similar to: Using "apply" instead of "for" loop / multithreading

Hi! Here is some output from an str command: > str(CO2) Classes ‘nfnGroupedData’, ‘nfGroupedData’, ‘groupedData’ and 'data.frame': 84 obs. of 5 variables: $ Plant : Ord.factor w/ 12 levels "Qn1"<"Qn2"<"Qn3"<..: 1 1 1 1 1 1 1 2 2 2 ... $ Type : Factor w/ 2 levels "Quebec","Mississippi": 1 1 1 1 1 1 1 1 1 1 ... $

I making an xyplot and the y label is too long and needs to be in two rows, but when I brake it there is a huge gap between the last text string and the expression, and I can't get rid of it. Any ideas? Data: structure(list(Temp = c(8L, 8L, 8L, 8L, 8L, 8L, 12L, 12L, 12L, 12L, 12L, 12L), CO2 = c(380L, 380L, 380L, 750L, 750L, 750L, 380L, 380L, 380L, 750L, 750L, 750L), Treat = structure(c(3L,

Hi, today i wrote a function in R of the type: index.refraction <- function(Temp,Press, RH, CO2) When i try to plug a negative number in Temp, i got this type of error: " n <- index.refraction(Temp= -40,100,80,CO2) Messages d'avis : 1: In Ops.ordered(left, right) : '-' is not meaningful for ordered factors 2: In Ops.factor(left, right) : - not meaningful for factors 3:

Hi r-help forum, I have been collecting contact data (with proximity logger collars) between a few different species of animal. All animals wear the collars, and any contact between the animals should be detected and recorded by both collars. However, this isn't always the case and more contacts may be recorded on one collar of the two. This is fine, it depends on battery life and other

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

I making an xyplot and the y label is too long and needs to be in two rows, but when I brake it there is a huge gap between the last text string and the expression, and I can't get rid of it. Any ideas? Data: structure(list(Temp = c(8L, 8L, 8L, 8L, 8L, 8L, 12L, 12L, 12L, 12L, 12L, 12L), CO2 = c(380L, 380L, 380L, 750L, 750L, 750L, 380L, 380L, 380L, 750L, 750L, 750L), Treat = structure(c(3L,

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

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

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

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 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 ...

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%" <-

Dear R community, I am trying to fit an nlme model where I want to estimate the fixed effects of two treatments on the parameters on the following equation Photo~(a*(1-exp(-c*PARi/a)))-b I was able to fit a simple model without covariates following the method described in Mixed-Effects Methods and Classes for S and S-PLUS, version 3.0, but when I add the covariates, I get the error "

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

Hi- I am trying to fit a log function to my data, with the ultimate goal of finding the second derivative of the function. However, I am stalled on the first step of fitting a curve. When I use the following code: FG2.model<-(nls((CO2~log(a*Time)+b), start=setNames(coef(lm(CO2 ~ log(Time), data=FG2)), c("a", "b")),data=FG2)) I get the following error: Error in

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

Good day: I am trying to use readcsvIts("nwr_data_qc.txt",informat=its.format("%Y%m%d%h%M %Y"),header=TRUE,sep="",skip=0,row.names=NULL,as.is=TRUE,dec=".") to read in a file (nwr_data_qc.txt) that looks like this: Time Y M D H Min CO2 2000.18790 2000. 3. 9. 18. 30. 373.60 2000.20156 2000. 3. 14. 18. 30. 373.34

How do I set up my model equation in aov to analyze a split-plot design? I have two factors (CO2 and NITROGEN), each with two levels (high and ambient). CO2 is my whole-plot factor with three replicates for each level (i.e., 6 rooms total). Is this syntax below correct? summary(aov(response ~ ROOM + CO2*NITROGEN + Error(ROOM/CO2)))