similar to: nls error control

HI I'm a student in chemical engineering, and i have to implement an algoritm about FIVE PARAMETERS INTERPOLATION for a calibration curve (dose, optical density) y = a + (c - a) /(1+ e[-b(x-m]) where x = ln(analyte dose + 1) y = the optical absorbance data a = the curves top asymptote b = the slope of the curve c = the curves bottom asymptote m = the curve X intercept Have you never seen

Dear R community, In the excellent nlme package the default diagnostic plot graphs the innermost residuals against innermost fitted values. I recently fit a mixed-effects model in which there was a very clear positive linear trend in this plot. I inferred that this trend occurred because my fixed effect was a two-level factor, and my random effect was a 12-level factor. The negative residuals

I don't get the same result, do you have a package loaded that would change the default behavior (such as Hmisc)? > list(grp.1) [[1]] [1] 1 2 Levels: 1 2 > list(grp.1[mask]) [[1]] [1] 1 Levels: 1 2 > library(Hmisc) <<snip>> > list(grp.1[mask]) [[1]] [1] 1 Levels: 1 --Matt > version _ platform i386-pc-mingw32 arch i386 os

Greetings all, I'm trying to figure out how to calculate the inverse CDF (i.e. a quantile) for a non-central F distribution. I could put together a quick numerical solver routine using the CDF, but I wonder if there's a function that I've missed that would be more efficient? Thank-you, Andrew Andrew Robinson Ph: 208 885 7115 Department of Forest Resources Fa: 208 885

Hi all, I'm using R 1.5.1 on Windows 2000. The following snippet of code doesn't seem to do anything - no error is reported, and there is no name change. names(myFrame[,c(1:3)]) <- c("name1", "name2", "name3") This code however works nicely: names(myFrame)[c(1:3)] <- c("name1", "name2", "name3") Can anyone suggest why

Hi everyone, How might I go about configuring R to keep add-on packages loaded from session to session? Is this undesireable for some reason? At present, I keep a file called "pckgs.txt" in my working directory with, e.g., library(gnlm) library(Hmisc) ... and then type source("add-ons.txt") every time I start a new session. I suspect there's a more elegant way to hold

Hello. I am trying to fit a non-rectangular hyperbola function to data of photosynthetic rate vs. light intensity. There are 4 parameters that have to be estimated. I find the nls function very difficult to use because it often fails to converge and then gives out cryptic error messages. I have tried playing with the control parameters but this does not always help. Is there another

Hi all, I'm trying to find a way to get xyplot to produce a second set of axes outside the right hand side of the graph. This is my progress so far: EE <- equal.count(ethanol$E, number=9, overlap=1/4) xyplot(NOx ~ C | EE, data = ethanol, prepanel = function(x, y) prepanel.loess(x, y, span = 1), xlab = "Compression Ratio", ylab = "NOx (micrograms/J)",

Hi. I am using Windows version of R 1.8.1. Being somewhat new to survival analysis, I am trying to compare cph (Design) with coxph (survival) for use with a survival data set. I was wondering why cph and coxph provide me with different confidence intervals for the hazard ratios for one of the variables. I was wondering if I am doing something wrong? Or if the two functions are calculating hazard

Dear R friends, I know that this topic has been mulled over before, and that there is a substantial difference between the convergence criteria for JMP and those for R. I apologize that this is somwehat raking cold coals. Summary: A model/data combination achieves convergence in JMP, and survives a reasonably rigorous examination (sensible parameter estimates, well-behaved surface,

If I place par(new=T) before I create a plot in a script that is sent to a pdf device, the pdf is unopenable and reports itself as having no pages. The postscript device seems to ignore the par instruction. I guess one of these is a bug, but I don't know which one! --please do not edit the information below-- Version: platform = i386-pc-linux-gnu arch = i386 os = linux-gnu system =

Hello, has anyone tried to get R running on a Sharp Zaurus 5500? Andrew Andrew Robinson Phone: 208-885-7115 Department of Forest Resources Fax: 208-885-6226 University of Idaho E: andrewr at uidaho.edu Po Box 441133 WWW: http://www.uidaho.edu/~andrewr Moscow, ID 83843 and: http://www.biometrics.uidaho.edu/ No statement above

Hello, I have been searching for ways to obtain these for combinations of fixed factors and levels other than the 'baseline' group (contrasts coded all 0's) from a mixed-effects model in lme. I've modelled the continuous variable y as a function of a continuous covariate x, and fixed factors A, B, and C. The fixed factors have two levels each and I'd like to know whether

Hello all, Here's my query: Running R 1.6.2 on FreeBSD 5.0, and on WinXP, and I find that the following hangs the process: dchisq(alpha=0.01, df=1, ncp=295) but it does work for ncp < 294.92. Is this general? Best wishes to all, Andrew Andrew Robinson Ph: 208 885 7115 Department of Forest Resources Fa: 208 885 6226 University of Idaho E : andrewr at uidaho.edu PO

Dear R-community, I'm using Sweave to produce reports. The reports require the "axis" command. When I run axis the program returns NULL as well as creating the axis. > plot(1:4, rnorm(4), axes=FALSE) > axis(1, 1:4, LETTERS[1:4]) NULL > So, my Sweave tex files have \begin{Schunk} \begin{Soutput} NULL \end{Soutput} \end{Schunk} in front of each graphic that requires

Greetings R-community, I'm running simulations within R that I wrote in C. The simulations require fitting that occasionally fails to finish. I was wondering if there is any kind of tool for process control in R, such that after e.g. 15 minutes I could kill the process, record the state for post-hoc analysis, and move to the next simulation? I'm running FreeBSD and could almost surely

I would appreciate some thoughts on using the bootstrap functions in the library "bootstrap" to estimate confidence intervals of ICC values calculated in lme. In lme, the ICC is calculated as tau/(tau+sigma-squared). So, for instance the ICC in the following example is 0.116: > tmod<-lme(CINISMO~1,random=~1|IDGRUP,data=TDAT) > VarCorr(tmod) IDGRUP = pdLogChol(1)

I have two lists: xa <- list( X=c(1,2,3), Y=c(4,5,6), Z=c(7,8,9) ) xb <- with( barley, tapply( X=seq(1:nrow(barley)), INDEX=site , FUN=function(z)yield[z])) I can convert xa to a dataframe easily with: as.data.frame(xa) But if i try the same with xb I get: as.data.frame(xb) Error in as.data.frame.default(xb) : can't coerce array into a data.frame What

< Does anyone know why they're called random deviates, as opposed to random numbers?> Others will probably give you some technical reason about random numbers can be considered as random deviates from a mean (I think at least the 1875 Galton paper at http://www.mugu.com/galton/ uses similar terminology (I'm not claiming this is the earliest use - just the easiest to access at the

Dear I used to use R under windows. I always save my code in a txt file. When I want to run something, I just copy and paste to the windows. I recently need to do a big simulation work which will cost 5 or 6 days. I am afraid the memory of windows can not cope with this situation. I now want to run the code under unix. However, I do not know how to run this code in txt file under unix. I