similar to: Split-plot model

I have no problem setting up my mixed model, or performing anova or lsmeans on my model?s outputs. However, performing lsd mean separation is giving me fits. So I do not have a problem when using two-way anova model. When using the code: fit.yield.add <- lm(data = ryzup, Yield ~ Rep + Nitrogen + Treatment) LSD.test(fit.yield.add, trt = "Nitrogen", alpha = 0.1, console = TRUE)

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

Ben, I would like to test the sulfur on the clover field, nitrogen on the clover field and then test for the presence of interaction. Sorry about the last email, seems it really screwed itself over, here it is again, hopefully nicer: Nitrogen(0) Nitrogen(20) Sulfur(0) 4.54 5.73 Sulfur(3) 4.64

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 ... $

Hello, I have tried to find this out some other way, but unsuccessful I have to try this list. I assume this should be quite simple. I have a dataset with 4 columns, "Sample_no", "Species", "Nitrogen", "Carbon" in csv format. In the species column I have many different species with varying number of obs per species Eg "Sample_no"

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

What test do I use to determine if there is a correlation between a discrete variable and a continuous variable? For example - I have water quality ratings for streams (excellent, good, fair, poor) and a corresponding nitrogen concentration for each rating. I want to know if the the ratings correlate with the concentration of nitrogen in the stream. Help?

G''day, all, So, I''ve decided to migrate my home server from Linux+swRAID+LVM to Solaris+ZFS, because it seems to hold much better promise for data integrity, which is my primary concern. However, naturally, I decided to do some benchmarks in the process, and I don''t understand why the results are what they are. I though I had a reasonable understanding of ZFS, but now

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

Hello, I am very new to R but would like to use the software to analyse the attached data. The experiment followed a split-split plot design There were two blocks and the whole plot is CO2 with two levels. The sub-plot is soil temperature with three levels and the sub-sub plot is soil moisture content with three levels (low, intermediate and high-similar for soil temperature). I had 7 plants per

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%" <-

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