similar to: no predict function in lme4 ?

I have a question regarding the build of my project papeR (hosted on R-forge http://r-forge.r-project.org/R/?group_id=1574) with respect to lme4. Both, Windows and MacOS are complaining that lme4 doesn't export summary: Error : object 'summary' is not exported by 'namespace:lme4' ERROR: lazy loading failed for package 'papeR' Linux however builds the project

Dear R users, I am trying to fit a GLMM to the following dataset; tab a b c 1 1 0.6 199320100313 2 1 0.8 199427100412 3 1 0.8 199427202112 4 1 0.2 199428100611 5 1 1.0 199428101011 6 1 0.8 199428101111 7 0 0.8 199527103011 8 1 0.6 199527200711 9 0 0.8 199527202411 10 0 0.6 199529100412 11 1 0.2 199626201111 12 2 0.8 199627200612 13 1 0.4 199628100111 14 1 0.8

Dear R users, I'm trying to use function 'refit' from lme4 and I get this error that I can't understand: > refit(dolo4.model4,cbind(uu,50-uu)) Error in function (classes, fdef, mtable) : unable to find an inherited method for function "refit", for signature "mer", "matrix" if I try: > refit(dolo4.model4,uu) Error in asMethod(object) :

Dear all, the help file for predict.gbm states that "The predictions from gbm do not include the offset term. The user may add the value of the offset to the predicted value if desired." I am just not sure how exactly, especially for a Poisson model, where I believe the offset is multiplicative ? For example: library(MASS) fit1 <- glm(Claims ~ District + Group + Age +

Dear list, I have another (again possibly boneheaded) puzzle about importing, again encapsulated in a nearly trivial package. (The package is posted at <http://www.math.mcmaster.ca/bolker/misc/coefsumtest_0.001.tar.gz>.) The package consists (only) of the following S3 method definitions: coeftab <- function(object, ...) UseMethod("coeftab",object) coeftab.default <-

Dear gbm users, When running predict.gbm() on a "large" dataset (150,000 rows, 300 columns, 500 trees), I notice that the memory used by R grows beyond reasonable limits. My 14GB of RAM are often not sufficient. I am interpreting this as a memory leak since there should be no reason to expand memory needs once the data are loaded and passed to predict.gbm() ? Running R version 2.9.2 on

-----BEGIN PGP SIGNED MESSAGE----- Hash: SHA1 While I'm not an R expert, I have used R on Windows XP. Now I've moved to Ubuntu (Intrepid), and I'm trying to configure R to work with the Gelman and Hill _Data Analysis Using Regression and Multilevel/Hierarchical Models_. So far, it's not working. I start by following the instructions for installing arm and BRugs at

Folks In trying to use lmer for a hierarchical model, I encountered the following message: Error in UseMethod("VarCorr") : no applicable method for 'VarCorr' applied to an object of class "mer" foo.mer <- lmer(y ~ TP + (TP|M),data=joe.q) > head(joe.q[,1:5]) TP M AB Trt y 1 1 Jan A NN 19.20002 2 1 Jan A NN 19.06378 3 1 Jan A NN

Hi all I have problems in accessing a mer object called model.01 from a workspace that was created with R 2.8.1 and saved with save into an .RData file (on Windows XP or Ubuntu 8.10, don't remember anymore). Now I want to open it in R 2.9.0 on Ubuntu 8.10. I use # load workspace load("name.RData") which seems to work: ls() # all objects in there [1] "all"

I want to call summary on a mer object (from lme4) within my package but I can't seem to get the namespace to import the necessary method. I've simplified my package to this one function: --------------------------- ss <- function(m) { summary(m) } --------------------------- And my namespace file looks like this, where I've attempted to follow the instructions in "Writing

I'm resending this message because I did not include a subject line in my first posting. Apologies for the inconvenience! Tanja > Hello, > > I'm trying to fit a generalized linear mixed model to estimate diabetes prevalence at US county level. To do this I'm using the glmer() function in package lme4. I can fit relatively simple models (i.e. few covariates) but when

I am having a problem extracting from "mer" objects.    I have constructed my problem using existing datasets.   Using the following commands:   require(lme4) fm1 <- lmer(Yield ~ 1 + (1 | Batch), Dyestuff) fixef(fm1) I get the following error message: "Error in UseMethod("fixef") : no applicable method for "fixef""   I know that "fixef" is in

Dear all I want to compute Monte Carlo p-values in lmer-models based on sampled data sets. To speed up calculations, I've tried to use internal functions from the Matrix package (as suggested ealier on the list by Doug Bates). So I did: fm2 <- lmer(resistance ~ ET + position + (1|Grp), Semiconductor,method='ML') simdata<-simulate(fm2,nsim=1) ynew <- simdata[,1] mer

Hi everyone, I'm sure this has been asked before, but I could not find the right search terms to find it. If I want to see what summarizing a model fit with lm() does, I can write "summary.lm". But what if I want to see what summarizing a model fit with lmer() (lme4 package) does? showMethods(summary) Function: summary (package base) object="ANY"

Hi all, I have a problem with package installing in Windows, on my PC machine. The end goal is to be able to use the lme() function. Here's what I did so far: > install.packages("lme4") Warning in install.packages("lme4") : argument 'lib' is missing: using 'C:\Users\Angela\Documents/R/win-library/2.9' --- Please select a CRAN mirror for use in this

Dear all, I try to run the example of lmer and get the following error message. > library(lme4) > example(lmer) lmer> (fm1 <- lmer(Reaction ~ Days + (Days | Subject), sleepstudy)) [[1]] Error in get(x, envir, mode, inherits) : variable "as.dpoMatrix" was not found This error message is similar to what I get with other models. It looks like the mer class has a slightly

Hello all! R2.0.1, W2k. All packages updated. I?m heavily dependant on using mixed models. Up til?now I have used lme() from nlme as I have been told to. Together with estimable() from gmodels it works smooth. I also often run Rcmdr, mostly for quick graphics. After using Rcmdr, on reopening the R workspace all help libraries for Rcmdr (22 !) loads, among them nlme, but not Rcmdr itself. Why?

Hi, I am using lmer (from the package lme4) to predict a binary response variable (REL) from a bunch of fixed effects and two random effects (Speaker_ID and NPhead_lemma): fit <- lmer(REL ~ SPEAKER_GENDER + log(SPEECHRATE) + SQSPEECHRATE + ..... + (1|Speaker_ID) + (1|NPhead_lemma), family="binomial", data=data.lmer, method="Laplace", model=T, x=T) I

I'm trying to use the lmeSplines package together with lme4. Below is (1) an example of lmeSplines together with nlme (2) an attempt to use lmeSplines with lme4 (3) then a comparison of the random effects from the two different methods. (1) require(lmeSplines) data(smSplineEx1) dat <- smSplineEx1 dat.lo <- loess(y~time, data=dat) plot(dat.lo) dat$all <- rep(1,nrow(dat)) times20

Hi all I tried to reproduce an example with lme and used the Orthodont dataset. library(nlme) fm2a.1 <- lme(distance ~ age + Sex, data = Orthodont, random = ~ 1 | Subject) anova(fm2a.1) > numDF denDF F-value p-value > (Intercept) 1 80 4123.156 <.0001 > age 1 80 114.838 <.0001 > Sex 1 25 9.292 0.0054 or alternatively