similar to: lmer coefficient distributions and p values

Hello: I am using R mainly on windows XP, version 2.5. I?m a biologist, with a medium level statistics background. I have a problem stating a two-way factorial design where both factors are random. I?m using the lmer() function implemented in the Matrix package version 0.99. My design is as follows: Two species were randomly selected from a total of 4 species. This species are present

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

I have executed a Repeated Measures ANOVA with one DV (latency) and one within subject factor (acoustic condtion: 3 levels) by bootstrapping my sampling distribution of F from the empirical sample distribution. I chose to resample because the sample distribution deviates from normality a lot. The overall F is significant and now I wish to decompose this with contrasts to ask if latencies to

[Yes, this is related to a homework problem, but is not the problems itself.] In my mathematical statistics class, we've just learned about properties of estimators, and I can now solve manually problems like this: A sample of size n = 16 is drawn from a normal distribution where sigma = 10 but mu is unknown. If mu = 20, what is the probability that the estimator mu hat = Y bar will lie

Please note clarifications in <<>> below. My apologies for any confusion. Thanks again, Scott ---------- Forwarded message ---------- From: Scott Stark <stark.sc@gmail.com> Date: Aug 23, 2007 1:03 PM Subject: Expedite scalar f(x) evaluation over vectors To: r-help@lists.r-project.org Dear R community, I am trying to code a fairly complex equation for optim(). My current

Hi! I'm translating some code from Matlab to R and I found a problem. I need to translate Matlab's function 'lsqnonlin' (http://www-ccs.ucsd.edu/matlab/toolbox/optim/lsqnonlin.html) into R, and at the beginning I thought it would be the same as R's 'optim'. But then I looked at the definition of 'lsqnonlin' and I don't quite see how to make

I have a server that runs stand-alone with an LDAP directory and a KDC . The linux machines have sssd to allow unified users etc. The clients are mostly MacOS and Windows machines that aren't part of an AD. This config has worked for 15 years, but after upgrading Debian and bringing in Samba Version 4.17.7-Debian it seems to be broken. I believe this is related to:

Folks, I wonder if anything could be said about the distribution of a random variate x, where x = N(0,1)/N(0,1) Obviously x is pathological because it could be 0/0. If we exclude this point, so the set is {x/(0/0)}, does x have a well defined distribution? or does it exist a distribution that approximates x. (The case could be generalized of course to N(mu1, sigma1)/N(mu2, sigma2) and one

Hi, New to R, I don't find a way to plot the following data with image(): x is a N * M matrix y is a vector of length M z is a N*M matrix I wish to plot z as a greyscale image, but my x axis is different for every row of the z data. Here is a minimal example, > theta<-c(3:6) # N > y<-c(1:5) # M > > x<-theta%*%t(y)# N * M > z<-sin(x) # N * M > > image(z)

Dear All, Is there any package to do multi-objective optimization? For instance, consider the following problem: maximize f(x,y) in order to x and maximize g(x,y) in order to y, simultaneously, with x and y being the same both for f and g. Can R do it numerically? Thanks in advance, Paul

I'm looking for a function in R similar to t.test() which was generously pointed out to me yesterday, but which can be used for normally distributed data. To recap yesterday: > x <- scan() 1: 62 52 68 23 34 45 27 42 83 56 40 12: Read 11 items > alpha<- .05 > t.test(x) One Sample t-test data: x t = 8.8696, df = 10, p-value = 4.717e-06 alternative hypothesis: true

Dear all, I want to estimate a crossed-random-effects model (i.e., measurements, students, schools) where students migrate between schools over time. I'm interested in the fixed effects of "SES", "age" and their interaction on "read" (reading achievement) while accounting for the sample design. Based on a previous post, I'm specifying my model as: fm1 <-

Version 0.9975-11 of the lme4 package has been uploaded to CRAN. The source package should be available on the mirrors in a day or two and binary packages should follow soon after. There are several changes in this release of the package. The most important is the availability of a development version of lmer called, for the time being, lmer2. At present lmer2 only fits linear mixed models.

Version 0.9975-11 of the lme4 package has been uploaded to CRAN. The source package should be available on the mirrors in a day or two and binary packages should follow soon after. There are several changes in this release of the package. The most important is the availability of a development version of lmer called, for the time being, lmer2. At present lmer2 only fits linear mixed models.

I am a SAS user currently evaluating R as a possible addition or even replacement for SAS. The difficulty I have come across straight away is R's apparent difficulty in handling relatively large data files. Whilst I would not expect it to handle datasets with millions of records, I still really need to be able to work with dataset with 100,000+ records and 100+ variables. Yet, when reading

Hello list, I've been reading through the archives and it seems as though, as of right now, there is no way to specify the correlation structure in lmer. I was wondering if anyone knows if this is going to be implemented? I'm using mixed-effects models within a tree structure, so I make a lot of calls to lme to get the resulting deviance, and lmer2 is almost 5 times faster than lme

I have 316 files. Each file represents a patient's breathing track (respiratory signal recorded for a variable number of cycles). All files have the same are made up of a header followed by a variable number of records. Each record contains 7 comma separated fields. The patient ID is recorder in the header which is stripped off when reading the file into a R data.frame. Since I need to keep

I am running the lmer() command in a for loop and occasionally a particular iteration is producing the false convergence warning. I would like to be able to mark these iterations with a dummy variable, but I can't find any other notification besides the warning message, which, in a for loop, only is printed after the loop is finished (which does not allow me to see which iteration it happened

Dear All, I know how to export graphics as pdf files and then how to include them in LaTeX documents. However, I do not know how to do in order to have the text of the graphics written with the font selected for the LaTeX document. Is that possible? Thanks in advance, Paul

Dear R Users, I am having trouble with lmer. I am looking at recombinant versus non recombinant individuals. In the response variable recombinant individuals are coded as 1's and non-recombinant as 0's. I built a model with 2 fixed factors and 1 random effect. Sex (males/females) is the first fixed effect and sexual genotype (XY, YY, WX and WY) the second one. Sexual Genotype is