similar to: Atomic setting of properties?

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 everyone, I'm having trouble adding error bars to a grouped barchart in lattice. I know that this topic has been addressed quite a bit, as I've been searching the internet for a while to try to troubleshoot the issue, but I've not been able to find any solution that I could get working on my data. I was wondering if someone could look at my code and tell me what I'm doing

I apologize if this comes across as confusing. I will try to explain my situation as best I can. I have R bootstrapping my growth data for fish. It's resampling my database of age and length data and then produces several new datasets for me. In this case, it's resampling my data to create three new datasets of age and length data. Here is my code with my original data called

Hi, The data (attached) I am looking at consists of measurements of growth rate at different ages, for individuals in two treatments (control and infected). What I want to know is whether and when (what age) the growth rate of infected individuals is higher than the growth rate for control individuals. The simplest way to approach this question is to just do a t-test at each age, but because

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 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>

I have the following script for generating a dataset. It works like a champ except for a couple of things. 1. I need the variables "itbs" and "map" to be negatively correlated with the binomial variable "lunch" (around -0.21 and -0.24, respectively). The binomial variable "lunch" needs to remain unchanged. 2. While my generated variables do come out

Hi R user, Would you mind to help me to find the range with stochastic events? For example, daT<-structure(list(sn = 1:14, growthrate = c(0.5, 0.6, 0.7, 0.99, 0.1, 0.3, 0.4, 0.5, 0.5, 0.2, 0.1, 0.4, 0.3, 0.43)), .Names = c("sn", "growthrate"), class = "data.frame", row.names = c(NA, -14L)) I want to find the ranges of growth rate of the above data using Mote

On 17 Jan 2017, at 01:17, Chris Lattner via llvm-dev <llvm-dev at lists.llvm.org> wrote: > - The download size of a mono-repo is manageable, and seems scalable for a project the size of LLVM (including reasonable growth over the next 10 years). Did you consider that GitHub has a 1GB size limit/recommendation? https://help.github.com/articles/what-is-my-disk-quota/ The full mono-repo is

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

Dear R-list members, Some months ago I wrote a message on the usage of gnls (nlme library) and here I come again. Let me give an example: I have a 10 year length-at-age data set of 10 fishes (see growth.dat at the end of this message) and I want to fit a von Bertalanffy growth model, Li= Linf*(1-exp(-k*(ti-t0))) where Li = length at age i, Linf= asymptotic length, k= curvature parameter, ti=

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 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 tried to calculate tree mean growth but I think the structure I used below (growthresumo) is not the most elegant, even though it worked. The only problem I had in this first part was that I cannot use 'summary', just 'mean' (sorry but 'R' is pretty new for me). >growthresumo <-

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 analyzing a dataset on the effects of six pesticides on population growth rate of a predatory mite. The response variable is the population growth rate of the mite (ranges from negative to positive) and the exploratory variable is a categorical variable (treatment). The experiment was blocked in time (3 blocks / replicates per block) and it is unbalanced - at least 1 replicate per block. I am

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

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 realized that there was a typo error. I mean "Monte Carlo Simulation" ________________________________________ From: R-help <r-help-bounces at r-project.org> on behalf of Kristi Glover <kristi.glover at hotmail.com> Sent: April 19, 2016 11:48 PM To: R-help Subject: [R] simulation in R Hi R user, Would you mind to help me to find the range with stochastic events? For

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')