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Hello I have a data set summarized like this: File name= Height Group Ind Age Trait 1 1 1 20 1 1 2 21 1 2 1 22 1 2 2 21 1 3 1 24 1 3 2 45 1 4 1 23 1 4 2 26 2 1 1 45 2 1 2 12 2 2 1 25 2 2 2 26 2 3 1 45 2 3 2 43 2 4 1 23 2 4 2 47 . . . I would like to plot Trait ~ Age but a different plot for each Group. I tried: > plot(Height$Trait ~ Height$Age | Group) But does not work. Any

Hi, I was hoping someone could help me with the following problem. Consider this toy example. For the input dataset there are four individuals (rows "indv.1" through "indv.4"), measured for two different variables (columns "var.1" and "var.2") at two different levels of a factor (column "factor.level"). I want to calculate a matrix that has the

Dear R community, I performed a generalized linear mixed model using glmmPQL (MASS library) to analyse my data i.e : y is the response with a poisson distribution, t and Trait are the independent variables which are continuous and categorical (3 categories C, M and F) respectively, ind is the random variable. mydata<-glmmPQL(y~t+Trait,random=~1|ind,family=poisson,data=tab) Do you think it

Hello, I am trying to figure out how to conduct a t-test on a specific contrast for my data. I have four factors in my data and would like to conduct a t-test on the average of the data from the first two factors against the average of the data on the second two factor (i.e. is the average of the first two different from the average of the second two). Is there a quick way to do this? I found

Dear R-Helpers, I am using qtl package to analyze qtl data from QTL cartographer. I have the map file and cro file from QTL cartographer. I was trying to import these two files in R using qtl package. data=read.cross("qtlcart", ".", "crofile.txt", "mapfile.txt") ### I have matched the file structure with the one on the website of qtl package - It matches

Following is a function that I wrote (It is working well). It's a simple one, nothing complicated. The only question that I have is a qutation mark issue, I guess. ############################################# funcname <- function(trait.file){ #line1 setwd('/root/subroot') # line 2 load('imge.RData')

Hi, You should look at Pinheiro and Bates (2000) Mixed-effects models in S and S-Plus. It describes how to format the correlation matrix to pass to functions lme and gls. Basically, the correlation matrix has to be one of the corStruct classes, probably corSymm for your example. So in the call to lme (or gls if you really have no random effects), use something like:

When I try to adjust a mixed model with random effects I can make this order without problem > lm.FA<-lme(absFA~trait*condition,random=~1|individual) But if I try to fit a model in which the response (absFA) is not the same in all individuals at different levels of "trait" factor , but varies randomly from each. That is, this order >

Good afternoon, I REALLY try to answer to my question as an autonomous student searching in the huge pile of papers on my desk and on the Internet but I can't find out the solution. Would you mind giving me some help? Please. ######################################### I'm trying to use glm with factors: > Pyr.1.glm<-glm(Pyrale~Trait,DataRav,family=poisson) If I have correctly

Is there a package that will allow me to fit Brownian motion and Ornstein?Uhlenbeck models of evolution for discrete traits? I know that geiger and ouch have commands for fitting these models for continuous traits, but these aren't suitable for discrete trait evolution, correct? -- View this message in context:

I use the step() function occasionally, and I think I understand its objective, proper use, and limitations. Now I see stepwise model selection being used in what seems to be an unusual way, and I wonder if it is right or wrong. May I describe? Genetic mapping tries to find where in an animal's genome are genetic elements that influence a particular physical trait. Say there are 100

Hello, I got an error message saying Error in lognet(x, is.sparse, ix, jx, y, weights, offset, alpha, nobs, : NA/NaN/Inf in foreign function call (arg 5) when I try to analysis a binary trait using glmnet(R) by running the following code library(glmnet) Xori <- read.table("c:\\SNP.txt", sep='\t'); Yori <- read.table("c:\\Trait.txt", sep=',');

I hope you can help with this one. I have two matricies: 1. A species abundance matrix: <http://r.789695.n4.nabble.com/file/n4643979/2species_matrix.jpg> 2. A species trait score matrix: <http://r.789695.n4.nabble.com/file/n4643979/2trait_matrix.jpg> The trait matrix lists trait scores for each species as listed in the species abundance matrix. I would like to create a script

Thanks for your answers Stephen and Ben, I hope I am posting on the correct list now. I managed so far to run the multinomial model with random effect with the following command: MCMCglmm(fixed=cbind(Apsy,Mygl,Crle,Crru,Miag,empty) ~ habitat:trait,random=~idh(trait):mesh,family="multinomial12", data=dataA,rcov=~trait:units) (where multiple responses are different species, Habitat

Hi, I performed an experiment where I raised different families coming from two different source populations, where each family was split up into a different treatments. After the experiment I measured several traits on each individual. To test for an effect of either treatment or source as well as their interaction, I used a linear mixed effect model with family as random factor, i.e.

Hi, First I should note I am relatively new to R so I would appreciate answers that take this into account. I am trying to perform an MDS ordination using the function ?metaMDS? of the ?vegan? package. I want to ordinate species according to a set of functional traits. ?Species? here refers to ?sites? in traditional vegetation analyses while ?traits? here correspond to ?species? in such

Hi, I am working with a species-by-trait .csv file (columns=traits, rows=species) and get the following warning message when trying to plot results of both metaMDS and pcoa:  "Warning message: In ordiplot(x, choices = choices, type = type, display = display,  :   Species scores not available" I am using a Gower's transformation in both procedures within the metaMDS or pcoa

Hi, Thanks in advance for any help on this question. I'm running multinomial models using the MCMCglmm package. The models have 5 outcome variables (each with count data), and an additional two random effects built into the models. The issue is that when I use the following code, the summary only gives me results for four of the outcome variables. Here is the code for my model: m3.random

Hello, I'm trying to illustrate the relationships between various trait and environment data gathered from a number of sites. I've created a GAM to do this: gam1=gam(trait~s(env1)+s(env2)+te(env1,env2)) and I know how to create a 3D plot using vis.gam. I want to be able to show points on the 3D plot indicating the sites that the data came from. I can do this on a 2D plot when there is one

Implement the DRM driver abstractions. The `Driver` trait provides the interface to the actual driver to fill in the driver specific data, such as the `DriverInfo`, driver features and IOCTLs. Co-developed-by: Asahi Lina <lina at asahilina.net> Signed-off-by: Asahi Lina <lina at asahilina.net> Signed-off-by: Danilo Krummrich <dakr at redhat.com> ---