similar to: coxph() - unexpected result using Crawley's seedlings data (The R Book)

Hei, I have a one simple question which does not seem to be that simple as I cannot find any solution/answer: Is it possible to compare multiple survival curves in R with survdiff-function when there is interaction term involved in predictor variables (and this interaction is significant)? Example: survdiff(Surv(death,status)~treatment*gapsize) R is making "problems" with it ie.e.

Dear all: I have the following codes: Xdata<-c(2,3,8,9,10) Ydata<-1:5 gap.plot(Xdata, Ydata,gap=c(5,6),gap.axis="x",type="o") However, the type='o' seems only work on the first part of gap plot, the second half of the plot always just points, you can not add lines on that part, any help will be highly appreciated. I would like to have these two parts of

Dear R- list, I have some data to present with histograms. Therefore I used hist(...). I have few values with almost 80% of the frequencies (totaly 800) and some other values with low frequencies ( totaly 5 -10 ) that I want to emphasize. Therefore I want to "cut" the y-axis on 100, but I don't know how to deal with this. Thanks in advance, Michael Graber

I have a feeling that this is an embarassingly simple fix, but I've been at it for most of the morning and can't get things figured out. I'm trying to work through some examples in Crawley's "The R Book". I have installed packages and libraries as described in the book, but when I try, for example: data<-read.table("c:\\temp\\daphnia.txt", header=T)

Hi, I have got to my hands an excellent book by Michael J. Crawley ``Statistical Computing: An Introduction to Data Analysis using S-Plus'' (John Wiley & Sons, Ltd, ISBN 0-471-56040-5). Its beauty for me is in the fact, that it is more of ``An Introduction to Data Analysis'' than ``using S-Plus'', but I guess that it may be of interest for many others. Most of the

Dear R gurus, I have XY data giving the locations of tree seedlings that were surveyed during a 210 meter belt transect. This belt transect was taken by stretching a line across the field, then measuring all seedlings within 1 meter on either side of the line. The end result was XY coordinates and height for ~1,300 seedlings. I would like to use that data to calculate density of

Full_Name: Charles J. Geyer Version: 1.8.0 OS: i686-pc-linux-gnu (Suse 8.2) Submission from: (NULL) (134.84.86.22) Two bugs (perhaps related, perhaps independent) revealed by the same Poisson regression with offset mydata <- read.table(url("http://www.stat.umn.edu/geyer/5931/mle/seeds.txt")) out.fubar <- glm(seedlings ~ burn01 + vegtype * burn02 + offset(log(totalseeds)),

Dear All, I am analysing a dataset on levels of herbivory in seedlings in an experimental setup in a rainforest. I have seven classes/categories of seedling damage/herbivory that I want to analyse, modelling each separately. There are twenty maternal trees, with eight groups of seedlings around each. Each tree has a TreeID, which I use as the random effect (blocking factor). There are two

Hello, After putting together interaction code that worked for a single pair of interactions, when I try to evaluate two pairs of interactions( flowers*gopher, flowers*rockiness) my computer runs out of memory, and the larger desktop I use just doesn't go anywhere after about 20 minutes. Is it really that big a calculation? to start: mle2(minuslogl = Lily_sum$seedlings ~ dnbinom(mu = a,

Hi everybody, I am trying to fit a model with the lmer function for mixed effects. I have an experimental design consisting of 5 field plots. Each plot is divided in 12 subplots where the influence of three factors on the growing of tree seedlings is tested: (1) seed (1 = presence; 0 = absence); (2) seedling species (oak holm vs. pine); (3) treatment (three different treatments). In each of

Dear R users, I have been having problems getting believable estimates from anova on a model fit from lmer. I get the impression that F is being greatly underestimated, as can be seen by running the example I have given below. First an explanation of what I'm trying to do. I am trying to fit a glmm with binomial errors to some data. The experiment involves 10 shadehouses, divided between

Hi, I'm new to R and GLMMs, and I've been unable to find the answers to my questions by trawling through the R help archives. I'm hoping someone here can help me. I'm running an analysis on Seedling survival (count data=Poisson distribution) on restoration sites, and my main interest is in determining whether the Nutrients (N) and water absorbing polymer Gel (G) additions to the

Hi there, I've looked through the very helpful advice about adding fitted lines to plots in the r-help archive, and can't find a post where someone has offered a solution for my specific problem. I need to plot logistic regression fits from three differently-sized data subsets on a plot of the entire dataset. A description and code are below: I have an unbalanced dataset

Hello, all R-users! I am working on fitting a survival analysis model using the coxph function for Cox proportional hazards regression model. Data look like usual: ========================== group block death censor Group1 1 4 1 Group1 1 12 1 ... Group2 30 4 1 Group2 30 4 1 ... Group3 57 16

Hi, I'm new to R and GLMMs, and I've been unable to find the answers to my questions by trawling through the R help archives. I'm hoping someone here can help me. I'm running an analysis on Seedling survival (count data=Poisson distribution) on restoration sites, and my main interest is in determining whether the Nutrients (N) and water absorbing polymer Gel (G) additions to the

Hi All, I am trying to do a survivorship analysis with library(survival)from a data set that looks like this: I followed a bunch of naturally germinated seedlings of an annual plant from germination to death (none made it to reproduce, and died in a period of ~60 days after germination.) I also know the size of the seed of every individual censused. So I am trying to analyze seedling survival as

Hi all, I am trying to run a glm with mixed effects. My response variable is number of seedlings emerging; my fixed effects are the tree species and distance from the tree (in two classes - near and far).; my random effect is the individual tree itself (here called Plot). The command I've used is: mod <- glmer(number ~ Species + distance + offset(area) + (1|Plot), family = poisson)

Hello, I am trying to use the projection.matrix( ) function and am following the example given. I have my data formatted very similar to the test.census example. > str(AsMi05mat) `data.frame': 1854 obs. of 6 variables: $ Tag : num 501 502 503 504 505 506 507 508 509 510 ... $ Year : int 1995 1995 1995 1995 1995 1995 1995 1995 1995 1995 ... $ Length : num 34

Dear R-help members I would be grateful if anyone could help me with the following problem: 1: I changed the font style of the plant names (along x-axis, under the bars) to "italic" . How can I avoid that the font in the legend also changes to "italic"? 2. I would like to place the plant names (along x-axis, under the bars) in a way that : a) the first plant name is under the

hello, i try to plot 3 graphs which have the same x.axis underneath each other. i'd like the plots to be aligned without margings between the boxes and draw a single x axis beneath the lowest plot. i managed to get the alignment by setting par(mar), but the middle box is stretched and i cant't figure out how to get around this. par(pin) was my guess, but this doesn't do the job