similar to: simulate() and glm fits

Hi. I'm using lmer() from the lme4 package (version 0.8-3) and I can't get anova() to group variables properly. I'm fitting the mixed model Response ~ Weight + Experimenter + (1|SUBJECT.NAME) + (1|Date.StudyDay) where Weight is numeric and Experimenter is a factor, ie, > str(data.df) `data.frame': 4266 obs. of 5 variables: $ SUBJECT.NAME : Factor w/ 2133 levels

Dear All, I want to test whether a method exists for given object. For example, whether a function "deviance" is defined for an object of the "lm" class. My imperfect understanding leads me to think something like hasMethod("deviance", object) hasMethod("deviance", "lm") existsMethod("deviance", signature(class="lm"))

Dear All, Is there is a fast way of refitting lm() when the design matrix stays constant but the response is different? For example, y1 ~ X y2 ~ X y3 ~ X ...etc. where y1 is the 1st instance of the response vector. Calling lm() every time seems rather wasteful since the QR-decomposition of X needs to be calculated only once. It would be nice if qr() was called only once and then the same

Good Day, I fit a multivariate linear regression model with 3 dependent variables and several predictors using the lm function. I would like to use stepwise variable selection to produce a set of candidate models. However, when I pass the fitted lm object to step() I get the following error: Error from R: Error in drop1.mlm(fit, scope$drop, scale = scale, trace = trace, k = k, : no

Hi all! I am working with GLMM using the binomial family I use the following codes I dropped no significant terms, refitting the model and comparing the changes with likelihood: G.1<-lmer(data$Ymat~stu+spi+stu*sp1+(1|ber),data=data,family="binomial") G.1b<-lmer(data$Ymat~stu+spi+(1|ber),data=data,family="binomial") anova (G.1,G.2) But, when I want to evaluate the

Dear All, I wish to model random effects that have known between-group covariance structure using the lme() function from library nlme. However, I have yet to get even a simple example to work. No doubt this is because I am confusing my syntax, but I would appreciate any guidance as to how. I have studied Pinheiro & Bates carefully (though it's always possible I've missed

Respected Sir/Madam, I have a question regarding calculation of LD50 (Lethal Dose) and IC50 (50% inhibitory concentration) of an antimicrobial experiment. I have used a compound isolated from a plant and observed its effect on the fungus *Fusarium oxysporum* by the food poisoning method. Solutions of the compound at concentrations of 0, 50, 100, 150, 200 and 250µg/ ml were added to

Hi, I am reading the source code of rpart. I have problems understand the following code and would appreciate for any helps. In rpart.s, there is a line: rpfit &lt;- .C(C_s_to_rp, &nbsp;&nbsp;&nbsp; &nbsp;&nbsp;&nbsp; &nbsp;&nbsp;&nbsp; n = as.integer(nobs), &nbsp;&nbsp;&nbsp; &nbsp;&nbsp;&nbsp; &nbsp;&nbsp;&nbsp; nvarx

Dear R help, I've encountered a problem with survreg's anova(). I am currently writing general code to fit a variety of models using different fitting functions. Here's a simple example of what I'm trying to do: ---begin code--- # general function to analyse data analyse.data <- function(formula, FUN, data, ...) { fit <- FUN(formula, data=data, ...) anova(fit)

Hi there, I don't have much experience with fitting at all and I'd like to get some advice how to use the "weights"-argument with nls correctly. I have created some data with a sigmoidal curve shape. Each y-Value was generated by the mean of three values. A standard deviation was calculated too. Now, I'd like to weight the data points respective to its standard

Christoph -- There was just a thread on this earlier this week. You can search in the archives for the title: "refitting lm() with same x, different y". (Actually, it doesn't turn up in the R site search yet, at least for me. But if you just go to the archive of recent messages, available through CRAN, you can search on refitting and find it. The original post was from William

This concerns the contributed package "concord". Sorry to bother you with it, but my attempt to contact the author/maintainer failed (see below). Perhaps you can forward it, or let me know where to send it. Regards, Rob Kushler ------------------------------------------------------ This is the Postfix program at host tak.itd.uts.edu.au. I'm sorry to have to inform you that

Hallo, I didn´t found any facilities for Halbergs cosinor analysis in R. This analysis is well known in the Chronobiology as the least square approximation of time series using cosine function of known period (in my case of 24hours-period). I tried to write a script but crashed... Can you give me some advices, please!? Thanks Anne Berger Institute of Zoo- and Wildlife Research, Berlin, Germany

I have 4000 observations that I need to randomly relabel 1000 times and then calculate the mean of the 1000 values at each of the 4000 points. Any ideas for where to begin? Thanks Kevin -- View this message in context: http://r.789695.n4.nabble.com/Random-Relabelling-tp3463100p3463100.html Sent from the R help mailing list archive at Nabble.com.

Dear all, I am trying to transform polynomial coefficients from orthogonal form to the standard power basis. There's poly.transform in S-plus. Does anybody know how to do that in R ? I've found question about that in the archives of R-help but no real answer. Example : I'm doing polynomial regression of percentage of one insect in a community on altitude, precipitations,

Hi, I'm not sure how use curve(dexGAUS(… None of the following four works: rt<- rexGAUS(100, mu=300, nu=100, sigma=35) m1<-gamlss(rt~1, family=exGAUS) curve(dexGAUS(rt=x, mu=300 ,sigma=35,nu=100), 100, 600, main = "The ex- GAUS density mu=300 ,sigma=35,nu=100") curve(dexGAUS(x=rt, mu=300 ,sigma=35,nu=100), 100, 600, main = "The ex- GAUS density mu=300

Dear all, I have the following dataset: each row corresponds to count of forest floor small mammal captured in a plot and vegetation characteristics measured at that plot > sotr plot cnt herbc herbht 1 1A1 0 37.08 53.54 2 1A3 1 36.27 26.67 3 1A5 0 32.50 30.62 4 1A7 0 56.54 45.63 5 1B2 0 41.66 38.13 6 1B4 0 32.08 37.79 7 1B6 0 33.71 30.62

Dear R users, I'm estimating a mixed effects model in which the spatial correlation is controlled for by the "corGaus" structure. I'm wondering if there is a document or paper that explains how the spatial correlation structure (such as "corExp" or "corGaus") works. Let me use the example and data posted on UCLA's R FAQ webpage to explain my problems.

Is there anyway to change any y[i] value (i=2,...6) to make following NLS workable? x <- c(0,5,10,15,20,25,30) y <- c(1.00000,0.82000,0.68000,0.64000,0.66667,0.68667,0.64000) lm(1/y ~~ x) nls(1/y ~~ a+b*x^c, start=list(a=1.16122,b=0.01565,c=1), trace=TRUE) #0.0920573 : 1.16122 0.01565 1.00000 #Error in numericDeriv(form[[3]], names(ind), env) : # Missing value or

https://bugs.freedesktop.org/show_bug.cgi?id=70511 Priority: medium Bug ID: 70511 Assignee: nouveau at lists.freedesktop.org Summary: nouveau_bo_name_get segmentation fault while running root tutorials/gl/glbox.C QA Contact: xorg-team at lists.x.org Severity: normal Classification: Unclassified