similar to: nlme graphics in a loop problem

I'm trying to use odesolve for integrating various series of coupled 1st order differential equations (derived from a system of enzymatic catalysis and copied below, apologies for the excessively long set of parameters). The thing that confuses me is that, whilst I can run the function rk4: out <- rk4(y=y,times=times,func=func, parms=parms) and the results look not unreasonable:

Dear R list I have an m * n matrix P and a vector V of length n containing indices for rows in P. For each of the m columns I want to extract the value in the row specified by V, and put these values into a new vector W of length n. At present I am doing this with a for.... loop, but I imagine there is a faster way that doesn?t involve loops. If anyone knows the way I would be most grateful.

I have a question about the lme function in R. My question is: After I got the object from function lme, why the numIter value of the object is always NULL? Following is my code: jjww<-lme(y~x*zz,data=simul,random=~x|group, control=lmeControl(returnObject=TRUE)) attributes(jjww) jjww$numIter the first 20 observation of data simul are: > simul y

I am using rw0641. In my continuing quest to understand repeated measures analysis, I again return to lme. I exported the Potthoff and Roy data Orthodont.dat from S-PLUS 4.5 to avoid capture errors and ran the examples in the R help. I imported the data.frame with data <- read.table("Orthodont.dat",header=T) attach(data) and created the objects Orthodont.fit1 <-

A maybe trivial and stupid question: In the case of a lm or glm fit, it is quite informative (to me) to have a look to the null deviance and the residual deviance of a model. This is generally provided in the print method or the summary, eg: Null Deviance: 658.8 Residual Deviance: 507.3 and (a bit simpled minded) I like to think that the proportion of deviance 'explained' by the

Dear R community, I'm fitting a complex mixed-effects model that requires numerous iterations and function evaluations. I note that nlminb accepts a list of control parameters, including eval.max. Is there a way to change the default eval.max value for nlminb when it is being called from lme? Thanks for any thoughts, Andrew -- Andrew Robinson Department of Mathematics and Statistics

Hi, I have a question about the lme function in R. My question is: After I got the object from function lme, why the numIter value of the object is always NULL? Following is my code: jjww<-lme(y~x*zz,data=simul,random=~x|group, control=lmeControl(returnObject=TRUE)) attributes(jjww) jjww$numIter the first 20 observation of data simul are: > simul y

Hi, I am starting coding in r and one of the things that i want to do is to scrape some data from the web. The problem that I am having is that I cannot get passed the disclaimer page (which produces a session cookie). I have been able to collect some ideas and combine them in the code below but I dont get passed the disclaimer page. I am trying to agree the disclaimer with the postForm and write

Hi, I've had many requests in the past for a Speex DirectShow filter. Well, the wait is now over, thanks to Jean-Francois Emond. Both source and binaries are available from the "Other software" page (http://www.speex.org/projects.html). Bear in mind that for now, only a subset of Speex features are supported (8 kHz, 15 kbps). For those who would like to know what's coming next,

Dear all, I have some problems with importing data from an Access data base via RODBC to R. The data base contains several tables, which all are imported consecutively. One table has a column with column name "NO". If I run the code attached on the bottom of the mail I get no complain, but the column name (name of the respective vector of the data.frame) is "Expr1014" instead

Hi all, I am attempting to learn SQL through sqldf... One task I am particularly interested in is merging separate (presumably large) files into a single table without loading these files into R as an intermediate step (by loading them into SQLite and merging them there). Taking a step back, I've considered these alternatives: 1) I know if I use straight SQLite commands I might use the

Dear list, If anyone knows how to assign dimnames to matrices or arrays I would be most grateful for help. I've tried various permutations of likely-looking code but get error messages every time. I could find no example in the documentation. Many thanks, Dan Bebber Department of Plant Sciences University of Oxford South Parks Road Oxford OX1 3RB UK Tel. 01865 275000

Hello all, I changed the subject line of the e-mail, because the question I''m posing now is different than the first one. I hope that this is proper etiquette. However, the original chain is included below. I've incorporated bits of both Ethan and Brian's code into the script below, but there's one aspect I can't get my head around. I'm totally new to programming

Wolfgang, (cc Linux XFS list) (cc Samba list) Quoting wolfgang.glas@ev-i.at: > I just found you message in the samba mailing list archives and foiund > out, that we have a very similar problem. We are using samba-2.2.1a on a > kerenl-2.4.16 and we use reiserfs. > We also experience data corruption with messages like: > > [2002/03/14 15:15:26, 0] >

Hello all. I was wondering if there is a way to hold constant the fixed effects correlation structure across multiple groups? For example, I have two groups and I fit a three parameter logistic growth curve where the fixed effects are free to vary across the groups. I'll paste in the code as a concrete example: > Result.NLME <- nlme(Score ~ SSlogis(Time, Asym, xmid, scal), +

I hope that the mailing list is the correct forum for the question below. I have trouble calling functions nlsList and nlme from another function. Any help would be greatly appreciated. Jens Praestgaard Human Genome Sciences Rockville MD. I have a data set v with two components, v$mixeddat and v$init. They are listed below: > v$mixeddat conc result rep sample z 11 20.00000000

Hi, I have gote the following data x1 <- c(rep(1,6),rep(4,7),rep(6,10)) x2 <- rnorm(length(x1),6,1) data <- data.frame(x1,x2) and I would like to compute the mean of the x2 for each individual of x1, i. e. x1=1,4 and 6? Thank you very much in advance, Amori [[alternative HTML version deleted]]

Hello, My data are numbers of trees in plots sampled in a number of forest stands. Some stands were subjected to a treatment, others not. Several plots were sampled per stand to get a better idea of what the stand means were, but replication is really at the stand level. Therefore I think this is a split-plot design. I would like to know whether the treatment affected the number of trees, so:

I am trying to carry out density estimation for three dimensions (with anywhere between a few hundred and ~8000 data points). Which leads me to ask: a) is there any equivalent to kde2d (in MASS) or bkde2D (in KernSmooth) out there for three dimensions? b) if not, my skills only seem to extend as far as writing a function which measures density as the number of data points falling within a

? "form" -> "from". Diff against latest SVN: Index: sparse.model.matrix.Rd =================================================================== --- sparse.model.matrix.Rd??? (revision 3336) +++ sparse.model.matrix.Rd??? (working copy) @@ -4,7 +4,7 @@ ?\alias{fac2sparse} ?\alias{fac2Sparse} ?\description{Construct a sparse model or \dQuote{design} matrix, -? form a formula