similar to: Mixed-effects models / heterogenous covariances

Dear R-list, maybe someone can help me with the following mixed-effects models problem, as I am unable to figure it out with the 'nlme-bible'. I would like to fit (in R, obviously) a so-called animal model (google e. g. "Heritability and genetic constraints of life-history" by Pettay et al.) to estimate the variance component that is due to genetic effects. The covariances of

Hi,I use the 'lmekin' model of the 'kinship' package of R in order to estimate heritability. I want to estimate the confidence interval of the variance coefficient and so I should use a bootstrap simulation. The pedigree file has 1386 subjects so I create a kinship matrix [1386*1386].This is the code of R I use: kfit2 <- lmekin(IT~1+AGE +(1|ID), dati1,

Dear all, i am trying to redo the 'eyestudy' analysis presented on the site http://www.ats.ucla.edu/stat/stata/faq/relative_risk.htm with R (1.9.0), with special interest in the section on "relative risk estimation by poisson regression with robust error variance". so i guess rlm is the function to use. but what is its equivalent to the glm's argument "family"

Dear All, I'm analysing a negative binomial dataset from a population-based study. Many covariates were determined on household level, so all members of a household have the same value for those covariates. In STATA, there seems to be an option for 'clustered analysis' for neg-bin regression. Does an equivalent exist for R(MASS)'s glm.nb or a comparable function? Many thanks for

Dear List, having searched the help and archives, I have the impression that there is no automatic model selection procedure implemented in R that includes/excludes predictors in logistic regression models based on LRT P-values. Is that true, or is someone aware of an appropriate function somewhere in a custom package? Even if automatic model selection and LRT might not be the most appropriate

Version 2.2 of coxme has been posted to CRAN, Windows versions and mirrors should appear in due course. This is a major update with three features of note: 1. A non-upwardly compatable change: Extractor functions: beta= fixed effects, b=random effects nlme lme4 coxme <2.2 coxme 2.2 lmekin 2.2 ------------------------------------------------------ beta

Hi, I had a problem with the lmekin() in kinship package: lmekin() can not be wrapped into another function library(kinship) #creat an example dataset xx<-rnorm(100) yy<-rnorm(100) id<-1:100 test.dat<-as.data.frame(cbind(xx,yy,id)) rm(xx,yy,id) a<-bdsmatrix(rep(10,10),rep(block,10),dimnames=list(c(1:100),c(1:100))) #100x100 block (n=10) diagonal matrix to indicate the

On 09/06/2012 05:00 AM, r-help-request at r-project.org wrote: > Hi, R experts > > I am currently using lmekin() function in coxme package to fit a > mixed effect model for family based genetic data. How can I extract the p > value from a lmekin object? When I print the object in R console, I can > see the p value and Z value are just over there. But I can not extract

Hi, R experts I am currently using lmekin() function in coxme package to fit a mixed effect model for family based genetic data. How can I extract the p value from a lmekin object? When I print the object in R console, I can see the p value and Z value are just over there. But I can not extract them by the coef() function. kinfit$coefficient$fixed (kinfit is the name of the lmekin object)

I am making the question clear. Please help. > Dear R experts > > I was using kinship package to fit mixed model with kinship matrix. > The package looks like lme4, but I could find a way to extract p-value > out of it. I need to extract is as I need to analyse large number of > variables (> 10000). > > Please help me: > > require(kinship) > > #Generating

Take a look at lmekin() in the coxme package. The motivating data set for my development of coxme was the Minnesota Family Breast Cancer project: 24050 subjects in 462 families. The random effect is an intercept per subject with sigma^2 K as its variance where K is the kinship matrix (1 for self-self, .5 for parent-child or sib-sib, .25 for uncle-neice, etc). lmekin is a linear models front

Someone just sent me a data set that causes the lmekin function, part of the kinship package, to fail. In chasing it down I get an error I have never seen before. fit <- lmekin(icam1 ~ factor(center) + age + factor(sex), random= ~1|iid, data=chaidata, varlist=kmat) Error in Y - fitted : non-numeric argument to binary operator Add the recover option, and the offending lines are

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

Dear R experts I was using kinship package to fit mixed model with kinship matrix. The package looks like lme4, but I could find a way to extract p-value out of it. I need to extract is as I need to analyse large number of variables (> 10000). Please help me: require(kinship) Generating random example data id <- 1:100 dadid <- c(rep(0, 5), rep(1, 5), rep(3, 5), rep(5, 5), rep(7,

I'm having a problem rebuilding a package, new to me in R 2.15.0 (Linux) It hits all that contain the line \usepackage[pdftex]{graphics} and leads to the following when running R CMD check on the directory. (I do this often; a final run on the tar.gz file will happen before submission.) Since I float and resize my figures, removing the line is fatal in other ways.

Hi everyone thanks for the replies. The issue was NOT a font problem; I deliberately chose ll1 and l11 as examples of easily confused variable names (evidently these were too easily confused ;-). The code snippet was written as intended, and increment() contained a deliberate, highlighted, bug. I was asking for guidance on avoiding/finding this sort of coding error. That was why I wrote

Hi everyone, I use the MNP package to fit my data. I can interpret the coefficients and covariance. I cannot put them into the original model which is Wi=Sum(bXij) over j, Yi(Wi)=0 if max(Wi)<0, j if max(Wi)=Wij>0 j=3 here, 3 choice alternatives, Clio, Punto, Polo I don't know how to put the coefficients and covariances into W, to construct W for each j. The coefficients and

Hi R help, I wanted to set a fixed heritability for the object "Trait" left of ~ operator. Is this possible using lme, or would I need to use something like gls? This is the model line contained within a function; lme.out <- summary(lme(Trait ~ genotype, random = ~1|sire, weights=~number, na.action="na.omit")) Many thanks for any help.

I was asked privately > I have been using <the Matrix package> extensively to > calculate the relatedness-matrix based on the pedigree for > our ornamental plants. I was wondering if you could give > me some advice, for which I would like to thank you in > advance! > The output of the relatedness matrix I get from > the nadiv package is a

Hello, how do I obtain standard errors of variances and covariances of the random effects with LME comparable to those of for example MlWin? I know you shouldn't use them because the distribution of the estimator isn't symmetric blablabla, but I need a measure of the variance of those estimates for pooling my multiple imputation results. Regards, Roel.