search for: nestlings

Dear all, I want to get a series of boxplots (grouped by two factors) and I want to overlay the original observations and the following code does almost what I want: library(ggplot) ddf <- data.frame(x=factor(rep(LETTERS[1:4], each=30)), y = runif(120,0,10), grp = factor(rep(rep(1:3, 10), 4))) ggplot(ddf, aes(x, y, colour=grp)) + geom_boxplot() + geom_point() Yet the position of the points

...geable" error structure. I am examining how nestling weight varies as a function of distance from a plot boundary. The response variable (WEIGHT) and the explanatory variable (DISTANCE) are both continuous. The clustering factor (MOTHER) is entered into my model to account for similarity of nestlings from the same nest produced by a given mother. My simplified code is as follows: summary(model1<-geeglm(WEIGHT~DISTANCE, id=MOTHER,data=df,corstr="exchangeable"))) #I've included part of the model output here <Coefficients: < Estimate Std.err Wald...

Dear Experts, May be the problem is still solved, however I tried to find the answer in the archives: I use: > R.version _ platform i386-pc-mingw32 arch i386 os mingw32 system i386, mingw32 status major 1 minor 7.1 year 2003 month 06 day 16

Hi, I'm using: platform i386-pc-mingw32 arch i386 os mingw32 system i386, mingw32 status major 1 minor 5.1 year 2002 month 06 day 17 language R and I would like to apply: > update.packages() trying URL

Dear all, I'm working with Cox-regression, because data could be censored. But in this particular case not. Now I have a simple example: PRO and PRE are (0,1) coded. The response is not normal distributed. We are interested in a model which could describe interaction. But my results are depending strongly in the choose of the contrast option. It is clear that there is some dependence in

Dear all, I produced the following graph with ggplot which is almost fine, yet I don't like that the legend for "Means" and "Observations" includes a line, though no line is used in the plot for those two (the line for "Overall Mean" on the other hand is wanted): library(ggplot2) ddf <- data.frame(x = factor(rep(LETTERS[1:2], 5)), y = rnorm(10)) p <-

I have a few questions relating to overdispersion in a sex ratio data set that I am working with (note that I already have an analysis with GLMMs for fixed effects, this is just to estimate dispersion). The response variable is binomial because nestlings can only be male or female. I have samples of 1-5 nestlings from each nest (individuals within a nest are not independent, so the response variable is the ratio of sons to daughters) and some females have multiple nests in the data set (so I need to include female identity as a random effect). He...

Dear all, I want to use aggregate.formula to conveniently summarize a data.frame. I have quiet some variables in the data.frame and thus I don't want to write all these names by hand, but instead create them on the fly. This approach has the advantage that if there will be even more columns in the data.frame I don't have to change the code. I've hence tried to construct a formula

Hi everybody, I'd like to have a list with two elements, where both elements have the same value: z <- list(a=1, b=1) If it happens, that I've to change the value, I've to assure that I change both. This is error prone. Hence, a better way to achieve this is to define: tmp <- 1 z <- list(a=tmp, b=tmp) Now, I'm wondering if it is possible to make this more compact: z

Dear All, Comparing linear mixed effect models in SAS and R, I found the following discrepancy: SAS R random statement random subj(program); random = ~ 1 | Subj -2*loglik 1420.8 1439.363 random effects variance(Intercept) 9.6033 9.604662

Hi All, What is the R function for computing multinomial distribution, e.g. f(2,1,3; 2/9, 1/6, 11/18, 6)? That is, a total of 6 trials, event 1's p1=2/9, x1=2, event 2's p2=1/6, x2=1, and event 3's p3=11/18, x3=3. thanks, Johnny [[alternative HTML version deleted]]

Dear all, Assume that I have the following data structure: d <- expand.grid(subj=1:5, time=1:3, treatment=LETTERS[1:3]) d$value <- 10 ^ (as.numeric(d$treatment) + 1) + 10 * d$subj + d$time d$value2 <- 100000 + d$value where d$treatment == "C" stands for my control group. What I want to achieve now is to subtract the values corresponding to d$treatment == "C" from

Hi, If I set the p.adjust="none", does it meant that the output p values from the pairwise.t.test will be the same as those from individual t.tests (set var.equal=T, alternative="t")? I actually got different p values from the two tests. See below. Is it supposed to be this way? Thanks Johnny > x [1] 61.6 52.7 61.3 65.2 62.8 63.7 64.8 58.7 44.9 57.0 64.3 55.1 50.0 41.0

Dear all, I have a question about linear models. Is there something comparable to the estimate statement in SAS? E.g. proc mixed data=test; class x1 x2; model y=x1|x2 /s; estimate 'x1' x1 -1 1 x1*x2 -0.5 -0.5 0.5 0.5; estimate 'x2' x2 -1 1 x1*x2 -0.5 0.5 -0.5 0.5; run; Thanks for the help, Dominik Dominik Grathwohl Biostatistician Nestl? Research

Hi, I want to free memory used by R. The usage of rm and gc give no result. I'm running an algorithm consuming a huge memory and I need to recover the memory used by R between 2 call of my algorithm. Thank you in advance for your help. e-mail: sofiane.lariani at rdls.nestle.com Sofiane Lariani

I actually have several questions revolving around the generation of wireframe plots. The most pressing is that which is described by the subject line. I am trying to produce a figure with x, y, and z labels, but only tick marks on the x, and y axes. I have supplied sample code below substituting the volcano data set for my own such that you may run the code and see the results.

Hi, I have to analyse the number of provisioning trips to nestlings according to a number of biological and environmental factors. I was thinking of building a mixed-effects model with species and nestid as random effects, using a Poisson distribution, but the data are overdispersed (variance/mean = 5). I then thought of using a mixed-effects model with negative bi...

Dear all, I am trying to fit a model with crossed random effects using lme. In this experiment, I have been measuring oxygen consumption (mlmin) in bird nestlings, originating from three different treatments (treat), in a respirometer with 7 different channels (ch). I have also measured body mass (mass) for these birds. id nest treat year mlmin mass ch hack 1EP51711 17 H 2008 1.401719138 10.7 4 2008:17 1EP51709 17 H 2008 1.257163112...

Hello, ? I believe that in R, it is not possible to analyze mixed effect-models when the distribucion is not gaussian (p.e. binomial or poisson), isn't? ? Somebody can suggest me alternative? ? thanks ? xavi ? -.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.- r-help mailing list -- Read http://www.ci.tuwien.ac.at/~hornik/R/R-FAQ.html Send "info",

Summary: proc mixed vs. lme The objective of this summary is to help people to get more familiar with the specification of random effects with proc mixed or lme. Very useful are the examples of Ramon Littell's book: "SAS System for Mixed Models (1996)" (http://ftp.sas.com/samples/A55235) The same data set's are kindly made available by Douglas Bates in the