similar to: Help to find correlation.

Hi Folks, How do I get red bold font on my y axis and black standard font on my x axis? plot(runif(10), ylab="Red, Bold?", xlab="Black, standard?") Any pointers or examples would be great. Thanks! Hank Dr. Hank Stevens, Assistant Professor 338 Pearson Hall Botany Department Miami University Oxford, OH 45056 Office: (513) 529-4206 Lab: (513) 529-4262 FAX: (513) 529-4243

Hi All, I am running R 2.2.0 on Mac OS 10.4.2, dual G5 processors with 8 Gig RAM. I am running a simulation with lsoda that requires ~378 s to complete one set of time intervals. I need to optimize the parameters, and so need to considerably speed up the simulation. I have tried to figure out how to change the appropriate memory allocation and have search R help and Introductory

All, In the function below I have 24 individuals and 6 calculations per individual. The 6 calculations are collected each time in a 1:24 loop when calculating "delta". I'd like to collect all 144 = 24*6 calculations in one vector ("delta.patient.comb"). The function works as is via indexing, but is there an easier way to collect the measurements via appendinng the 6

I have data on number of vines per tree for ~550 trees. Over half of the trees did not have any vines and the data is fairly skewed (median = 0, mean = 1.158, 3rd qu. = 1.000). I am attempting to investigate whether plot location (four sites), species (I'm using only the four most common species), or tree dbh has a significant influence on the number of vines per tree. When I

I was trying to help someone who used a spanish keyboard on a PC running Windows. he discovered that he had no tilde key. Does anyone know of any simple work-arounds? Thanks, Hank Dr. Hank Stevens, Associate Professor 338 Pearson Hall Botany Department Miami University Oxford, OH 45056 Office: (513) 529-4206 Lab: (513) 529-4262 FAX: (513) 529-4243 http://www.cas.muohio.edu/~stevenmh/

Hi Folks, I was wondering what everyone thought about adding a sentence to each package update announcement that described what the package did. R extensions are so numerous that it is difficult to keep up with them. Would it be appropriate to ask package developers to add a brief sentence about what the package does, when they announce updates? I would benefit from such descriptions.

Dear All, I am working with linear mixed-effects models using the lme4 package in R. I created a model with the lmer function including some main effects, a two-way interaction and a random effect. Now I am searching how I could incorporate an interaction between the random effect and one of the fixed effects. I tried to express the interaction in:

Hi Folks, I think I have searched exhaustively, including, of course R-help (D. Bates, S. Graves, and others) and but I remain uncertain about testing fixed effects with lmer(..., family=binomial). I gather that mcmcsamp does not work with Do we rely exclusively on z values of model parameters, or could we use anova() with likelihood ratios, AIC and BIC, with (or without)

Hello all: I frequently have glm models in which the residual variance is much lower than the residual degrees of freedom (e.g. Res.Dev=30.5, Res.DF = 82). Is it appropriate for me to use a quasipoisson error distribution and test it with an F distribution? It seems to me that I could stand to gain a much-reduced standard error if I let the procedure estimate my dispersion factor (which

Hi folks, Warning: I don't know if the result I am getting makes sense, so this may be a statistics question. The fitted values from my binomial lmer mixed model seem to consistently overestimate the cell means, and I don't know why. I assume I am doing something stupid. Below I include code, and a binary image of the data is available at this link:

Hi Everyone. I am rather new to R and I've been trying to implement a function to carry out the above test. For a couple of days now I've been stuck on how to generate average rank differences. Say I have a vector of average ranks: averank<- c(2,5,9,12) I would like to subtract averank[1] from averank[2], averank[1] and averank[2] from averank[3] and averank[1], averank[2] and

Hi, the question on subject is possible? example: m1 <- lmer(y~1+(1|subject)) m2 <- update(m1,.~.+x) I try but dont work, exist any other method for this? Thanks Ronaldo -- He won a Toyota now, eh? -- pal?ndromo -- |> // | \\ [***********************************] | ( ? ? ) [Prof. Ronaldo Reis J?nior ] |> V [UNIMONTES/Depto. Biologia

Dear R-friends, is Rwiki down? I've been trying to login for the past couple of days with no success. Yours sincerely, Jin ____________________________________________________________________________________ wherever you're surfing.

Hello, I'm building a couple of mixed models using the lmer function. The actual modelling is going well, but doing some reading on the use of crossed random effects and the comparison of models with and without random effects it is clear that I need to generate some Markov Chain Monte Carlo samples. However, I'm struggling because everyone time I go to generate a sample I get the

Hi folks, I am tearing my hair out on this one. I am using an example from Pinheiro and Bates. ### this works data(Orange) mod.lis <- nlsList(circumference ~ SSlogis(age, Asymp, xmid, scal), data=Orange ) ### This works mod <- nlme(circumference ~ SSlogis(age, Asymp, xmid, scal), data=Orange, fixed = Asymp + xmid + scal ~ 1, start =

Howdy I apologize for duplicated posting. But I decided to correct my previous posting. I had the regression results using r <- lm(Y ~ nemp + as.factor(devt), data=d). First, there is the result of anova(r). Here I could not find regression coefficients. Response: Y Df Sum Sq Mean Sq F value Pr(>F) nemp 1 58.2 58.2 1233.23 < 2e-16 ***

Hi all, I try to build a model using lmer, with 2 crossed fixed factors (poptype and matingtype) and 2 random factors (pop and family) which I want to nest within poptype. Which of these formulae should I use ? 1. lmer(y~poptype*matingtype + (1|poptype/pop/fam)) 2. lmer(y~poptype*matingtype + (poptype|/pop/fam)) thanks in advance Agnes -- Marie-Agn?s Coutellec UMR INRA-Agrocampus Ouest 985

Respected Sir, I am working on something in R. I am getting the following error. Warning messages:1: the standard deviation is zero in: cor(x, y, na.method, method == "kendall") I like to handle this error. I like to know how to do error handling in R. I want to write the WARNING MESSAGES to a text file and exit R. Please help me with a solution. My mailid is

I'm still relatively new to R, so my apologies if this is covered somewhere. I've been running some mixed-effect models in R using lme{nlme}, but read in Faraway's recent book, Extending the Linear Model with R, that lmer in package lme4 is a much improved version. I tried using this approach, but the output for the fixed effects doesn't report a p-value or the degrees of freedom

Dear R-help, I am trying to create groupedData objects using the nlme library. I'm missing something basic, I know: Here is the first example in ch.1 of Pinheiro & Bates (2000): library(nlme) x2=Rail$travel;x1=Rail$Rail;eg1=data.frame(x1,x2);eg1gd=Rail print(eg1gd) x11();print(plot(eg1gd)) femodel=lm(x2~x1-1,data=eg1gd) print(femodel$coefficients) Result: x12 x15 x11