similar to: lmer error confusion

Hi All. I need a bit of help extracting the residual error variance from the VarCorr structure from lmer. #Here's a 2-way random effects model lmer.1 <- lmer(rating ~ (1|person)+(1|rater), data = dat) #Get the structure vc.fit <- VarCorr(lmer.1) #results in..... $person 1 x 1 Matrix of class "dpoMatrix" (Intercept) (Intercept) 0.7755392 $rater 1 x 1 Matrix

Hi All. I need to create a first-order autoregressive covariance matrix (AR(1)) for a longitudinal mixed-model simulation. I can do this using nested "for" loops but I'm trying to improve my R coding proficiency and am curious how it might be done in a more elegant manner. To be clear, if there are 5 time points then the AR(1) matrix is 5x5 where the diagonal is a constant

In the following example, how can I drop the group index from the list after I perform a split? n <- 3 nn <- 10 g <- factor(round(n * runif(n * nn))) x <- rnorm(n * nn) + sqrt(as.numeric(g)) df <- data.frame(g,x) df.s <- split(df,g) Thanks! Rick DeShon [[alternative HTML version deleted]]

Hi All. I've run into a problem with the plinear algorithm in nls that is confusing me. Assume the following reaction time data over 15 trials for a single unit. Trials are coded from 0-14 so that the intercept represents reaction time in the first trial. trl RT 0 1132.0 1 630.5 2 1371.5 3 704.0 4 488.5 5 575.5 6 613.0 7 824.5 8 509.0 9

Hi All. I'd like to change the default plotting colors used to construct a scatterplot with regression line in the CAR package. So, scatterplot(y~pred,smooth=FALSE, xlab="X", ylab="Y", lwd=2) If I change the palette (e.g., palette(ranbow(6)), I can change the color of the lines and points. However, the axes and labels remain in black (i.e., the first color in the

Hi All. I'm trying to run nlsList an getting an error that makes no sense to me. I have accuracy and reaction time data over many trials for each person (id) When I use nlsList code that is virtually identical to the example in the doc file I get the following error. I've tried everything I could think of and can't get around it. Any ideas what I'm doing wrong? **************

Hi All. Imagine you have a large block diagonal matrix. I'd like to replace the zeros in this matrix with small random (runif) numbers. Any ideas for a simple and efficient way to do this? Best regards, Rick DeShon

Hi All. I'd like to generate a sample of n observations from a k dimensional multivariate normal distribution with a random correlation matrix. My solution: The lower (or upper) triangle of the correlation matrix has n.tri=(d/2)(d+1)-d entries. Take a uniform sample of n.tri possible correlations (runi(n.tr,-.99,.99) Populate a triangle of the matrix with the sampled correlations Mirror the

Hi, I got the following error. Sorry but this time I couldn't reproduce it with a simple chunk of code: .TM.repl.i.2col(): drop 'matrix' case ... Error in .nextMethod(x = x, i = i, j = j) : 'i' has no integer column number should never happen; please report In addition: Warning messages: 1: Ambiguous method selection for "%*%", target

Hi All. I would like to compute a separate covariance matrix for a set of variables for each of the levels of a factor and then compute the average covariance matrix over the factor levels. I can loop through this computation but I need to perform the calculation for a large number of levels and am looking for something more elegant. To be concrete.... u <- 3 n <- 10 x <-

Thanks for the quick response. That makes sense. I just wanted to make sure it wasn't an easy fix, or that I wasn't overlooking something. My scenario calls for very low latency playback with a user being able to jump between many files quickly, or seek quickly. This means I am priming the pump by downloading the first chunk of many files that are likely to be started soon. I was

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

Platform: Windows Version: 1.6.2 When first called, socketConnection gives an error. On subsequent calls it performs correctly. Thus the first call has the effect of "priming" the connection. Eg > socketConnection(port=50) Error in socketConnection(port = 50) : unable to open connection In addition: Warning message: localhost:50 cannot be opened > MIM <-

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

Dear R help I have a question on the variance of the binomial probit model. I have fitted the following model : > lmer1<-lmer(mp ~ l + op + l*op+ us_lev + bw_lev +(1|tatu) , + family = binomial(link="probit"), + method = 'Laplace', + data = matings, + msVerbose= True) > summary(lmer1) Generalized linear

Hello, > Looks like your in the middle of the dist-upgrade in buster. Yes indeed. > looks like bind is upgrading and samba-libs is not yet installed. Indeed, I think it is not: # dpkg -L samba-libs dpkg-query: package 'samba-libs' is not installed Also the following directories no longer exist: - /usr/lib/x86_64-linux-gnu/samba/ - /var/lib/samba/bind-dns/ > apt-get update

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