similar to: extract value from mer object ?

Dear Thomas Lumley, and R-help list members, I have read your article "Network meta-analysis for indirect treatment comparisons" (Statist Med, 2002) with great interest. I found it very helpful that you included the R code to replicate your analysis; however, I have had a problem replicating your example and wondered if you are able to give me a hint. When I use the code from the

What is teh reason some functions in the AICcmodavg package do not work with 'mer' class models? One such example would be the 'importance' function. Thanks Ronny -- View this message in context: http://r.789695.n4.nabble.com/AICcmodavg-functions-and-mer-class-models-tp3714534p3714534.html Sent from the R help mailing list archive at Nabble.com.

Hello, Could somebody please tell me what am I doing wrong in following? I try extract coefficients (using arm-package) from the lmer frunction, but I get the following warning: a<-data.frame(coef(res)) Error in as.data.frame.default(x[[i]], optional = TRUE, stringsAsFactors = stringsAsFactors) : cannot coerce class "coef.mer" into a data.fram I think I have done it before

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

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

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"

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

Dear all, I get an error message when I run my model and I am not sure what to do about it. I try to determine what factors influence the survival of voles. I use a mixed-model because I have several voles per site (varying from 2 to 19 voles). Here is the model: ### fm5 <-lmer(data=cdrgsaou2, alive~factor(pacut)+factor(agecamp)+factor(sex)+ResCondCorp+(1|factor(cdrgsa ou2$ids)),

Hello, I have a data frame with a factor column, which uniquely identifies the observations in the data frame and it looks like this: sample1_condition1_place1 sample2_condition1_place1 sample3_condition1_place1 . . . sample3_condition3_place3 I want to turn it into three separate factor columns "sample", "condition" and "place". This is what I did so far: #

Hi all, I want to fit the following model to my data: Y_t= a+bY_(t-1)+cY_(t-2) + Z_t +Z_(t-1) + Z_(t-2) + X_t + M_t i.e. it is an ARMA(2,2) with some additional regressors X and M. [Z_t's are the white noise variables] So, I run the following code: for (i in 1:rep) { index=sample(4,15,replace=T) final<-do.call(rbind,lapply(index,function(i)

I am attempting to model data with the following variables: timepoint - n=48, monthly over 4 years hospital - n=3 opsn1 - no of outcomes total.patients skillmixpc - skill mix percentage nurse.hours.per.day Aims To determine if skillmix affects rate (i.e. no.of.outcomes/total.patients). To determine if nurse.hours.per.day affects rate. To determine if rates vary between

On 11/26/2017 08:42 PM, jim holtman wrote: > try this: > > ########################################## > > library(dplyr) > > input <- tribble( > ~station, ~from, ~to, ~record, > "07EA001" , 1960 , 1960 , "QMS", > "07EA001" , 1961 , 1970 , "QMC", > "07EA001" , 1971 , 1971 ,

Hello, I have been attempting to set up a lme and have looked at numerous posts including 'R's lmer cheat-sheet' as well as reading a number of papers and other resources including R help, but I am still a little confused on how to write my model (I thought I had it). I have asked a number of questions on different forums; most of which have been resolved. My main concern right now

Hi, A benchmarking study with an additional (data.table-based) solution. Enjoy! ;) Cheers, Denes -------------------------- ## packages ########################## library(dplyr) library(data.table) library(IRanges) library(microbenchmark) ## prepare example dataset ########### ## use Bert's example, with 2000 stations instead of 2 d_df <- data.frame( station =

Hi Martin, On 11/29/2017 10:46 PM, Martin Morgan wrote: > On 11/29/2017 04:15 PM, T?th D?nes wrote: >> Hi, >> >> A benchmarking study with an additional (data.table-based) solution. > > I don't think speed is the right benchmark (I do agree that correctness > is!). Well, agree, and sorry for the wording. It was really just an exercise and not a full

Or with the Bioconductor IRanges package: df <- with(input, DataFrame(station, year=IRanges(from, to), record)) expand(df, "year") DataFrame with 24 rows and 3 columns station year record <character> <integer> <character> 1 07EA001 1960 QMS 2 07EA001 1961 QMC 3 07EA001 1962 QMC 4

Hi, I recently changed from uw imap to dovecot on the sound recommendation of a friend and have mostly succeeded in getting all of my clients up and running, but am really stuck with the iPhone which is failing to make connections. I run certificates on all of my clients and thunderbird happily connects both locally and remotely. I installed the certificate on the iPhone after great pain (pk12

On 11/29/2017 04:15 PM, T?th D?nes wrote: > Hi, > > A benchmarking study with an additional (data.table-based) solution. I don't think speed is the right benchmark (I do agree that correctness is!). For the R-help list, maybe something about least specialized R knowledge required would be appropriate? I'd say there were some 'hard' solutions -- Michael (deep

> On 9 Nov 2017, at 14:58, PIKAL Petr <petr.pikal at precheza.cz> wrote: > > Hi > > Thanks for working example. > > you could use split/ lapply approach, however it is probably not much better than dplyr method. > > sapply(split(mydf, mydf$type), function(speed, n_vehicles) sum(mydf$speed*mydf$n_vehicles)/sum(mydf$n_vehicles)) > gives you averages > The

Hi I have a question regarding an output of a binomial lmer-model. The model is as follows: lmer(y~diet * day * female + (day|female),family=binomial) The corresponding output is: Generalized linear mixed model fit by the Laplace approximation Formula: y ~ diet * day * female + (day | female) AIC BIC logLik deviance 1084 1136 -531.1 1062 Random effects: Groups Name Variance