similar to: Urgent - I really need some help lme4 model avg Estimates

Hello, I hope that you can bare with me. I am new to models, but I think I have a pretty godd understanding of how to run them now, including how to use AICc and Anova. The issue is that I have many factors that I wish to compare so doing each one at a time would take forever. I came across the MuMIn package and I was so excited, however I am getting an error message and i don't know why.

Hello, I have been attempting to set up a lme and have looked at numerous posts including 'R's lmer cheat-sheet' as well as reading a number of papers and other resources including R help, but I am still a little confused on how to write my model (I thought I had it). I have asked a number of questions on different forums; most of which have been resolved. My main concern right now

Dear R users, I am trying to fit a GLMM using lmer to a dataset where the brood identity (LNRREIR) is nested within mothers identity. The reason for this is that each mother can have several nests within each year and also between years. I am running the following script (actually I have tried all different combinations with LNRREIR and mother): mod <- lmer(sr~z.hatchday +

Hello R users, Doing My PhD I collected count data which I believe is zero inflated. I have run a statistical model with lmer and family=poisson and got summary(model)@sigma=1 so I believe there is no overdispertion. I would like to use the fmr function from the 'gnlm' library but I just cannot figure out from the examples in the help page and some forums out there how to convert the lmer

Hello, I have a dataset with a continuous independent variable (fish length, range: 30-150 mm) and a binary response (foraging success, 0 or 1). I want to discretize fish length into 5 mm bins and give the proportion of individuals who successfully foraged in each each size bin. I have used the cut function to discretize the length values into my desired bins, but I can't figure out how to

Dear all, I want make correction depth of a bathymetric data set. To do so, I have the depth data set sample every second (a depth at each second) in one hand, and in the other hand, I have a tide variation level data set sample every 250 ms. The time register in each data sets (tide and bathymetric) is express in seconds followinf this format : hh:mm:ss.ss I would like to rectify the depth

Trying to find a quick/slick/easily interpretable way to collapse a data set. Suppose I have a data set that looks like this: h <- structure(list(INDEX = structure(1:6, .Label = c("1", "2", "3", "4", "5", "6"), class = "factor"), TICKS = c(0, 0, 0, 0, 0, 3 ), BROOD = structure(c(1L, 1L, 2L, 3L, 3L, 3L), .Label =

Dear R users, It may be very simple but it is being difficult for me. I'd like to calculate the difference in percent between to measures. My data looks like this: set.seed(123) df1 <- data.frame(measure = rep(c("A1", "A2", "A3"), each=3), water = sample(c(100:200), 9), tide = sample(c(-10:+10), 9)) df1 # What I want to calculate is:

I have been trying to visualize times of lowest tides, month by month. I have tide predictions with times either in unix time or a text format, and heights in feet or meters. I had been able to derive the clock times of each prediction. I would now like to graph this data with points showing heights as "r" and times as "theta", from 0000 to 2355. There is a seasonal

Hi R experts, I am interested on the effects of two dietry compunds on the growth of chicks. Rather than extracting linear growth functions for each chick and using these in an analysis I thought using ReML might provide a neater and better way of doing this. (I have read the pdf vignette("MlmSoftRev") and "Fitting linear mixed models in R" by Douglas Bates but I am not

Hello, everyone. I'm hoping to prevent myself from doing a lot of pointing and clicking in Excel. I have a dataframe of bird nest check observations, in which I know the date of the first check, the date of the second check (both currently in Julian date format), the status of the nest at the second check (alive or failed), and the date that the nest hatched (i.e. changed from Incubation

I'm having a little trouble with trying to install newer versions of Starcraft (updates or Brood War) on a win-free Wine installation. I can run the installer off the original Starcraft CD, and it puts all of the files in the right place, adds a couple registry entries, and then chokes on creating the entries in the Start Menu. Still, I can kill the process at that point, and I have a usable

Hi: I have the following dataset in R( thanks Gabor for your help) but now the problem is that all the dashes are converted to questions marks ("?") I?am trying to get the dashes back using: mydf <- data.frame(lapply(mydf,function(x) replace(mydf,"?"(x),"-"))) but isn't working. I also tried: mydf <- replace(mydf,mydf=="?","-)")

Hi: I?need?help with a dataframe(see?pic attached). is a mix of dates and text. I want to create a table either?using latex function from hmisc or xtable. I already know how to do this but the problem is getting the dataframe into R. I don't have a reproducible example?but I am hoping that the pic attachment will make it to you.?If someone is interested in helping with this task I could send

Hello! So, my problem is following. I have bird offspring growth data and I'd like to model individual growth curves (aim is to study asymptotes and inflection points) with nlme according to Pinheiro & Bates 2000: first using nlsList to generate individual curves and then nlme to study the parameters and fixed effects. The data is structured to two levels. I have broods and

Hi, Use offset variables if count occurrences of an event and you want to model the observation time. glm(count ~ predictors + offset(log(observation_time)), family=poisson) If you want to compare durations, look at library(survival), ?coxph If tnoise_sqrt is the square root of tourist noise, your example seems incorrect, because it is a predictor, not the dependent variable tnoise_sqrt ~

Dear all, This question is basic but I am stumped. After running the below, I receive the message: "non-integer #successes in a binomial glm!" model1 <- glmmML(y~Brood.Size*Density+Date.Placed+Species+Placed.Emerging+Year+rate.of.parperplot, data = data, cluster= data$Patch, family=binomial(link="logit")) My response variable is sex ratio, and I have learned quickly not

I am working with transition matrices of sequences of animal behaviours. What I would like to do is parse the original matrices, adjusting row/column order so that the matrix has its main values in blocks surrounding the diagonal. This would cause behaviours involved in functional groupings (e.g. grooming, resting, foraging etc) to appear as blocks. This can be done manually by applying

Hello Lets say as an example I have a dataframe with the following attributes: rownum(1:405), colnum(1:287), year(2000:2009), daily(rownum x colnum x year) and foragePotential (0:1, by 0.01). The data is actually stored in a netcdf file and I'm trying to provide a conceptual version of the data. Ok. I need to calculate a moving mean and a moving variance for each cell on the following

I'm stumped, can anyone find my error in this sequence. for(j in 1:(varsize[4]-1)) temp <- get.var.ncdf(nc=input, varid="p_foraging",c(1,1,j),c(varsize[1],varsize[2],1)) filled.contour(x, y, temp, color = terrain.colors, plot.title = title(main = paste("Everglades Wood Stork Foraging Potential \nYear", (2000+j)), xlab =