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.

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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

Now I've found another way to make it work. All I need is to pick up the names in the column (x.1.age...). > v<-pr(goprobit.p); v Maximum-Likelihood Estimates weighted = FALSE iterations = 5 logLik = -14160.75 finalHessian = TRUE Covariance matrix is Robust Number of parameters = 66 Sample size = 17922 est se t p g sig x.1.age 0.0341 0.0138 2.4766 0.0133 -3.8835e-04 ** x.1.sleep

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: #

Below is part a regression printout. How can I use "grep" to identify rows headed by variables (first column) with a certain label. In this case, I like to find variables containing "somewhath", "veryh",?"somewhatm", "verym", "somewhatc", "veryc","somewhatl", "veryl". The result should be an index 6:13 or

On 12.07.2024 10:54, Steven Yen wrote: > Below is part a regression printout. How can I use "grep" to identify > rows headed by variables (first column) with a certain label. In this > case, I like to find variables containing "somewhath", > "veryh",?"somewhatm", "verym", "somewhatc", "veryc","somewhatl",

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

Could not get "which" to work, but my grep worked. Thanks. > which(grep("very|somewhat",names(goprobit.p$est))) Error in which(grep("very|somewhat", names(goprobit.p$est))) : argument to 'which' is not logical > grep("very|somewhat",names(goprobit.p$est)) [1] 6 7 8 9 10 11 12 13 28 29 30 31 32 33 34 35 50 51 52 53 54 55 56 57 On 7/12/2024

Thanks. In this case below, what is "x"? I tried rownames(out) which did not work. Sorry. Does this sound like homework to you? On 7/12/2024 5:09 PM, Uwe Ligges wrote: > > > On 12.07.2024 10:54, Steven Yen wrote: >> Below is part a regression printout. How can I use "grep" to identify >> rows headed by variables (first column) with a certain label. In

Yes. Any of the following worked. The pipe greater than (|>) is neat! Thanks. > v<-goprobit.p$est > names(v) |> grep("somewhat|very", x = _) ?[1]? 6? 7? 8? 9 10 11 12 13 28 29 30 31 32 33 34 35 50 51 52 53 54 55 56 57 > v |> names() |> grep("somewhat|very", x = _) ?[1]? 6? 7? 8? 9 10 11 12 13 28 29 30 31 32 33 34 35 50 51 52 53 54 55 56 57 >

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