similar to: NLS bi exponential Fit

I'm using a rather strange config, borne out of neccessity rather than choice at home. My internet 'router' is a Win2000 Pro box running Winroute, and my three Linux boxen (running 7.2/8.0/9.0 RH) are networked thru to the Win box using SNAT on the box I work on (don't ask why - it's just pratical, and I can't afford a hub/switch to do this). In any case, it's a

I have a RHEL5 Server and some dual-boot XP/CentOS 5 systems (Linux systems all 64-bit). All Linux is out-of-box, with all packages, minus international languages, installed. No patching has been done. On the server, I selected system-config-authentication and enabled LDAP for User Information, Kerberos, LDAP, and SMB for Authentication, and Shadow and MD5 Passwords, along with

Dear R specialists, I'm working with time series and want to investigate the relationship between two time series by correlation analysis or by fitting a gen. lineair model to the plot of x(timeserie1) and y(timeserie2). Lin1 <- data.frame( Nr = c(1:lengte), NDII = window(ts.mNDII,c(1998,10),c(2003,11)), InvERC = window(Inv.ERC,c(1998,10),c(2003,11)) )

Hi all, I am not a mathematician and I am trying to fit a function which could fit my observed data. Which function should I use and how could I fit it to data in R? Below are the data: x <- c(0, 9, 17, 24, 28, 30) y <- c(500, 480, 420, 300, 160, 5) I use R for Mac OS, version 2.10-1 2009-08-24 Thank you for your help. Vincent. [[alternative HTML version deleted]]

Hi, I have a problem fitting the following Weibull Model to a set of data. The model is this one: a-b*exp(-c*x^d) If I fitted the model with CurveExpert I can find a very nice set of coefficients which create a curve very close to my data, but when I use the nls.lm function in R I can't obtain the same result. My data are these: X Y 15 13 50 13 75 9 90 4 With the commercial

This data are kilojoules of energy that are consumed in starving fish over a time period (Days). The KJ reach a lower asymptote and level off and I would like to use a non-linear plot to show this leveling off. The data are noisy and the sample sizes not the largest. I have tried selfstarting weibull curves and tried the following, both end with errors. Days<-c(12, 12, 12, 12, 22, 22, 22,

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! when i have busy signal on analog line (zap card) it doesn't detect that line is busy ? is it possible to change detected sequence (frequency) of busy tones on line (zapata.conf ??) tnx, Thomas my zapata.conf [channels] language=en context=lin1 signalling=fxs_ks channel => 1 group=1 echocancel=yes echocancelwhenbridged=yes rxgain=3.0 txgain=3.0 busydetect=yes

I am using Microsoft .NET (2.0) PrintPreviewDialog to preview reports, and all works well when the Windows default printer is set to a local printer or a network printer on a Windows server, but if I set it to a Samba network printer, it slows down to a crawl. With a Windows server network printer, it takes about 10 seconds to render 100 pages. With a Samba network printer, it takes over 5

Dear sirs It seems like there is a bug in `profile.nls` with `algorithm = "plinear"` when a matrix is supplied on the right hand side. Here is the bug and a potential fix ##### # example where profile.nls does not work with `plinear` but does with # `default` require(graphics) set.seed(1) DNase1 <- subset(DNase, Run == 1) x <- rnorm(nrow(DNase1)) f1 <- nls(density ~ b1/(1 +

Hi, Thanks in advance for any advice you can give me, I am very stumped on this problem... I use R every day and consider myself a confident user, but this seems to be an elementary problem.. Outline of problem: I am analysing the results of a study on protein expression in cancer tissues. I have raw intensities from 2 different types of cancer and normal tissue, which can be taken from several

Package: logcheck Version: 1.2.69 The current ruleset "kernel" provided with this logcheck package don't match entries where the kernel timeline has leading spaces, like: [ 42.302707] For example, the following entry: Feb 4 17:05:24 hostname kernel: [ 144.591487] tun: Universal TUN/TAP device driver, 1.6 didn't matched the re: ^\w{3} [ :0-9]{11} [._[:alnum:]-]+

Hi, I have a question about using "lme" and "aov" for the following dataset. If I understand correctly, using "aov" with Error term in the formula is equivalent to using "lme" with default settings, i.e. both assume compound symmetry correlation structure. And I have found that equivalency in the past. However, with the follwing dataset, I got different

Hi, I have a question about using "lme" and "aov" for the following dataset. If I understand correctly, using "aov" with an Error term in the formula is equivalent to using "lme" with default settings, i.e. both assume compound symmetry correlation structure. And I have found that equivalency in the past. However, with the follwing dataset, I got different

Hi all, Sent the following question yesterday, but haven't got any suggestions yet. So just trying again, can anyone comment on the problem that I have? Thank you! ------------- Hi, I have a question about using "lme" and "aov" for the following dataset. If I understand correctly, using "aov" with an Error term in the formula is equivalent to using

Hi Bert, Thanks for the suggestions. I tried lme with different control parameters, and also tried using "ML", instaed of "REML", but still got the same answers. Yes, I hope some gurus on this list could give me some hints. Thanks --- "Gunter, Bert" <bert_gunter at merck.com> wrote: > But they are close. This is almost certainly a > numeric issue --

Thank you for reading. I am trying to get sphericity values, and I understood I need to use mlm, but how do I implement a nested within subject design in mlm? I already read the R newsletter, fox chapter appendix, EZanova, and whatever I could find online. My original ANOVA anova(aov(resp ~ sucrose*citral, random =~1 | subject, data = p12bl, subset = exps==1)) Or anova(aov(resp ~