similar to: variance using lmer

Dear All, I am analysing some data for a colleague (not my data, gotta be published so I cannot divulge). My response variable is the number of matings observed per day for some fruitlies. My factors are: Day: the observations were taken on 9 days Regime: 3 selection regimes Line: 3 replicates per selection regime. I have 81 observations in total The lines are coded A to I, so I do not need

#I am putting a test together for an introductory biology class and I would like to put different cross hatching inside of each bar for the bar plot below color <- c("Brightly Colored", "Dull", "Neither") lizards <- c(277, 70, 3) liz.col <- data.frame(color, lizards) qplot(color, lizards, data=liz.col, geom="bar", ylab="Observed Matings",

Dear all, I would like to use the residuals in a general linear mixed effect model to diagnose model fit. I know that the resid function has been implemented for linear mixed models but not yet for general linear mixed effects. Is there a way to get them out of lmer fit objects? I tried searching the r-help archive and found nothing. I tried and failed to replicate what (I guessed would be

Dear colleagues, I have a data from a repeated measures design that I'm analysing through a mixed model. Nine independent sampling units (flasks with culture medium with algae) were randomly divided into 3 groups ("c", "t1", "t2"). There is no need for inclusion of the random effect of the intercept, because the nine sample units are homogeneous among each other

I seem to come to this problem alot, and I can find my way out of it with a loop, but I wish, and wonder if there is a better way. Here's an example (lmer1-5 are a series of lmer objects): bs=data.frame(bic=BIC(lmer1,lmer2,lmer3,lmer4,lmer5)$BIC) rownames(bs)=c('lmer1','lmer2','lmer3','lmer4','lmer5') best=rownames(bs)[bs==min(bs)] > best [1]

Hi there, In multiple regression one way to view R^2 is as (the square of) the correlation between original y's and the estimated y's. Suppose you fit a multilevel model with random intercept for each cluster. Would it be valid to compute an R^2 by using fixed effects plus the group intercepts to reduce the residuals? I suspect this has been done and, given its absence from the lmer

Dear all, I'm using the latest version of the package lme4 and R version 2.7.2 (2008-08-25). After I run the model, I get the results of the model (cf. below). Then, I run an ANOVA using the "anova" function and I get the following message "Error in anova(lmer1) : Calculated PWRSS for a LMM is negative". I went trough the R-mailing list and a similar error message was

Hi all, I've now got a problem with some modified lmer code (function lmer1 pasted at end) - I've made only three changes to the lmer code (marked), and I'm not really looking for comments on this function, but would like to know why execution of the following commands that use it almost invariably (but not quite predictably) leads to the R session terminating. Here's the command

I'm attempting to do some amount of mass migration from const std::string& (& const char*) to StringRef. In doing so I stumbled across the fact that while StringRef has no op+ to speak of (well, it has += and I added a manual StringRef+std::string and std::string+StringRef for now - though I'm thinking perhaps they can be skipped in favor of Twine operations), Twine does provide a

I'm having trouble with 'lmer' and would really appreciate it if anybody could help. I am trying to run generalized linear mixed effect model, and am using 'lmer', but some of the names inside the data do not show up in the summary after I compute the 'lmer'. As a close example of the data I have, the objects are Calls = MM, MB, MM, MM, MN, MNX, MNX, ... -this

Dear members, I would like to switch from nlme to lme4 and try to translate some of my models that worked fine with lme. I have problems with the pdMat classes. Below a toy dataset with a fixed effect F and a random effect R. I gave also 2 similar lme models. The one containing pdLogChol (lme1) is easy to translate (as it is an explicit notation of the default model) The more parsimonious

Greetings, I am trying to run a logistic regression model for binary data with a random intercept and slope in R 2.6.1. When I use the code: lmer1<-lmer(infect ~ time+gender + (1+time|id), family=binomial, data=ichs, method="Laplace") Then from: summary(lmer1) I get the message: Error in if (any(sd < 0)) return("'sd' slot has negative entries") : missing

Dear R Community! We analyse the impact of climbing activity on cliff vegetation. During our fieldwork, we recorded 90 Transects in 3 climbing sites. The aim is to see, if the plant cover (response: Cover) is influenced only by crevice availability (predictor: Cracs), or, additional, by the distance to the climbing route (predictor: Distance). Six plots are nested within one Transect

On Jul 18, 2011, at 12:38 PM, David Blaikie wrote: > I'm attempting to do some amount of mass migration from const std::string& (& const char*) to StringRef. Great! > In doing so I stumbled across the fact that while StringRef has no op+ to speak of (well, it has += and I added a manual StringRef+std::string and std::string+StringRef for now - though I'm thinking perhaps

Dear All, I've been using mcmcsamp() successfully with a few different mixed models but I can't get it to work with the following. Is there an obvious reason why it shouldn't work with a model of this structure ? *brief summary of objective: I want to test the effect of no-fishing marine reserves on the abundance of a target species. I have samples at coral reef sites inside and

Running R 2.5.1 and a newly downloaded lme4 package on WinXP I'm trying to work my way through Everitt and Hothorn's "Handbook of Statistical Analyses Using R," c 2006. (No, it's not homework.) Chapter 10 discusses linear mixed effects models for longitudinal data. I've called my long data frame BtheB.long Here's the model from the book, which I run. lmer1 <-

Hi All. I'm trying to run a simple model from Baayan, Davidson, & Bates and getting a confusing error message. Any ideas what I'm doing wrong here? # Here's the data..... Subj <- factor(rep(1:3,each=6)) Item <- factor(rep(1:3,6)) SOA <- factor(rep(0:1,3,each=3)) RT <- c(466,520,502,475,494,490,516,566,577,491,544,526,484,529,539,470,511,528) priming

Dear r-helpers, I would very much appreciate help with the following problem: The following command (in a .Rnw file) latex(anova(e7.lmer3, e7.lmer4), file = 'e7lmer34.tex', rowname = c ('nonlinear', 'linear'), longtable = FALSE, dcolumn = T, booktabs = T, table.env = F) produces the following output after running Sweave: % latex.default(anova(e7.lmer1, e7.lmer2),

Dear useRs, I am using mgcv version 1.7-16. When I create a model with a few non-linear terms and a random intercept for (in my case) country using s(Country,bs="re"), the representative line in my model (i.e. approximate significance of smooth terms) for the random intercept reads: edf Ref.df F p-value s(Country) 36.127 58.551 0.644

> And for arguments, generally always use Twine as the default, it allows construction of complex things, and is still efficient when passed the equiv of a StringRef (with the toStringRef method).  The only annoying thing about it is that the API to do this requires a temporary SmallVector to scribble in, which makes it more difficult to use. > > It seems that there should be a good way