similar to: Making R packages (Unix)

Hi: I am posting this message for a colleague who has a lot of trouble to build an R package on Windows. He did not find a solution to his problems on the R-help archives and hopes that one of the R gurus will be able to help. He has a directory "CO2" which should contain all the required files and directories: DATA: DESCRIPTION MAN: R: README SRC: The following command is

Hi: I have started to use Lattice two days ago and find that it is an outstanding package. I am, however, stumbling on two problems, despite the fact that I have read the help pages as well as two pdf documents on Trellis that I found on the web. 1- I want to remove the ticks on the top and right sides of the panels and did not see any option in "scales" to do so. 2- I would like

Hi: I have made a lot of progress reading and manipulating large data files, thanks to the help of several of you. I am now stuck with writing the final file with the following error (see also the full transcript below): Error in as.matrix.data.frame(x) : length of dimnames[2] not equal to array extent I do not know what it means and could not find how to get around that in the manual nor

Hi: I am a very recent user of R 1.40 for MacOSX and went through the help files but did not not find the function that I am looking for. I have two comma separated files which I want to partly combine. File 1 has 49670 lines and 26 columns (separated by commas) and looks like this: ...

Hi: Thanks a lot to Deepayan Sarkar, author of lattice I think, who solved my first query. I am afraid that I have another one. I am plotting several mutipanels boxplots (with one conditioning variable) on one page. The x, y and conditioning variable are all continuous variables. The x and conditioning variables are transformed to shingles before being plotted. The plot looks nice but there

Hi: I am using the following command: xyplot(dat6$CO3*1e6 ~ dat6$irradiance, data=dat6, group=ref, xlab=list(label=expression(paste("Irradiance (", mu, "mol photons", m^"-2", " ", s^"-1", ")")), cex=1.3), ylab=list(label=expression(paste("Carbonate concentration (x ", 10^"6", " ", kg^"-1",

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

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

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

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-listers: I am trying to work with a big (262 Mb) file but apparently reach a memory limit using R on a MacOSX as well as on a unix machine. This is the script: > type=list(a=0,b=0,c=0) > tmp <- scan(file="coastal_gebco_sandS_blend.txt", what=type, sep="\t", quote="\"", dec=".", skip=1, na.strings="-99", nmax=13669628)

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

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