similar to: Comparing GLMMs and GLMs with quasi-binomial errors?

Hello, I am trying to create multiple matrices (to run a PVA) but can't import all of them from a .csv without the numbers treated as labels and not factors. I can enter the matrix slowly: Site05_96 <- matrix(c(0.07,0,0.03,0.00,NA,0.00, 0.09,0.166666667,0.31,0.42,NA,0.00, 0.00,0,0.00,0.00,NA,0.00, 0.00,0,0.00,0.00,NA,0.00,

Hi all, I am trying to set up a simple path analysis in the SEM package, but I am having some trouble. I keep getting the following error message or something similar with my model, and I'm not sure what I'm doing wrong: Error in solve.default(C) : system is computationally singular: reciprocal condition number = 2.2449e-20 In addition: Warning message: In sem.default(ram = ram, S = S,

Dear members of the R help list, I want to do a hierarchical glm with binomial family but am unsure about how to write the syntax which involves nesting. I want to test whether the risk of being attacked by Herbivores for Meadowsweet plants is significantly dependent on the Distance to heterospecific source plants. Dependent variable = Herbivory (yes/no) Explanatory continuous variable =

Hello, R-users,   I have a datafile with 37313 records and each record has 5 different measurements on the same variables. The format looks like this: treeID, VIG0, VIG1, VIG2, VIG3, VIG4 I was trying to convert the one row record to 5 rows record with format like this (treeID, MEASUREMENT, VIGOR). My code like this:   treeMeas<-matrix(data=0,nrow=(length(tree1$indivTree)*5), ncol=3)

Dear all, I'm quite sure that this is a stupid question, but I'll ask anyway. I want to perform an ANCOVA with two continuous factors and three categorical factors. Plant population growth rate (GR) = dependent variable Seed reduction due to herbivory (SR) = continuous explanatory variable Herbivore species (HS, 2 levels) = categorical explanatory variable Population (Pop, 24 levels) =

Dear all, Is it possible to incorporate a nested design in GEE? I have measurements on trees that where measured in two years. The trees are nested in plots. Each plot contains 24 trees. The number of plots is 72. Hence we would expect 2 * 24 * 72 = 3456 data points. A few are missing, so we end up wih 3431 data points. This is what I have tried until now. #assuming independence between trees

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 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

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)

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

Dear all I was trying to put a title for my plot, but i got this result, > x11(width=10,height=5,title="seedling");par(mfrow=c(1,2))Error in x11(width = 10, height = 5, title = "seedling") : unused argument(s) (title = "seedling")> The title is not taking How can i give a title for the plot (where in need to make 2 plots within a window of x11() ) thanking

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'm a beginner user in R and I would like to make a quadratic and plateau model in R. Can you help please with an example? Thanks so much -- Ahmed M. Attia Assistant Lecturer El-Khattara farm Station Agronomy Dept., Zgazig Univ., Egypt Visiting Scientist Haskell Agricultural laboratory Agronomy and Horticultural Dept., Univ. of Nebraska-Lincoln ahmedatia at zu.edu.eg

Could someone explain the meaming of this error message from augPred: > augPred(area3.pen.nlme, primary=~day) Error in predict.nlme(object, value[1:(nrow(value)/nL), , drop = FALSE], : Levels 1,2,3 not allowed for block > predict.nlme(area3.pen.nlme) does not produce an error. area3.pen.nlme was created with: > area3.pen.nlme <- nlme(area ~ SSlogis(day, Asym, xmid, scal),

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,

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

I'm trying to plot observations against observation dates and getting julian dates along the x-axis: library(date) Week<-as.date(c("05/02/03","05/09/03","05/16/03","05/23/03","05/30/03","06/0 7/03","06/14/03")) Leafminers<-c(0,2,5,10,4,6,5) Diglyphus<-c(0,0,4,5,7,3,1) LeafDig<-cbind(Week,Leafminers,Diglyphus)

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