search for: co2

...ows the contact duration and the fourth shows the pair of animals involved in the contact. In this case the top 6 contacts are all between animals CO1 and CO12. There are nearly 100,000 records. There were many animals that could have contacted each other: > animals animals 1 CO1 2 CO2 3 CO3 4 CO4 5 CO5 6 CO6 7 CO7 8 CO8 9 CO9 10 CO10 11 CO11 12 CO12 13 CO13 14 CO14 15 CO15 16 CO16 17 CO17 18 PO1 19 PO2 20 PO3 21 PO4 22 PO5 23 PO6 24 PO7 25 PO8 26 PO9 27 PO10 28 PO11 29 PO12 30...

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 not the same for each 'Plant'. I have replicated the problem using the CO2 data in nlme (based off of the Ch08.R script). This works (when 'conc' is the same for each 'Plant...

...cross 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 the F-test for CO2 yield a non-significant P-value, while the t-test in the summary.lme() is significant? Many thanks for your help! Best wishes Christoph ###################################################### mod11=lme(log(ind1+1) ~ CO2*DROUGHT*TEMP, random=~1|B/C,na.action=na.exclude) summary(mod11) Linear mix...

...mula(paste(chemical, "a~", cmd, sep = "")), singular.ok = TRUE) Coefficients: (Intercept) Odom Displace Weight -1.440e+00 2.936e-02 9.276e-01 -2.619e+00 HC1b HC2b CO1b CO2 8.974e-01 -1.223e-01 -3.695e-01 2.484e-01 NOX2 R00001 R00010 R00011 2.738e-01 -1.498e+00 3.087e-01 3.494e-01 R00100 R00101 R00110 R00111 -3.313e+00 -1.041e+0...

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 > plot(co2) I get a time seri...

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 considered continuous if I use aov(NO3 ~ CO2 + LEG + CO2:LEG) ? If not, what model can I us...

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 numericDeriv(form[[3L]], names(ind), env) : Missing value or an infinity produced when evaluating the model In addition: Warning messages: 1: In min(...

The formula attribute of the builtin CO2 dataset seems a bit strange: > formula(CO2) Plant ~ Type + Treatment + conc + uptake What is one supposed to do with that? Certainly its not suitable for input to lm and none of the examples in ?CO2 use the above.

...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 measuring meteorological conditions to account for any bias cause by changing weather conditions, the data is over dispersed so I have fitted a negative binomial glm (glm.nb) and simplified using stepAIC from the MA...

Hello: I am new on R, this is the first time I work with it, I am trying to run some R example to get learn of them, but, I have a problem when running the example below: http://stat.ethz.ch/R-manual/R-patched/library/datasets/html/zCO2.html CO2.Qn1 <- CO2[CO2$Plant == "Qn1", ] SSasympOff(CO2.Qn1$conc, 32, -4, 43) # response only Asym <- 32; lrc <- -4; c0 <- 43 SSasympOff(CO2.Qn1$conc, Asym, lrc, c0) # response and gradient getInitial(uptake ~ SSasympOff(conc, Asym, lrc, c0), data = CO2.Qn1) ## Initial valu...

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 with frequencies between 0 and .5 although it is based o...

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 2000.22609 2000. 3. 23. 18. 0. 373.01 and R returns this: Y M D H Min CO2 <NA> 2000 3 9 18 30 373.60 <NA> 2000 3 14 18 30 373.34 <NA> 2000 3 23 18 0 373.01 I...

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 ... NO * use '--force' to overwrite the existing 'INDEX' * removing junk files * building 'CO2_1.0.tar.gz' tar: U...

...nd 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 R (or at least non-comparable "methodologies")? Please, feel free to comment/suggest. Regards, Eduardo Esteves # Dados CO2<-c(141,172,181,227,309,414,641,936) Prop<-c(0.25,0.34,0.34,0.68,0.85,0.99,0.98,0.99) # Diagrama dispersão plot(Prop~CO2, las=1, xlim=c(0,1000),ylim=c(0,1),pch=16,cex=1.5, xlab="CO2 (ppm)", ylab="Proporção de respostas correctas") # Estimaçao (Método Mínimos Quadrados)...

...exists. Thanks, Tim. --- "%nested.in%" <- function(x,f,...){ #coerce to list if(!is.list(x))x<-list(x) if(!is.list(f))f<-list(f) #collapse to vector x <- tapply(x[[1]],x) f <- tapply(f[[1]],f) #analyse return(all(sapply(lapply(split(f,x),unique),length)==1)) } CO2$Plant %nested.in% CO2[,c("Type","Treatment")] #TRUE CO2$Plant %nested.in% (CO2$uptake < mean(CO2$uptake)) #FALSE

Dear list: I'm using bwplot to compare concentrations by location and treatment as in: # using built in data bwplot( conc ~ Type : Treatment, data = CO2 ) I would like the order of the plots to be: 3,4,1,2. I can't seem to figure this out with index.cond or permc.cond. Any help is appreciated! Brad Eck [[alternative HTML version deleted]]

...n'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 emitted by stem volume biomass (CO2v) and stem area (CO2s). (tree respiration) The loop that I have is calculating the CO2 fluxes for each row. *Script :* for (i in 1:nrow(resp)) { k=resp[i,5] CO2umol[i]<-resp[i,6]*((Press*infoch[k,11]...

Dear all I'm not sure if I did the right analysis for my specific split splot design. We are studying biomass increase with different CO2 concentrations with four different functional plant groups (e.g. grasses, herbs, broad-leafed trees and conifers). Of each functional plant group we have four species. The design is orthogonal. The design is: Blocks: 2 (climate chambers, called Gruppe) Plot: 3 (3 CO2 concentrations) whole p...

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 significant lme-result and if I do a Tukey afterwards (I know that one...

...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 S-Plus by Pinheiro and Bates"; library(nlme) plot(CO2) postscript("tmp.eps") plot(CO2) dev.off() The first plot has few colors on the screen, but postscript output is monochrome. I tried a more complex one too: postscript("test1.eps",horizontal = FALSE, onefile = FALSE, paper = "special"). BTW, loading NLME chances t...