similar to: variables in object names

Does anyone know of any issues with nesting source() calls within multiple scripts? I have at least one script which always finds errors when I source it but runs fine when run on its own. It containd source() calls to other scripts and it seems to fail during the first nested source() command. Ken Kenneth B. Pierce Jr. Research Ecologist Landscape Ecology, Modeling, Mapping and Analysis

Is there a way to run a simple perl script from R? Kenneth B. Pierce Jr. Research Ecologist Landscape Ecology, Modeling, Mapping and Analysis Team PNW Research Station - USDA-FS 3200 SW Jefferson Way, Corvallis, OR 97331 ken.pierce@oregonstate.edu 541 750-7393 http://www.fsl.orst.edu/lemma/gnnfire http://www.fsl.orst.edu/R_users/index.php [[alternative HTML version deleted]]

Dear R wizes, I have a data.frame of species abundances with column names consisting of 4 letter codes then an underscore and a number like this: abco_1, abco_2, abco_3, psm_1, psme_2, psme_3, etc. I would like to get an identifier for all the abco, and psme and other species etc. I used spec.fact<-substring(names(spec.count),1,4) To make a vector of the first 4 letters of

Dear all, For an introductory course on glm?s I would like to create an example to show the difference between glm and transformation of the response. For this, I tried to create a dataset where the variance increases with the mean (as is the case in many ecological datasets): poissondata=data.frame( response=rpois(40,1:40), explanatory=1:40) attach(poissondata) However, I have run into

#I am trying to understand how R fits models for contrasts in a #simple one-way anova. This is an example, I am not stupid enough to want #to simultaneously apply all of these contrasts to real data. With a few #exceptions, the tests that I would compute by hand (or by other software) #will give the same t or F statistics. It is the contrast estimates that R produces #that I can't seem to

hello all, I am trying to use the function getCovariateFormula(nlme) in conjunction with the library lme4. When I load both packages I get the following message and the getCovariateFormula function no longer works: library(nlme) library(lme4) Attaching package 'lme4': The following object(s) are masked from package:nlme : contr.SAS getCovariateFormula

I am running an lme model with the main effects of four fixed variables (3 continuous and one categorical – see below) and one random variable. The data describe the densities of a mite species – awsm – in relation to four variables: adh31 (temperature related), apsm (another plant feeding mite) awpm (a predatory mite), and orien (sampling location within plant – north or south). I have read

Dear R users, I am using lme and nlme to account for spatially correlated errors as random effects. My basic question is about being able to correct F, p, R2 and parameters of models that do not take into account the nature of such errors using gls, glm or nlm and replace them for new F, p, R2 and parameters using lme and nlme as random effects. I am studying distribution patterns of 50 tree

If I have a model line = lm(y~x1) and I want to use a for loop to change the number of explanatory variables, how would I do this? So for example I want to store the model objects in a list. model1 = lm(y~x1) model2 = lm(y~x1+x2) model3 = lm(y~x1+x2+x3) model4 = lm(y~x1+x2+x3+x4) model5 = lm(y~x1+x2+x3+x4+x5)... model10. model_function = function(x){ for(i in 1:x) { } If x =1, then the list

Hello list, I have a linear regression: mylm = lm(y~x-1) I've been reading old mail postings as well as the plotmath demo and I came up with a way to print an equation resulting from a linear regression: model = substitute(list("y"==slope%*%"x", R^2==rsq), list(slope=round(mylm$coefficients[[1]],2),rsq=round(summary(mylm)$adj.r.squared, 2))) I have four models and I

hello everybody. i need to log "who did what" on some models. i was reading recipe 59 of rails recipes and i created something like that: class LogSweeper < ActionController::Caching::Sweeper observe :model1, :model2, :model3, :model4, :model5, ... after_save(model) save_log(model, "save") end after_destroy(model) save_log(model, "destroy) end

I have 5 GAMs ( model1, model2, model3, model4 and model5) Before I use some data X(predictor -January to June data) to form a equation and calculate the expected value of Y (predictand -January to June). After variable selection, GAMs (Model 1)were bulit up! R-square :0.40 NOW, I want to use new X'( predictor -July - December data) and put into Model 1, then get the expected value of Y'

Dear all, I need to fit a gee model with an auto-regressive correlation structure and I faced some problems. I attach a simple example: ####################################################### library(gee) library(geepack) # I SIMULATE DATA FROM POISSON DISTRIBUTION, 10 OBS FOR EACH OF 50 GROUPS set.seed(1) y <- rpois(500,50) x <- rnorm(500) id <- rep(1:50,each=10) # EXAMPLES FOR

Dear R user I have 2 problems with lmer. The statistical consultance service of my university has recomended to me to expose those problems here. Sorry for this quite long message. Your help will be greatly appreciated... Gilles San Martin 1) anova() I fit a first model : model1 <- lmer(eclw~1 + density + landsc + temp + landsc:temp + (1|region) + (1|region:pop) + (1|region:pop:family),

Dear R users, Could somebody please help me to find a way of comparing nonlinear, non-nested models in R, where the number of parameters is not necessarily different? Here is a sample (growth rates, y, as a function of internal substrate concentration, x): x <- c(0.52, 1.21, 1.45, 1.64, 1.89, 2.14, 2.47, 3.20, 4.47, 5.31, 6.48) y <- c(0.00, 0.35, 0.41, 0.49, 0.58, 0.61, 0.71, 0.83, 0.98,

Hello, I am using the following model model1=lmer(PairFrequency~MatingPair+(1|DrugPair)+(1|DrugPair:MatingPair), data=MateChoice, REML=F) 1. After reading around through the R help, I have learned that the above code is the right way to analyze a mixed model with the MatingPair as the fixed effect, DrugPair as the random effect and the interaction between these two as the random effect as well.

Dear all, I was wondering if anyone could help solve a problem of a missing interaction effect!! I carried out a 2 x 2 factorial experiment to see if eggs from 2 different locations (Origin = 1 or 2) had different hatching success under 2 different incubation schedules (Treat = 1 or 2). Six eggs were taken from 10 females (random = Female) at each location and split between the treatments,

Hello, I am trying to replicate example 4 in the package 'scatterplot3d': s3d.dat_data.frame(cols=as.vector(col(my.model4)), rows=as.vector(row(my.model4)), value=as.vector(my.model4)) scatterplot3d(s3d.dat, type="h", lwd=5, pch=" ", x.ticklabs=colnames(my.model4), y.ticklabs=rownames(my.model4), main="Conditional probabilities, Model 3") Nice! but,

Dear R-Community, I am relatively new with R, so sorry for things which for you might be obvious... I am trying to automatically update lmer formulae. the variables of the model are: depM= my dependent measure Sb2= a random factor OS = a predictor VR= another predictor So, I am building the first model with random intercept only: model = lmer(depM ~ (1 |Sb2)) then I update the formula

Hi guys, Got a few days left and I need to model a random effect of species on the body mass (logM) and temperature (K) slopes. This is what i've done so far that works: model1<-lme(logSSP~logM + K,random=~1|species,data=data1) model2<-lme(logSSP~logM + K,random=~K|species,data=data1) model3<-lme(logSSP~logM + K,random=~logM|species,data=data1) The one I now want is: