similar to: [LLVMdev] Register Allocator based on Live Range Growth

Hi everyone, I have a quick and probably easy question about lme for this list. Say, for instance you want to model growth in pituitary distance as a function of age in the Orthodont dataset. fm1 = lme(distance ~ I(age-8), random = ~ 1 + I(age-8) | Subject, data = Orthodont) You notice that there is substantial variability in the intercepts (initial distance) for people at 8 years, and that

I would like to install the package "growth" as it contains the function "corgram" and some other presumably useful stuff for time series analysis. I can see it is in R standard library list: http://hosho.ees.hokudai.ac.jp/~kubo/Rdoc/doc/html/packages.html<http://hosho.ees.hokudai.ac.jp/%7Ekubo/Rdoc/doc/html/packages.html>

Hi, This is not a sophisticated statistics question as the subject suggests. But I am logging data - number of user sessions in a web application - before and after new users are migrated. I use R to graph but I am looking for a way to quantify the growth in the number of user sessions. As more users are now using the web application there is a growth. What is the way to measure this

I'm a programmer in a biology lab who is starting to use R to automate some of our statistical analysis of growth rate determination. But I'm running into some problems as I re-code. 1) Hypotheses concerning Slope similarity/difference: I'm using R's anova(lm()) methods to analyse a model which looks like this: growth.metric ~ time * test.tube I understand that

Hi, Andrey, sorry for the delay: I made a page with the code available for download: http://homepages.dcc.ufmg.br/~douglas/projects/RangeAnalysis/RangeAnalysis.html Feel free to get it, and if you need some help, I will be happy to tell you how to set the analysis up, in case the explanation in the page is not good. I also have a report describing the implementation here: (

Dear all, I am running matrix population models using package "popbio". In a deterministic model {i.e., transition matrix is defined as A <- matrix(c(0.70, 0.70,0.35,0.50), nrow=2,byrow=TRUE}, population growth rate can be estimated from the dominant eigenvalue {command "eigen.analysis"}. However, I cannot figure out the way to compute the asymptotic stochastic population

Can't read your paper because the permission is not set. Chuck On 2/25/2011 12:16 PM, Douglas do Couto Teixeira wrote: > Hi, Andrey, > > sorry for the delay: I made a page with the code available for > download: > http://homepages.dcc.ufmg.br/~douglas/projects/RangeAnalysis/RangeAnalysis.html >

Dear all, I have some growth curve data from an experiment that I try to fit using lm and lmer. The curves describe the growth of classification accuracy with the amount of training data t, so basically y ~ 0 + t (there is no intercept because y=0 at t0) Since the growth is somewhat nonlinear *and* in order to estimate the treatment effect on the growth curve, the final model is y ~ 0 + t +

I am having trouble using Jim Lindsey's libraries because of unresolved symbols. I am currently using R 0.65.0, but had this problem in earlier releases as well. I have a RedHat 6.0 Linux on i386 and use egcs (upgraded to that distributed with rawhide, after first failures with the libraries): egcs-g77-1.1.2-19 egcs-1.1.2-19 Installation of the libraries works but on loading >

What is the R way of computing cumulative growth rates given a series of discrete values indexed . For instance, given a matrix of 20 observations for each of 5 series (zz), what is the most straight forward technique in R for computing cumulative growth (zzcum) ? It seems for the solution I'm after might be imbedding the following cum growth rate calc as a function into a function call

Dear community, I'm trying to model growth with this function: Yi = A* exp(-k*(1/ti^m)) ; A asymptote, k rate of decrease of the relative growth rate, m shape parameter. I don't have variable time so, finally, following some papers, I try to fit Yi+a = A*exp(-k* (1/(-k/(log(Yi/A)))^(1/m)+a)^m); a= 10 I've tried: a) nls.1 <- nls(Yi+a ~A*exp(-k*

Hello, I am new to R and I am having problems trying to model logistic population growth with the deSolve package. I would like to run the model for four populations with the same initial population and carrying capacity but with different growth rates and put the results into a data frame. When I run the following lines of code I get unexpected results from "output" but the format is

We are constructing growth charts (age/weight and age/length) for children with diagnosis that impacts weight/length. But we we don't know how to use R for producing growth charts. We are collection data of Age, Weight and Length. The data are used to produce diagnosis-specific Growth charts (like the CDC Growth Charts:

I'm trying to understand how to plot individual growth curve trajectories, with the overall mean trajectory superimposed (preferably in a slightly thicker line, maybe in black) over the individual trajectories. Using the sleepstudy data in lme4, here is the code I have so far: library(lme4) library(lattice) xyplot(Reaction ~ Days, data = sleepstudy, group = Subject, type = 'l')

Hello, Having measured two populations' characteristics at one particular time[with great precision] with R, I would like to extend this to measuring the same populations starting at t1, and then again at t2, and try to develop a growth model (something like dpop1/dt=r*pop^(...),dpop2/dt=r*pop^(...)). I think the idea is to create a model that will predict the growth of a population(N(mu,

Hi!! Im doing my graduated work in Onion Curves Growth with Nonlinear Models, I'm amateur in R so i have doubt how i put or program next models, http://r.789695.n4.nabble.com/file/n3236748/96629508.png Also, i cant derivate for Gauss Model, and Richard Model dont have funtion, If someone could help me, i was so grate, -- View this message in context:

Hi R experts, I am interested on the effects of two dietry compunds on the growth of chicks. Rather than extracting linear growth functions for each chick and using these in an analysis I thought using ReML might provide a neater and better way of doing this. (I have read the pdf vignette("MlmSoftRev") and "Fitting linear mixed models in R" by Douglas Bates but I am not

Dear all, I would like to hear from anyone who has experience using R to simulate and visualise the formation and growth of crystals. Thank you. mpl -- View this message in context: http://r.789695.n4.nabble.com/Modelling-Crystal-Growth-tp2268746p2268746.html Sent from the R help mailing list archive at Nabble.com.

Hi Isn't this exactly what we _don't_ wanna do?! =) I suppose TDM and VoIP is supposed to interconnect not to be separated. i think it's nice with a busy list, it means some real hot stuff is happening, and that's good! rgds /staffan -----Ursprungligt meddelande----- Fr?n: Luciano Ramos [mailto:lramos@telviso.com.ar] Skickat: den 9 januari 2004 14:12 Till:

Hello, It's not easy to express clearly what I have in mind by stochastic growth models. I've been working with a couple of titles -Stochastic Models in Biology, for one, but both date back a decade or more. I'll try to illustrate the model a little more. A reasonably comparable situation is Bolker's analysis of how many flowers are created on a given plant among a patch of