similar to: How to test combined effects?

Hello. A quick question about incorporating variation due to study in the metafor package. I'm working with a particular data set for meta-analysis where some studies have multiple measurements. Others do not. So, let's say the effect I'm looking at is response to two different kinds of drug treatment - let's call their effect sizes T1 and T2. Some studies have multiple

Sorry, the authentication problem still wasn't actually fixed in beta6. Now, this time I tested every possible authentication case that it really works. I'll soon create a CVS branch which is going to stabilize into the 1.0 release. I won't add new features there so it shouldn't really get broken anymore, at least because of new features.. So, two changes in this release:

Sorry, the authentication problem still wasn't actually fixed in beta6. Now, this time I tested every possible authentication case that it really works. I'll soon create a CVS branch which is going to stabilize into the 1.0 release. I won't add new features there so it shouldn't really get broken anymore, at least because of new features.. So, two changes in this release:

Hello, I tried to do a 'sem' analysis for data of how blueberry consumption by birds is influenced by a pollution gradient, using distance and vegetation structural and composition variables, but I got the following error message: Error in sem.default(ram = ram, S = S, N = N, param.names = pars, var.names = vars, : S must be a square triangular or symmetric matrix This may be very

Hi, how to estimate a the following model in R: y(t)=beta0+beta1*x1(t)+beta2*x2(t)+...+beta5*x5(t)+beta6*y(t-1)+beta7*y(t-2)+beta8*y(t-3) 1) using "lm" : dates <- as.Date(data.df[,1]) selection<-which(dates>=as.Date("1986-1-1") & dates<=as.Date("2007-12-31")) dep <- ts(data.df[selection,c("dep")]) indep.ret1

Hi, I was wondering if I might be able to ask some advice about doing residual plots for the lmer function in the lme4 package. Our group's aim is to find if the expression staining of a particular gene in a sample (or "core") is related to the pathology of the core. To do this, we used the lmer function to perform a logistic mixed model below. I apologise in advance

Hi guys again, it seems I haven't been doing the maximum likelihood estimation correctly. I quote below, can someone explain to me please what does it mean that the 2nd and 3rd derivatives of the function equals zero and how to compute that in R. "We have our initial estimated, subjective parameters for the gamma mixture and we have our likelihood that is the mixture of negative

Hi Guys, it would be great if you could help me with a MLE problem in R. I am trying to evaluate the maximum likelihood estimates of theta = (a1, b1, a2, b2, P) which defines a mixture of a Poisson distribution and two gamma prior distributions (where the Poisson means have a gamma distribution, actually 2 gammas and P is the mixing factor). The likelihood function for theta is L(theta) = Pi,j{P

I'm hoping that this is a relatively easy question for someone familiar with the lme4 package. I'm accustomed to using HLM software and writing a simple 2 level [null] equation like this: L1 - Yij = b0 + e L2 - b0 = B00 + u0 The following command in R provides results that are identical to the HLM program. results <- lmer( Y ~ 1 |id , PanelData4) I can't

Hi all, Thanks to all who offered advice on using F95 in R. Now I'm trying to compile a test package using gfortran, Linux 2.4.21 and R 2.1.0. I was able to successfully compile and use a test F95 routine by setting my environment variables as follows in bash: export PATH=~/bin/:$PATH export F77=gfortran export LD_LIBRARY_PATH=~/bin/irun/lib export GFORTRAN_STDIN_UNIT=-1 Now I'm

http://dovecot.org/releases/1.1/beta/dovecot-1.1.beta8.tar.gz http://dovecot.org/releases/1.1/beta/dovecot-1.1.beta8.tar.gz.sig Changes since beta7: - Added a new "raw" mail storage backend which allows opening files/streams as single mail mailboxes. deliver uses this now instead of opening the incoming mail as a mbox. So deliver should be now faster and it doesn't anymore remove

Hi everyone, I would like to ask a question regarding to the data used to fit the mixed model. I wonder that, for the response variable data used to fit the mixed model (either via "spm" or "lme"), we must have several observations per subject (i.e. Yij, i = 1,..,M, j = 1,.., ni) or it can be just one observation per subject (i.e. Yi, i = 1,...,M). Since we have to

Hi all, I have a data.frame with the following colnames pattern: x1 y11 x2 y21 y22 y23 x3 y31 y32 ... I.e. I have an x followed by a few y's. What I would like to do is turn this wide format into a tall format with two columns: "x", "y". The structure is that xi needs to be associated with yij (e.g. x1 should next to y11 and y12, x2 should be next to y21, y22, and

We are proud to announce another beta release of Samba 4.0, beta8 Specifically, this release addresses the panic on startup all users upgrading to beta7 experienced. What's new in Samba 4.0 beta8 ============================= Samba 4.0 will be the next version of the Samba suite and incorporates all the technology found in both the Samba4 alpha series and the stable 3.x series. The primary

Dear All, I wonder if there is an efficient way to fit the generalized linear mixed model for multivariate outcomes. More specifically, Suppose that for a given subject i and at a given time j we observe a multivariate outcome Yij = (Y_ij1, Y_ij2, ..., Y_ijK). where Y_ijk is a binomial(n_ijk, p_ijk). One way to jointly model the data is to use the following specification: g(p_ijk) =

I'm not looking for an answer here, this is just a report of a peculiar event I've observed and can reproduce. I'm developing a g95 (20050129) compiled package on windows xp pro using R-2.1.1, and gcc-3.4.2 (mingw special). An older version of the F95 package compiled and ran without problems. The new version of the package includes extensive changes in the code. The package has a

Hi, I would really appreciate if I could get some help here. I'm using nlm to minimize my negative log likelihood function. What I did is as follows: My log likelihood function (it returns negative log likelihood) with 'gradient' attribute defined inside as follows: # ==========Method definition====================== logLikFunc3 <- function(sigma, object, totalTime) { y <-

Hi all, How do I get to previous versions of the dovecot source code? I wanted to download dovecot-1.0.beta7, but can't find it via the web, only the latest version (beta8). It's probably obvious how to do this...but I'm not seeing it. Thanks much! Jackie ---- Jackie Hunt ACNS Voice: (970)

Hi all I am trying to generate a normal unbalanced data to estimate the coefficients of LM, LMM, GLM, and GLMM and their standard errors. Also, I am trying to estimate the variance components and their standard errors. Further, I am trying to use the likelihood ratio test to test H0: sigma^2_b = 0 (random effects variance component), and the t-test to test H0:mu=0 (intercept of the model Yij = mu

Hi folks, I have a dataset that consists of counts over a ~30 year period at multiple (>200) sites. Only one count is conducted at each site in each year; however, not all sites are surveyed in all years. I need to impute the missing values because I need an estimate of the total population size (i.e., sum of counts across all sites) in each year as input to another model. >