similar to: How do you report lmer results?

Dear R gurus I am running a GLMM that looks at whether chimpanzees spend time in shade more than sun (response variable 'y': used cbind() on counts in the sun and shade) based on the time of day (Time) and the availability of shade (Tertile). I've included some random factors too which are the chimpanzee in question (Individual) and where they are in a given area (Zone). There are

Dear all, I have the following dataset with results from an experiment with individual bats that performed two tasks related to prey capture under different conditions: X variables: indiv - 5 individual bats used in the experiment; all of which performed both tasks task - 2 tasks that each individual bat had to perform dist - 5 repeated measures of individual bats at 5 different distances from

Dear all, I'm having a few problems trying to reshape a data frame. I tried with reshape{stats} and melt{reshape} but I was missing something. Any help is very welcome. Please find details below: ################################# # data in its original shape: indiv <- rep(c("A","B"),c(10,10)) level.1 <- rpois(20, lambda=3) covar.1 <- rlnorm(20, 3, 1) level.2

Dear r-helpers, why do I get an output in the first iteration of the for-loop which contains the string values of the input vector, and how can I avoid that? Here's the output (only line 1 is wrong) latentVariable Indiv Group 1 rPlanning rIterat rTDD 2 rPlanning 0.79 0.84 3 rIterat 0.79 0.83 4 rTDD 0.9 0.96 5 rStandup 0.83 0.82 6

Hello world, I'm trying to do an anova on data in data.set, dependent variable is a column named "dep.var", grouping variable is in a column called "indep.var", and is.factor(indep.var) is TRUE... why can't I just do aov(dep.var ~ indep.var, data = data.set)? What have I done to deserve this?! What gives? Am I missing something totlly obvious? R-base-1.0.0-1,

Dear all, I have no problem with this individual grep command: > datk <- grep("XM_528056", source$V1) > dat2 <- source[datk,] > print(dat2) V1 V2 V3 V4 V5 V6 V7 35995 XM_528056 panTro2 chr8 + 1775569 1896107 Chimpanzee BUT, when I run them under the loop it gives this error: > hm_acc <-

I am trying to run a GLMM on some binomial data. My fixed factors include 2 dichotomous variables, day, and distance. When I run the model: modelA<-glmmPQL(Leaving~Trial*Day*Dist,random=~1|Indiv,family="binomial") I get the error: iteration 1 Error in MEEM(object, conLin, control$niterEM) : Singularity in backsolve at level 0, block 1 >From looking at previous help

Dear all, i'm trying to label specific individuals (supplementary ones) after a PCA with the FactoMiner package. There is not much details (possibilities?) in the R-help of the plot.pca function. There is indeed a "label" parameter but i could only manage to label the supplementary individuals with there "row.names" (i.e. label="indiv.sup") and not with the

Dear all, I would like to draw a 3-D horizontal cylinder preferably in RGL device (because this gives the look from different angles). Basic idea is from http://www.tau.ac.il/cc/pages/docs/sas8/insight/chap18/sect3.htm. Below is the description exactly what I want to do. Please see at figure 18.6, 1st plot. Here it is an confidence ellipsoid. Suppose now you put another 4 same ellipsoids on top

I do the these passages: library(Geneland) set.seed(1) data <- simdata(nindiv=200, coord.lim=c(0,1,0,1) , number.nuclei=5 , allele.numbers=rep(10,20), IBD=FALSE, npop=2, give.tess.grid=FALSE) geno <- data$genotypes coord <- t(data$coord.indiv) path.mcmc <-

I followed Bill Venables's suggestion and tried to make a multiplot figure with fig (using R .62). > x<-rnorm(100) > y<-rnorm(100) > x11() > par(fig=c(0,2/3,0,1)) > plot(x,y) > par(fig=c(2/3,1,0,1)) > qqnorm(x) > postscript(file="twoplot.ps") > par(fig=c(0,2/3,0,1)) > plot(x,y) > par(fig=c(2/3,1,0,1)) > qqnorm(x) However

Hi folks, I am having trouble to plot a mixed model analysis of covariance (ANCOVA). To do so I use the function predict in nlme but the line that is being drawn is totally out of control!!! here is my script (where MASS_S is dry mass and MASS_F is fresh mass): MEN<-read.table("Mentha_lme2.txt", h=T) attach(MEN) lme1<-lme(log(MASS_S)~log(MASS_F)*TREAT, random=~1|INDIV)

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Hi, I'm quite new to R and currently trying to use lm to fit linear models but I am currently stuck my code is as follows: Model1 = function(meth_matrix,exposure, X1, X2, X3, batch) { mod = lm(meth_matrix[, methcol]~exposure+X1+X2+X3+batch) res = summary(mod)$coef[2,] system.time(indiv.results <- mclapply(setNames(ncol=473864(cord_betas)), dimnames(cord_betas)[[2]]), Model1,

Dear R helpers   I have written some function (the actual code I have pasted at the end of mail) like say   indiv_rate = function(n, rate_name, rate, rate_rf1, rate_rf2, rate_rf3, rateprob1, rateprob2, rateprob3) { ........some R commands   return(data.frame(rate_name, rates = round(rate_data, digits = 4)))   }   ## INPUT   rates = indiv_rate(n = read.csv('number.csv')$n, rate_name =

Hello, This is in response to a post from a couple of years back regarding Kaiser-Meyer-Olkin Measures of Sampling Adequacy. (http://tolstoy.newcastle.edu.au/R/help/05/12/17233.html) As it turns out, last year Trujillo-Ortiz et al. at the Universidad Autonoma de Baja California wrote and posted a script for MATLAB that does the job. You can see it (with a discussion of KMO statistics) at

Hola Isidro: También había sopesado esa posibilidad pero por una parte me parecía "complicar" el proceso y por otra tengo mis dudas acerca de que en que el análisis cluster pueda prescindir de la hipótesis de Normalidad en las dos variables "raras" que tengo. De cualquier forma muchas gracias por tu idea. Un saludo {In Archive} RE: [R-es] Clasificacion de

> > Message: 5 > Date: Sat, 28 Apr 2007 11:37:51 -0700 > From: "J.H." <warthog19 at eaglescrag.net> > Subject: Re: [syslinux] USB stick meets ISO system > To: Geert Stappers <stappers at stappers.nl> > Cc: syslinux at zytor.com > Message-ID: <1177785471.25140.29.camel at localhost.localdomain> > Content-Type: text/plain > > On Sat,

Estimados apañeRos: La duda o propuesta que os voy a plantear es a la vez metodológica y relacionada con R. Me encuentro trabajando con tres variables que son el resultado de un computo de porcentajes. Me explico, se toma una muestra de n casos (unos 6.500 aprox) pertenecientes a i individuos (unos 230 aprox) en la que se comprueba si un determinado evento ha ocurrido o no, anotándose 1 en

Hi, I have installed Dovecot 1.0.0 on a FreeBSD6.0 machine with Exim 4.66 and Vexim 1.5. When I restart the machine, dovecot does not load properly. The logs of /var/log/maillog are as: May 8 00:10:32 lhr dovecot: pop3-login: No authentication sockets found May 8 00:10:32 lhr dovecot: child 11478 (login) returned error 89 May 8 00:10:34 lhr dovecot: imap-login: No authentication sockets