similar to: Remove reciprocal data from a grouped animal social contact dataset

Hi R help forum, I have a simple data frame of four columns - one of numbers (really a categorical variable), one of dates and one of data. I have over 500,000 data points to work with, spread over 40 files, each named after a different animal. These are contact data recorded by proximity loggers over two years between the animals of the file name and collars being worn by other animals. The

Hi I'm trying to measure the effect of a policy intervention (Box and Tiao, 1975). This query has to do with the coding of the model rather than with the particulars of my dataset, so I'm not providing the actual dataset (or a simulated one) in this case, apart from some general description. The time series are of length n=34 (annual observations between 1977 and 2010). The policy

Hi R forum, Each row of my data (below) show a new contact event between animals. In order to ultimately look at the patterns of intervals between contacts, I need to calculate a contact end time. The contact starts at the date and time shown in V4 and V5, and lasts for the duration shown IN SECONDS in V6: > data2<- read.csv(file=file.choose(), header=F, sep=" ") >

Dear expeRts, I would simplify following code. --------------------------------------------- youtput <- function(x1, x2){ n <- length(x1) y <- vector(mode="numeric", length=n) for(i in 1:n){ if(x1[i] >=5 & x1[i] <= 10 & x2[i] >=5 & x2[i] <=10) y[i] <- 0.631 * x1[i]^0.55 * x2[i]^0.65 if(x1[i] >=10 & x1[i] <= 15 &

Coming off of xen environment; still testing with kvm, just a few questions: 1. What is the roadmap for release of qemu-kvm 1.1 and libvirt 0.10.2 for el6, cause I had to compile from upstream to get the latest stuff. 2. Should not virt-manager show the sparsed disk size instead of actual reserved size of a vm? 3. Where is the virsh bash_completion conf.d file from upstream; since el6 rpm

Right now the functionality is available, sometimes, from the C standard library. But embedded environments (often running a limited standard library) and server environments would benefit heavily from a standard way to specify context switches within a single thread in the style of makecontext/swapcontext/setcontext, and built-in support for these operations would also open the way for

I am having trouble fitting correlation structures within nlme. I would like to fit corCAR1, corGaus and corExp correlation structures to my data. I either get the error "step halving reduced below minimum in pnls step" or alternatively R crashes. My dataset is similar to the CO2 example in the nlme package. The one major difference is that in my case the 'conc' steps are

Dear all, When analyzing data from a climate change experiment using linear mixed-effects models, I recently came across a situation where: - the summary(model) showed a significant difference between the levels of a two-level factor, - while the anova(model) showed no significance for that factor (see below). My question now is: Is the anova.lme() approach correct for that model? And why does

On Sun, Apr 11, 2010 at 4:09 PM, Jeffrey Yasskin <jyasskin at google.com> wrote: > Kenneth Uildriks <kennethuil at gmail.com> wrote: >> As I see it, the context switching mechanism itself needs to know >> where to point the stack register when switching.  The C routines take >> an initial stack pointer when creating the context, and keep track of >> it from

I need some help understanding how on of the example data sets is formatted in the basic R installation. If I load the Mona Loa CO2 data, with the command: > data(co2) I can view the data with: > co2 And the data are in the form of 11 rows labeled as years (1994-2004) and 12 columns labeled (Jan - Dec). This structure appears to be a dataframe, however, if I type the command

Hi I have some experimental data where I have counts of the number of insects collected to different trap types rotated through 5 different location (variable -location), 4 different chemical attractants [A, B, C, D] were applied to the traps (variable - semio) and all were trialled at two different CO2 release rates [1, 2] (variable CO2) I also have a selection of continuous variables

Dear list users, I have trouble with covariance analysis. I measured nitrate concentrations in the soil (NO3) and the percentage of legumes (LEG, continuous), affected by 2 different CO2 concentrations (CO2, discrete). I suspect that CO2 has an effect on LEG and NO3, but also that LEG has an effect on NO3, so this is the formula I wrote to test this: NO3 ~ CO2 + LEG + CO2:LEG Will LEG be

Dear R users I have two questions about estimating the spectral power of a time series: 1) I came across a funny thing with the following code: data(co2) par(mfrow=c(2,1)) co2.sp1<-spectrum(co2,detrend=T,demean=T,span=3) co2.sp2<-spectrum(co2[1:468],detrend=T,demean=T,span=3) The first plot displays the frequencies ranging from 0 to 6 whearas the second plot displays the same curve but

Dear All, I'd like to fit a "kind" of logistic model to small data-set using nonlinear least-squares regression. A transcript of R-script are reproduced below. Estimated B and T (the model's coeff, herein B=-8,50 and T=5,46) seem appropriate (at least visually) but are quite diff from those obtained w/ SPSS (Levenberg-Marquardt): B=-19,56 and T=2,37. Am I doing something wrong in

Hi: I have have taken over from a colleague who prepared an R package and failed to build it on Windows. I am doing this with unix as I am a mac user. Below is the output I get when I use the build command: [gattuso:unix/R/CO2.Rcheck] gattuso% R CMD build CO2 * checking for file 'CO2/DESCRIPTION' ... OK * preparing 'CO2': * checking whether 'INDEX' is up-to-date ...

Hello, (Sorry for the repost, i am resending in plain text) I have a function 'ewrap' (see below for definition). It takes 3 expressions and returns another expression e.g. map <- ewrap({ len <- length(r$addon) rhcollect(len,1) }) becomes: expression({ NULL result <- mapply(function(.index, k, r) { { len <- length(r$addon)

Is there a conventional way to test for nested factors? I.e., if 'a' and 'b' are lists of same-length factors, does each level specified by 'a' correspond to exactly one level specified by 'b'? The function below seems to suffice, but I'd be happy to know of a more succinct solution, if it already exists. Thanks, Tim. --- "%nested.in%" <-

Hi everyone I'm using Tukey HSD as post-hoc test following a lme analysis. I'm measuring hemicelluloses in different species treated with three different CO2 concentrations (l=low, m=medium, h=high). The whole experiment is a split-plot design and the Tukey-function from the package multcomp is suitable for lme-analysis with random factors. The analysis works fine but I get a non

Hello, First a disclaimer :) I am very new to using R. I am generating some plots and eventhough I can get colored output in the encapsulated postscript files in the simplest of commands (e.g. plot(1:10,1:10, type="l", col="red") ), it does not work for the particular plots I want. It works on the screen. Here is an example taken out from "Mixed-Effects Models in S and

Hello , I'm new to R and don't really understand how to use the function "apply" instead of a "for loop", particularly for a function with multiple entries. I have a big data file and would like to apply a function in multi thread to accelerate the processus. I have a data frame containing values of* CO2 in ppm (resp[i,6])* that I want to convert in umol of CO2