I found this surprising since I've long regarded this idea as one of the more interesting notions in the multivariate robustness literature. To begin to remedy this oversight I wrote a bivariate version and then decided that writing a general p-variate version

Dear R helpers, I have the following model model_1<-glm(y~A+B+C+E+A:D,contrasts=list(D=contrasts_D),data=mydata,na.action=na.omit) with: options(contrasts=c("contr.sum", "contr.poly")) A,B and E are 2-levels factor, C is covariate, D is 20 levels factor with 10 in relation with the first levels of factor A, the other in relation with the second levels of factor A

Hi R users, The nlme library enabled several alternate error structures useful for longitudinal or repeated-measures data. For example, a continuous AR(1) process: model_2 = update(model_1, correlation = corCAR1(form = ~ time | subject)) Does anybody know if this is available in lme4? Thank you Ben -- View this message in context:

Hi Folks, Can anyone give me the tip I've been groping for with the following question:? mu: kx2x2x2 array of reals corresponding to means of k RVs at the combinations of values (1,2)x(1,2)x(1,2) of dichotomous variables F1,F2,F3 mu prints out as k rows (one for each Xi) of 8 numbers M: N x (3+k) matrix of cases. The first 3 cols are values of

> It might clarify your thinking to note that a seasonal ARIMA model > is just an ``ordinary'' ARIMA model with some coefficients > constrained to be 0 in an efficient way. E.g. a seasonal AR(1) s = > 4 model is the same as an ordinary (nonseasonal) AR(4) model with > coefficients theta_1, theta_2, and theta_3 constrained to be 0. You > can get the same answer as from

Hi you guys: I have been wondering if there is any way to change the labeling in plot.ts( ), for example , if I plot two sequences, i always got y labels as "series1", "series2", I tried to use ylab=c((expression(mu_1)),(expression(mu_2))), but it doesn't work. thanks in advance for any help best.

Dear R People: I have a few questions about multiple comparisons and contrasts for ANOVA, please. I've tried some things but with no success. Suppose I have a completely randomized design, and I want to have the contrast \mu_1 - 0.5 \mu_2 - 0.5 \mu_2 How do I set that up, please? I used the C command, and ran aov, but the results were identical to those with no contrasts. Also, is there

To whom it may concern: I am using R version 1.9.1 beta on a windows machine, and I am trying to use a function called lmer (in packages lme4 and Matrix). I am not sure if this version will support these packages (when I install them from the GUI, the function lmer does not work). I wondered if I need to install a newer version of R, and if so, which version should I install, and how should I

Hello, I have some parameters from Mclust function. The parameters are in the form *parametersDf * * mu_1 mu_2 var_mc1 var_mc2 c1 c2 * *2 1.357283 2.962736 0.466154 0.1320129 0.5258975 0.4741025 * *21 8.357283 9.962736 0.466154 0.1320129 0.5258975 0.4741025 * Each row in the above data frame

Dear All, I used lmer for data with non-normally distributed error and both fixed and random effects. I tried to calculate a "Type III" sums of squares result, by I conducting likelihood ratio tests of the full model against a model reduced by one variable at a time (for each variable separately). These tests gave appropriate degrees of freedom for each of the two fixed effects, but

To whom it may concern: I am using lmer for a statistical model that includes non-normally distributed data and random effects. I used this same function in the most recent version of R as of fall 2005, and have re-done some of the same analyses using all of the same files, but with the newest version of R (2.2.1). I get answers that are not exactly the same (although I do get the same