similar to: Fixed heritability in an lme model

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:

Hi, I'm using R for a QTL analysis of SNP data. I was wondering if anyone had any advice on fitting a dominance effect into the following function; > myfun4 function (x) { x <- scan(con, nmax=169) y <- unique(x[which(!is.na(x))]) if(length(y)>1) { summary(lme(Ad ~ x, random= ~1|sire, na.action="na.omit")) } else {print("no.infomation")} } Con is the

-------- Original Message -------- Subject: [AGDG-LIST:405] R Computing Contest Date: Sat, 21 Oct 2006 12:08:13 -0400 From: Larry Schaeffer <lrs at uoguelph.ca> Reply-To: lrs at uoguelph.ca To: Animal Geneticist's Discussion <agdg-list at colostate.edu> For those that are interested only: R Computer Programming Challenge Given: y = Factor A + Factor B + b1(Covariate1) +

I'm trying also to understand how to get the between-group variance out of a one-way ANOVA, but I'm beginning to think that in a sense, the variance does not exist. Emma said: *The model is response(i,j)= group(i)+ error(i,j)* Yes, if by group(i) you mean intercept + coefficient[i]. *we assume that group~N(0,P^2) and error~N(0,sigma^2) * Only the error is assumed to be a random

Hello, I'm looking for a method to estimate narrow sense heritability of traits in a RIL population. Papers I've checked either use either SAS or SPSS or do not give any details at all. I've found some reference to using variance components in ANOVA, using the kinship or wgaim packages, but I don't have a clue as to how to do any of this. Is there any way fro a very R illiterate

Hello, I am trying to make the switch from SAS, and I have a fairly elemental problem with syntax using the nlme package for analyzing mixed models. There was a previous question on this topic posted to this list, so I apologize for redundancy, but I didn't understand the advice given to that inquiry. The model I want to run has the following factors: Host (fixed) Sire (random) Dam

Hi. I was wondering if anyone might have some suggestions about how I can overcome a problem of "iteration limit reached without convergence" when fitting a mixed effects model. In this study: Outcome is a measure of heart action Age is continuous (in weeks) Gender is Male or Female (0 or 1) Genotype is Wild type or knockout (0 or 1) Animal is the Animal ID as a factor

Hello R Users, I am investigating the basic use of the LME function, using the following example; Response is Weight, covariate is Age, random factor is Genotype model.lme <- lme (Weight~Age, random=~ 1|Genotype) After summary(model.lme), I find that the estimate of Age is 0.098 with p=0.758. I am comparing the above model with the AOV function; model.aov <- aov (Weight~Age + Genotype)

I want to specify a two-factor model in lme, which should be easy? Here's what I have: factor 1 - treatment FIXED (two levels) factor 2 - genotype RANDOM (160 genotypes in total) I need a model that tells me whether the treatment, genotype and interaction terms are significant. I have been reading 'Mixed effects models in S' but in all examples the random factor is not in the main

I assume I'm missing something obvious here... The short form of my main question is: how do I extract variance components from an lme object? The longer form (plus optional supplementary question!): I'm looking at some quantitative genetics, and want to estimate two variance components so that I can then calculate a statistic called Qst from them. So I have this: reg1 <- lme(y ~

Hi All I was wondering how to get the same results with gls and lme. In my lme, the design matrix for the random effects is (should be) a identity matrix and therefore G should add up with R to produce the R matrix that gls would report (V=ZGZ'+R). Added complexity is that I have 3 levels, so I have R, G and say H (V=WHW'+ZGZ'+R). The lme is giving me the correct results, I am

Dear R-Users, I am currently working with LME to analyse repeated measures data. I encounter a problem when including both a random effect and a correlation structure with different grouping levels into the LME model. The error message is: Error in lme.formula(diameter ~ flowers*timef + competition*timef + population*timef, : Incompatible formulas for groups in "random" and

Hello, My R skills are somewhere between novice and intermediary, and I am hoping that some of you very helpful forum members, whom I've seen work your magic on other peoples' problems/questions, can help me here. I have a matrix with the following format: (i) individual plants comprising many different genotype groups (i.e., a plant is genotype 1 or genotype 2 or genotype 3, etc). The

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.

Hello folks, I have some data where spatial autocorrelation seems to be a serious problem, and I'm unclear on how to deal with it in R. I've tried to do my homework - read through 'The R Book,' use the online help in R, search the internet, etc. - and I still have some unanswered questions. I'd greatly appreciate any help you could offer. The super-super short explanation is

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 >

Hi, To simplify, suppose I have 2 observations each day for three days. I would like to define the correlation structure of these 6 observations as follows: the correlation of 2 observations on the same day is, say, alpha, the correlation for 2 observations one day apart is rho and the correlation for 2 observations 2 days apart is rho^2. I.e. I would like to have an AR1 correlation + a

Dear all,   I have data from the following experimental design and trying to fit a mixed model with lme function according to following steps but struggling. Any help is deeply appreciated.   1) Experimental design: I have 40 plants each of which has 4 clones. Each clone planted to one of 4 blocks. Phenotypes were collected from each clone for 3 consecutive years. I have genotypes of plants. I

Hi, I have binary (0,1) data for a trait as my response variable, and a dependent variable, genotype, with three classes (AA, AB, BB). I would like to plot this data so that across the three genoytpes, even though the points are all either 0 or 1, i want them to stack up or be seen using 'jitter'. So far I have been able to do this using xyplot {lattice} (code below) but could not get

Hi the list, A new version (0.92) of my package 'noia' will be available soon on CRAN mirrors, and I think it might be a good opportunity to introduce it shortly to the R community. In summary: 'noia' will be of absolutely no interest for 99.99% of you. The 0.01% remaining are quantitative geneticists who are interested in measuring the effect of genes in a proper way. Since