similar to: Ordinal mixed model

Good afternoon, gentlemen! After several days studying and researching on categorical data (various forums with answers from the owner of the library - all incipient) how to interpret the output the function MCMCglmm, come to enlist the help of you, if someone has already worked with MCMCglmm function in the case of variables ordinal dependent. I've read and reread all the pdf's of the

Hi All, The data set that I have is a cluster data, and I want to run a HLM mixed model with multi-level response. Here is my data set: response: - Level (num: 1, 2, 3, 4, 5 - 5 levels) Covariates: - Type (Factor: A, B, C - 3 levels) - yr (num: 2006, 2007, ...) - Male (num: 0=not Male, 1=Male - 2 levels) - Ethnicity (Factor: A, B, H, ..., - 7 levels) - ELL (num: 0, 1, - 2

Dear All, This may sound like a dumb question but I am trying to use a mixed model to determine the predictors of bat activity along hedges within 8 sites. So my response is continuous (bat passes) my predictors fixed effects are continuous (height metres), width (metres) etc and the random effect is site - can you tell me if the fixed effects can be continuous as all the examples I have

Dear Mr. or Ms.,   I would like to use the R-software to run the zero inflated mixed model and hurdle mixed model. But I do not know how to do? Would you please tell me the code and data format?   I will be very appreciated if you can help me. Thank you very much.   Best regards,   Sincerely, Xiongqing Zhang [[alternative HTML version deleted]]

Hi to all the people, I'm working with abundance data of some species, but containing too zero values, and the factors are the ones typical in a BACI experiment (Before-and-After-Control-Impact). Thus, these are two fixed factors. As the data does not holds the normality and homogeneity of variances assumptions of clasiccal ANOVA, I'm trying to fit a zero-altered model using the MCMC glmm

Hi all,   I have data set with repeated measurement ordinal responses and I would like it using generalized linear mixed model.   Shall I use MCMCglmm packeage or I can use lme4 package?   waiting for  answer eagerly, [[alternative HTML version deleted]]

Hi, I have a dataset where the residual variance decreases with on one of the predictors (population size). Currently, the full model looks like this: prior<-list(R=list(V=1e-16, nu=-2),G1=list(V=diag(2), nu=2)) m<-MCMCglmm(response~poly(population size,2)*poly(other predictor,2)+time, random=~us(1+time):population, data=data, prior=prior) Basically, it's a random regression with

I am attempting to replicate some of my experience from SAS in R and assume there are best methods for using a combination of summary(), subset, and which() to produce a subset of mean values by categorical or ordinal factors. within sas I would write proc means mean data=dataset; class factor1 factor2 var variable1 variable2; RUN; producing an output with means for each variable by factor

Hi R-users: Can anyone give an example of giving starting values for MCMCglmm? I can't find any anywhere. I have 1 random effect (physicians, and there are 50 of them) and family="ordinal"? How can I specify starting values for my fixed effects? It doesn't seem to have the option to do so. Thanks, Ping

I have a dataset relating the effects of engineering works on the level of salinity in a river before and after the works. I have modelled this using linear mixed effects models to determine if the significance and level of the response to the works. I am wanting to calculate the se of difference at several points. I am not sure of how to calculate the difference and which estimate of se I

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Hello, I wanted to fit a linear mixed model to a data that is similar in terms of design to the 'Machines' data in 'nlme' package except that each worker (with triplicates) only operates one machine. I created a subset of observations from 'Machines' data such that it looks the same as the data I wanted to fit the model with (see code below). I fitted a model in

Dear all, I'm not getting what I'm doing wrong. The line below from my read.fsa.bin function throws an error when just loading my AFLP package and disappears when I load the zoo package as well. #the line that throws the error Index <- which(Peak == rollmax(Peak, k = 1 + 2 * floor((min(diff(SizeStandard)) * Fs - 1) / 2), fill = -Inf)) #the error Error in UseMethod("rollmax")

Hi All, I am running an Rscript with a bunch of algorithms that are UNSTABLE under some parameter settings. At a certain point one of them sends error massage and my whole run STOPS! What I would like is to save the error massage in some file or variable and carry on to the next command line without stopping this run... Any help or ideas would be welcome, please, with a concrete example (not

Hi, I have following codes : library(zoo); library(ggplot2); library(plyr) dat <- rnorm(306); vv <- letters[1:6]; dat1 <- data.frame(dat, vv) dat2 = zooreg(rnorm(51), as.yearmon(as.Date("2000-01-01")), frequency=12) ggplot(dat1) + geom_line(aes(y=dat, x=index(dat2), colour=vv), group=vv, size = 1.3) However I got error while plotting them :

Hello, I am working in a longitudinal study, with a categorical variable as a dependent variable (alcohol consumption: no use, use, abuse and dependence) with repeated measures (baseline, 1 year, 2 years). Besides I have another variable with two groups: control and experimental. I would like to analyze the evolution in each group, I have though in lme, but I am not sure if I can do an lme with

Dear all, I have a list of n matrices which all have the same dimension (r x s). What would be a fast/elegant way to calculate the element wise average? So result[1, 1] <- mean(c(raw[[1]][1, 1] , raw[[2]][1, 1], raw[[...]][1, 1], raw[[n]][1, 1])) Here is my attempt. #create a dummy dataset n <- 3 r <- 5 s <- 6 raw <- lapply(seq_len(n), function(i){ matrix(rnorm(r * s), ncol =

Dear R experts, Might be very simple question to ask but would be insightful. As the same story of nested "for loops". following is the code that I am using to get the autocorrelation function of the sample data. I have tried to get rid of for loops but since I am touching R after such a long time that I need to practice more but I need help to revive my skills. I know that apply() or

Hello, is it possible to create two uncorrelated random vectors for a given distribution. In fact, I would like to have something like the function "rnorm" or "rlogis" with the extra property that they are uncorrelated. Thanks for your help, Luba [[alternative HTML version deleted]]

Hello, I'd like to plot an empirical cumulative distribution function, except instead of the fraction of values < x, I'd like the fraction of values > x. I think this can be done using the ecdf function in {Hmisc}. I installed the package and loaded it. However, when following the example given in the documentation, I get an error: x <- rnorm(100) ecdf(x,what='1-F')