similar to: Growth rate determination using ANCOVA

Could you please help me on the following ANCOVA issue? This is a part of my dataset: sampling dist h 1 wi 200 0.8687212 2 wi 200 0.8812909 3 wi 200 0.8267464 4 wi 0 0.8554508 5 wi 0 0.9506721 6 wi 0 0.8112781 7 wi 400 0.8687212 8 wi 400 0.8414646 9 wi 400 0.7601675 10 wi 900 0.6577048 11 wi 900

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

Hi there, The following data is obtained from a long-term experiments. > mydata <- read.table(textConnection(" + y year Trt + 9.37 1993 A + 8.21 1995 A + 8.11 1999 A + 7.22 2007 A + 7.81 2010 A + 10.85 1993 B + 12.83 1995 B + 13.21 1999 B + 13.70 2007 B + 15.15 2010 B + 5.69 1993 C + 5.76 1995 C + 6.39 1999

Dear all, I'm quite sure that this is a stupid question, but I'll ask anyway. I want to perform an ANCOVA with two continuous factors and three categorical factors. Plant population growth rate (GR) = dependent variable Seed reduction due to herbivory (SR) = continuous explanatory variable Herbivore species (HS, 2 levels) = categorical explanatory variable Population (Pop, 24 levels) =

R-users - I must preface this question by saying that I'm a relative newbie to both R and mixed-modeling. I'm using lme fit an ANCOVA-like model. My data consist of bone length measurements for a developmental series of two capuchin monkey species. I'm interested in whether the rate of bone length scaling to body mass (i.e., growth) differs between species. My call to lme

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

Dear All, I am a researcher doing research in plant growth and I have a statistical problem that seems to not be able to handle. Recently, I conducted an experiment about plant growing in three different nutrient-level sediments. I harvested these every three week (three harvests in all). Some growth traits of these plants were recorded (e.g. total biomass, leaf biomass and stem biomass). In

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>

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

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

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 >

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 +

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')

Hi, As part of my dissertation, I'm going to be doing an Anova, comparing the "dead zone" diameters on plates of microbial growth with little paper disks "loaded" with antimicrobial, a clear zone appears where death occurs, the size depending on the strength and succeptibility. So it's basically 4 different treatments, and I'm comparing the diameters (in mm) of

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,

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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

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:

Dear R-help folks, Can someone guide me to a source where I can learn more about the / operator in model formulae? I found a reference to it in Venables and Ripley's MASS, p. 142, where it says, in reference to ANCOVA: "Terms of the form a/x, where a is a factor, are best thought of as 'separate regression models of type 1 + x within the levels of a.'..." This seems