similar to: Problem extracting from mer objects

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

Hello, I am trying to do contrasts after applying a binomial mixed effect model with the function lmer. I have to extract the fix effect values, but as I write fixef(model), I get this error message: Error in UseMethod("fixef") : no method for 'fixef' with objects of class "mer" Has anybody some ideas why? And how can I then post-hoc testing my data if I cannot do

Dear R group, Tried to turn off the html in yahoo.  So far  not successful.  Apologies! I tried to get xyplot shrink fit for my mixed model.  But, the mixed model line is not seen in the graph. I would like to know if there is anything wrong with my code. Thanks, A.K. (fm1 <- lmer(Response3 ~1+ Wavelength*Start_Resp*time + (1|resid) + (1+time|Subject_BDat), family=binomial, data=Behavdat,

Dear R help list, I have done a lot of searching but have not been able to find an answer to my problem. I apologize in advance if this has been asked before. I am applying a mixed model to my data using lmer. I will use sample data to illustrate my question: >library(lme4) >library(arm) >data("HR", package = "SASmixed") > str(HR) 'data.frame': 120 obs.

Hi All, I have tried every fix on my try or tryCatch that I have found on the internet, but so far have not been able to get my R code to continue with the "for loop" after the lmer model results in an error. Here is two attemps of my code, the input is a 3D array file, but really any function would do.... metatrialstry<-function(mydata){ a<-matrix(data=NA, nrow=dim(mydata)[3],

Dear R-users, I am using the most updated package of lme4 (lme4_0.999375-2). I have a data set consisting of ~900 observations at ~440 independent survey sites. There are an unbalanced number of surveys at each site. I am attempting to develop several models evaluating the presence/absence of a species (PRES) at these random sites (SITE) using a number of predictor variables. The

Hi folks, I am tearing my hair out on this one. I am using an example from Pinheiro and Bates. ### this works data(Orange) mod.lis <- nlsList(circumference ~ SSlogis(age, Asymp, xmid, scal), data=Orange ) ### This works mod <- nlme(circumference ~ SSlogis(age, Asymp, xmid, scal), data=Orange, fixed = Asymp + xmid + scal ~ 1, start =

I am making the question clear. Please help. > Dear R experts > > I was using kinship package to fit mixed model with kinship matrix. > The package looks like lme4, but I could find a way to extract p-value > out of it. I need to extract is as I need to analyse large number of > variables (> 10000). > > Please help me: > > require(kinship) > > #Generating

I'm testing out a new version of coxme and R CMD check fails with "could not find function ranef" (or random.effects or fixef, or anything from nlme). The NAMESPACE file has the line below importFrom(nlme, ranef, random.effects, fixef, fixed.effects, VarCorr) and nlme is declared in the DESCRIPTION file as an import. I feel that I must be staring at some obvious (but

I'm trying to use "predict" with a linear mixed-effects logistic regression model fitted with nlmmPQL from the MASS library. Unfortunately, I'm getting an error "non-conformable arguments" in predict.lme, and I would like to understand why. I have used the same call to "predict" with "glm" models without problems. I assume I'm doing

What is the easiest way to extract a value from a 'mer' object from glmer? The first I need is the trtpair Std.Dev. which in this case is 0.17542? I've managed to get the fixed effects numbers from summary(fednmaout)@coeffs but no luck with the Random effects stuff. TIA Jim > fednmaout Linear mixed model fit by REML Formula: lor ~ as.factor(t2) + as.factor(t3) + (1 |

I am trying to make predictions with se's using a nlme (kew11.nlme below). I get an error indicating levels for a factor are not allowed. I have searched and read Rnews, MEMSS, MASS, R-Help, and other lists in Spanish where I found questions similar to mine but not solution. I do not really care about the method used. Any suggestions to obtain predictions with se's from an nlme

Good day, For lack of a better solution (or perhaps I am ignorant to something more elegant), I have been bootstrapping panel data by hand so to speak and I would like to know if there is a way to define multiple variables in a loop using the loop variable. I found a post (here: https://stat.ethz.ch/pipermail/r-help/2002-October/026305.html ) that discussed naming multiple variables but it

Full_Name: Renaud Lancelot Version: Version 2.3.0 (2006-04-24) OS: MS Windows XP Pro SP2 Submission from: (NULL) (82.239.219.108) I think there is a bug in predict.lme, when a polynomial generated by poly() is used as an explanatory variable, and a new data.frame is used for predictions. I guess this is related to * not * using, for predictions, the coefs used in constructing the orthogonal

Hi All, I am trying to shift from running mixed models in SAS using PROC MIXED to using lme4 package in R. In trying to match the coefficients of R output to that of SAS output, I came across this problem. The dataset I am using is this one: http://support.sas.com/documentation/cdl/en/statug/63033/HTML/default/viewer.htm#statug_mixed_sect034.htm If I run the following code: proc mixed data=rc

Hi, Thanks for your response, it nearly worked! But it only wrote one coloumn of data and not the three columns I need. Using fixef(m1) doesnt give the same results as coef(m1) when you are using more than one random effect. I need the coefficients for each individual so I use coef(m1) to get this which results in an object of class lmer.coef, 3 columns by 700 rows. as.data.frame() wont work on

Hi All! I am desperately needing some help figuring out how to calculate LD50 with a GLMM (probit link) or, more importantly, the standard error of the LD50. I conducted a cold temperature experiment and am trying to assess after how long 50% of the insects had died (I had 3 different instars (non significant fixed effect) and several different blocks (I did 4 replicates at a time)=

Hi, I wish to extract a subset of the information of given by summary(lmer.object) as a dataframe. In particular, I wish to extract just a table listing the Estimate, Std Error, and t-values rounded to 3 decimal places. I have learned how to extract the coefficients with "round(fixef(lmer.object),3)" and the standard errors with "round(sqrt(diag(vcov(a.lmer))),3)" but I do not

Hi, I would like to extract the fitted values from a model using LMER but only for the fix portion of the model and not for the fix and random portion (e.g it is the procedure outpm or outp in SAS). I am aware of the procedure fitted() but I not sure it give the fitted values both for the fixed and random or only the fixed. I looked in the r help and the r list and I haven?t not found much

Dear R-Users: I am trying to find the robust (or sandwich) estimates of the standard error of fixed effects parameter estimates using the package "lmer2". In model-1, I used "robust=TRUE" on the other, in model-2, I used "robust=FALSE". Both models giving me the same estimates. So my question is, does the robust option works in lmer2 to get the robust estimates of