search for: neurophysiological

Two post-doctoral positions are available from the Brain Physiology Laboratory in Paris (France). The first position is available immediately to work on spike sorting and spike train analysis. It consists in both data analysis and methods development. Familiarity with neurophysiology is not required. Familiarity with R and with some of the methodologies used: clustering, classification,

am I right in the assumption, that for calculation of the condition numbers I have to use the correlation matrix of X, and not t(x) %*% x? > e <- eigen(t(x) %*% x) better (x must not have a first column of ones): > e <- eigen(cor(x)) > e$val [1] 6.6653e+07 2.0907e+05 1.0536e+05 1.8040e+04 2.4557e+01 2.0151e+00 > sqrt(e$val[1]/e$val) [1] 1.000 17.855 25.153 60.785 1647.478

I want to migrate my R workspace databases (e.g., .Rdata) from Windows to Linux. The R help file for "save" says that "[a]ll R platforms use the XDR representation of binary objects in binary save-d files, and these are portable across all R platforms." Is it is simple as that? Copy the Windows .Rdata file to the Linux .ext3 file system? Thanks, Derek Derek N. Eder

Christoph -- There was just a thread on this earlier this week. You can search in the archives for the title: "refitting lm() with same x, different y". (Actually, it doesn't turn up in the R site search yet, at least for me. But if you just go to the archive of recent messages, available through CRAN, you can search on refitting and find it. The original post was from William

I am just about working through Faraways excellent tutorial "practical regression and ANOVA using R" on page 24 he makes the x matrix: x <- cbind(1,gala[,-c(1,2)]) how can I understand this gala[,-c(1,2)])... I couldn't find an explanation of such "c-like" abbreviations anywhere. thanks for a hint. another problem: I couldn't load the faraway library, using the

Sorry, I am sure, this must be documented somewhere (but there are that many docs and tutorials to scan for topics..., actually a great thing... but if you are in a hurry..): I want to save a plot as .ps (or .eps): > postscript("plot1.eps", horizontal=FALSE, onefile=FALSE,height=8,width=8,pointsize=10) > plot(hpfit$fit,rstudent(hpfit),xlab="Fitted

I am having trouble understanding what must be a quite simple rule of scope. In the code example below, the argument to the main function, my.subset, would like to be passed to the function LME. This does not happen however. Curiously, the other argument (my.data) is passed to LME. This problem goes away with this: assign("my.subset", my.subset, env = sys.frame()). It works, but it

Dear R-users, dear Prof. Ripley as package maintainer I tried to investigate the odd error, when I call nnet together with a 'weights' parameter, using the 'fgl' example in V&R p 348 The error I get is: Error in eval(expr, envir, enclos) : Object "w" not found I think it is a kind of scoping problem, but I really cannot see, what the problem exactly is. and

Dear R-users I use nnet for a classification (2 classes) problem. I use the code CVnn1, CVnn2 as described in V&R. The thing I changed to the code is: I define the (class) weight for each observation in each cv 'bag' and give the vector of weights as parameter of nnet(..weights = weight.vector...) Unfortunately I get an error during some (but not all!) inner-fold cv runs:

Isn't there a cryptic footnote somewhere in the blue book that describes regular expressions as "a funny little language ..." ? Well, I'm not laughing! I've googled and Man paged, but the GREP-style solution to the following problem still eludes me: ... Using a regular expression in the "pattern" argument to list.files() to restrict the return to filenames which

How is exact pattern matching achieved in GREP (and GREPlike) functions ? # Want: listing of all object names that end in *.lm > objects(pattern="*.lm",pos=1) # ... but get: all objects that partially match *.lm, e.g., *.lme [1] "j3.lm" "J3.lme" "j8.lm" "J8.lme" # Want: position of string "4jan2002" in vector >

I hope this question is adequate for this list I use the nnet code from V&R p. 348: The very nice and general function CVnn2() to choose the number of hidden units and the amount of weight decay by an inner cross-validation- with a slight modification to use it for classification (see below). My data has 2 classes with unequal size: 45 observations for classI and 116 obs. for classII With