similar to: multivariate meta-analysis with the metafor package

Dear all, I'm trying to estimate the parameters of a special case of a poisson model, where the specified equation has an integral and several fixed parameters. I think that the MLE command in STATS4 package could be a good choice, but it's a little complicated. I've got some problems with the offset and I don't understand some of the functions. Do you know where can I find some

Dear all, I have a very basic question: how does the logLik function work for poisson models? Example: I simulate 20 observations from a Poisson distribution with mean 800. y <- rpois(20,800) model <- glm(y ~ 1, family=poisson()) logLik(model) I would like to know what's the exact formula the function logLik uses. I looked at ?extractAIC but I cannot sort it out. Can you please

Dear all, I found a problem with 'lme4'. Basically, once you load the package 'aod' (Analysis of Overdispersed Data), the functions 'lmer' and 'glmer' don't work anymore: library(lme4) (fm1 <- lmer(Reaction ~ Days + (Days|Subject), sleepstudy)) (gm1 <- glmer(cbind(incidence, size - incidence) ~ period + (1 | herd), family = binomial, data

Hello, I'm trying to save a graph as a .ps file to include it in a LaTex presentation, but R replies: Error: Invalid font type In addition: Warning messages: 1: font family not found in PostScript font database 2: font family not found in PostScript font database If I load the package grDevices and type "postscript()" then I can save it, but it creates also a text file saying:

Dear all, I've tried to solve the Es. 12, cap 4 of "Introduction to GLM" by Annette Dobson. It's about the relationship between survival time of leukemia patients and blood cell count. I tried to fit a model with exponential distribution, first by glm (family gamma and then dispersion parameter fixed to 1) and then with survreg. They gave me the same point estimates but the

Hello, I have compared the potentials of R and Stata about GLMM, analysing the dataset 'ohio' in the package 'faraway' (the same dataset is analysed with GEE in the book 'Extending the linear model with R' by Julian Faraway). Basically, I've tried the 2 commands 'glmmPQL' and 'glmer' of R and the command 'xtmelogit' of Stata. If I'm not

Dear R-users, I've just installed the last version of R (2.10.0) in Windows and found a completely new version of the help pages. Basically, they now appear as a simple (even if slightly embellished) text file. I tried to install it with a customized startup and realized that the old CHM files (the default for Windows up to 2.9.2) are not included anymore as a possible choice. I don't

Dear R users, I found a small problem with the package creation procedure in R 2.9.0 under Windows. Basically, I input in the command prompt; Rcmd BUILD --binary --use-zip namepackage The procedure works, but the titles in the help pages of the created package lose the usual colours and font. Conversely, the result is perfect using R 2.8.1 (changing the path) and the same version of Rtools

Dear R users, I'm trying to specify a generalized linear mixed model in R, basically a Poisson model to describe monthly series of counts in different regions. My aim is to fit subject-specific curves, modelling a non-linear trend for each region through random effects for linear splines components (see Durban et al, Stat Med 2005, or " Semiparametric regression" by Ruppert et al,

Dear fellows, I'm trying to extract the AIC statistic from a GLM model with quasipoisson link. The formula I'm referring to is AIC = -2(maximum loglik) + 2df * phi with phi the overdispersion parameter, as reported in: Peng et al., Model choice in time series studies os air pollution and mortality. J R Stat Soc A, 2006; 162: pag 190. Unfortunately, the function logLik

Dear R users, I created some functions I want to convert in a package under Windows (Vista or XP). I carefully read both "Writing R extensions" by the R Development Core Team and "Creating R packages: a tutorial" by Friedrich Leisch. I've also installed Rtools as suggested. These tutorials are very useful, but I'm struggling with the last step of the installation

Dear R users, I included 2 internal functions in the package 'dlnm', called 'mkbasis' and 'mklagbasis'. Despite they are not meant to be called by the users, I included them in the namespace in order to make them available, keeping the process more transparent and giving the opportunity to change or improve them. I included an help page 'dlnm-internal.Rd' to

Dear R users, I included 2 internal functions in the package 'dlnm', called 'mkbasis' and 'mklagbasis'. Despite they are not meant to be called by the users, I included them in the namespace in order to make them available, keeping the process more transparent and giving the opportunity to change or improve them. I included an help page 'dlnm-internal.Rd' to

Dear R Community, I am pleased to announce the release of a new package called 'dlnm', now available on CRAN (version 0.2.1). The package dlnm provides some facilities to run distributed lag models (DLM's) and their non-linear extension (DLNM's), a modelling framework to describe simultaneously non-linear and delayed effects between predictors and an outcome in time-series

Dear R Community, I am pleased to announce the release of a new package called 'dlnm', now available on CRAN (version 0.2.1). The package dlnm provides some facilities to run distributed lag models (DLM's) and their non-linear extension (DLNM's), a modelling framework to describe simultaneously non-linear and delayed effects between predictors and an outcome in time-series

Hello R users, I'm trying to understand the meaning of the results returned by the command 'predict' with type="terms". I thought it was the matrix with the fitted values for each term (value of predictor * related coef), but the calculation is different. I include an example below: y <- rnorm(10) x1 <- rnorm(10) x2 <- rnorm(10) model <- lm(y ~ x1 + x2)

Hello R users, I'm trying to solve an apparently simple problem. I include a matrix in a model formula, and I want then to extract the related coefficients. Basically, I want a function func such that: mymatrix <- cbind(rnorm(10),rnorm(10)) y <- b <- rnorm(10) model <- lm(y ~ b + mymatrix) func(mymatrix,model) which returns only the 3rd and 4th coefficients. I thought to

Dear R Community, I am pleased to announce the release of a new package called 'mvmeta', now available on CRAN (version 0.2.0). The package mvmeta provides some functions to perform fixed and random-effects multivariate meta-analysis and meta-regression. This modelling framework is exploited to pool multiple correlated outcomes across studies, and already applied in different fields:

Dear R Community, I am pleased to announce the release of a new package called 'mvmeta', now available on CRAN (version 0.2.0). The package mvmeta provides some functions to perform fixed and random-effects multivariate meta-analysis and meta-regression. This modelling framework is exploited to pool multiple correlated outcomes across studies, and already applied in different fields:

Dear all, I need to fit a gee model with an auto-regressive correlation structure and I faced some problems. I attach a simple example: ####################################################### library(gee) library(geepack) # I SIMULATE DATA FROM POISSON DISTRIBUTION, 10 OBS FOR EACH OF 50 GROUPS set.seed(1) y <- rpois(500,50) x <- rnorm(500) id <- rep(1:50,each=10) # EXAMPLES FOR