similar to: Multiple comparisons for coxph survival analysis model

Dear list, I am confused on how to explain the interaction term in the context of "treatment contrast". for example, I have an data frame as below: sub group val 1 a group1 3.685625 2 a group1 3.407445 3 a group1 4.040920 4 a group1 2.890875 5 b group1 3.853280 6 b group1 4.113585 7 b group1 3.043250 8 b group1 3.800920 9 c group1 5.394305 10 c

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

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

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

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, 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 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)

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

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

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, I ran the example on pp. 799-800 from Machael Crawley's "The R Book" using package survival v. 2.36-5, R 2.13.0 and RStudio 0.94.83. The model is a Cox's Proportional Hazards model. The result was quite different compared to the R Book. I have compared my code to the code in the book but can not find any differences in the function call. My results are attached as well as a

Dear all I am learning R I am doing Z-test with package 'BSDA' here is my syntax and result: > sdgr.ztest<-z.test(growth[type=='SD'& from_treeline=='above'], + growth[type=='SD'& from_treeline=='below'],alternative = "two.sided", + mu = 0, sigma.x =(sd(growth[type=='SD'& from_treeline=='above'],na.rm = T)), +

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

Dear R-users, I have 3 plant populations (fixed). Within each population there is the same number of “families” (random) – the seed progeny of the same plant. These families were exposed to 2 treatments (fixed) and their response was measured (mean values for 25 seedlings per family per treatment are presented in data table). I would like to know if there is a significant difference in the

SSweibull() :  problems with step factor and singular gradient Hello I am working with growth data of ~4000 tree seedlings and trying to fit non-linear Weibull growth curves through the data of each plant. Since they differ a lot in their shape, initial parameters cannot be set for all plants. That’s why I use the self-starting function SSweibull(). However, I often got two error messages:

Hello. I am new to R. And, I want to perform a multiple nested anova on a large datasets (with 9448 observations). Under the helps from R-Sig-ecology mailing list, I have gained many progresses. But I still have some confusions. I want to ask for some helps here. my dataset("SeedL.txt") was not attached. Data are not sorted by factors. In this dataset, "SpecN"

I got two .RData file e.g data.2005.RData & data.2006.RData I would like to combine these two different data set and make single RData file. in both file there are some NULL files are also available and I would like to clear this NULL files also. $ : NULL $ : NULL $ : num [1:43285, 1:8] -21.1 -21.1 -24.9 -24.9 -24.9 ... ..- attr(*, "dimnames")=List of 2 .. ..$ : NULL ..