similar to: Color eps/ps output from specialized plots?

Hello everyone, I am ploting a groupeddata object with formula: formula(mydatausegroup) BF ~ HO | ID/Infar/Day Using this command: plot(na.omit(mydatausegroup), displayLevel=2,layout=c(10,2),aspect=2) This trellis plot does almost what I want and produces a 10x2 trellis plot, each panel is labeled as ID/Infar where infarct is either 1 or 0. And in each panel, it plots BF vs HO for each Day.

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! 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 ... $

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.

Dear forum members, What is the formula to calculate denominator degrees of freedom (den df) for nonlinear mixed-effect models with covariates? My model is similar to a CO2 uptake example from Pinheiro and Bates (2000, page 376). In this CO2 dataset, there are two treatments and two types (84 observations in total), but den df for each parameter of the model is 64. Isn’t it too high? Your

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

Dear R-experts, I have a problem to formulate non linear mixed effects when more than one explanatory variable is present. I'm using R 1.1.1 under Linux. The version number of the nlme library is 3.1-7. "help(nlme)" says that among several possibilities it is possible to code fixed effects according to fixed = a1 + a2 + ... + an ~ X1 + X2 + ... + Xm where the left hand side

Dear All, Using:- R : Copyright 2005, The R Foundation for Statistical Computing Version 2.1.1 (2005-06-20), ISBN 3-900051-07-0 and Jose Pinheiro, Douglas Bates, Saikat DebRoy and Deepayan Sarkar (2005). nlme: Linear and nonlinear mixed effects models. R package version 3.1-65. on a WINDOWS 2000 machine I am trying to run the scripts from the Mixed Models book and am running into some

Dear R-users, A while ago, Deepayan Sarkar suggested some code that uses the group argument in bwplot to create some 'side-by-side' boxplots (https://stat.ethz.ch/pipermail/r-help/2010-February/230065.html). The example he gave was relatively specific and I wanted to generalize his approach into a function. Unfortunately, I seem to have some issues passing the correct arguments to the

Hi, I just started a new course this semester on R, I never used it in my life and i'm stuck on these questions from 3 days, it would be really nice if someone could explain me the answers with the relative commands. thanks a lot in advance The following 7 questions are based on the CO2 dataset of R. 1) How many of the plants in CO2 are Mc2 for Plant? 2) How many are either Mc2 or Mn2?

An unwanted side effect of the new restrictions on abrreviated names. The anova.coxph command, in a slavish copy of anova.lm etc, returns a data frame with column labels of loglik Chisq Df Pr(>|Chi|) If one tries to extract the final column of the table errors result since it is not a standard R variable name. > afit <- anova(lm(conc ~ uptake, CO2)) > afit$P [1]

The following works fine with the x11 device, though it may well be that an initial plot is overwritten. With a pdf or postscript device, I get two plots, the first of which still has the red border from having the focus, while the second is the plot that I want. library(lattice); library(grid) plt <- xyplot(uptake ~ conc, groups=Plant, data=CO2) print(plt)

Hi, what is the recommended way of adding a strip to a lattice plot? In the example below I would like to add the value of mean(y) to a new strip.: --8<---------------cut here---------------start------------->8--- library(lattice) ## Small sample data set: p0 <- xyplot(uptake ~ Type | Treatment, data = CO2) p1 <- update(p0, panel = function(x, y, ...) {

can you recommend a good manual for R that starts with a data set and gives demonstrations on what can be done using R? I downloadedR Langauage definition and An introduction to R but haven't found them overly useful. I'd really like to be able to follow some tutorials using a dataset or many datasets. The datasets I have available on R are Data sets in package 'datasets':

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

Hi I am fairly new to Asterisk. I will have few questions on one Asterisk system: Description: I looked at Asterisk Mall says that "The TrueLine SMB PBX is perfect for the small office providing service for up to 28 Telco lines". This is a 1U Rack Mount Single T1 Asterisk Appliance. Question: Does it mean it has 7 PCI bus and takes 7x4=28 channels (if they used 7 TDM400P card

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

Hello! I write once more. I slightly modified your code Ross and now it returns all modes from the distribution. I changed the name of the funtion to distinguish the difference. I do not like the name simpleMode, however mode is already taken by another function (The (Storage) Mode of an Object). It would be nice to have some additional options for number of observations in modes, however

Hello! I write once more. I slightly modified your code Ross and now it returns all modes from the distribution. I changed the name of the funtion to distinguish the difference. I do not like the name simpleMode, however mode is already taken by another function (The (Storage) Mode of an Object). It would be nice to have some additional options for number of observations in modes, however