search for: lmekin

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$coeffici...

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)))...

...f 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 fixef fixef fixef fixef fixef b ranef ranef ---- ranef ranef var(beta) vcov vcov ---- vcov vcov var(b) VarCorr VarCorr ranef...

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...

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 them > by the coef() funct...

...rame(matrix(dat, ncol = 100)) > > names(snpmat) <- c(paste ("VR",1:100, sep='' )) > > yvar <- rnorm(100, 30, 5) > covtrait <- rnorm(100, 10, 5) > > mydata <- data.frame(id, yvar, covtrait, snpmat) > #******************************mixed model in lmekin ******************************************* > > fmod <- lmekin(yvar ~ mydata[,3] , data= mydata, random = ~1|id, > varlist=list(kmat)) $coefficients[2,4] # does not work > > # **************************************error > message********************************************...

I've moved nlme from Depends to Imports in my coxme package. However, a few of the examples for lmekin use one of the data sets from nlme. This is on purpose, to show how the results are the same and how they differ. If I use data(nlme::ergoStool) the data is not found, data(nlme:::ergoStool) does no better. If I add importFrom(nlme, "ergoStool") the error message is that ergoSto...

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 th...

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...

...switching to PDF mode. See the pdftex.def package documentation for explanation. Type H <return> for immediate help. ... l.414 }\@ehc ? /usr/share/texmf-texlive/tex/latex/pdftex-def/pdftex.def:414: Emergency stop. ... l.414 }\@ehc No pages of output. Transcript written on lmekin.log. /usr/bin/texi2dvi: latex exited with bad status, quitting. make: *** [lmekin.pdf] Error 1 Error in buildVignettes(dir = "/home/therneau/research/surv/Hg/coxme.Rcheck/vign_test/coxme") : running 'make' failed Execution halted ----------------------------------------- The...

...seed(3456) SNPdata <- c(1:1000) dat <- sample(c(-1,0,1), 10000, replace = TRUE) snpmat<- data.frame(matrix(dat, ncol = 100)) names(snpmat) <- c(paste ("VR",1:100, sep='' )) yvar <- rnorm(100, 30, 5) mydata <- data.frame(trait1, covtrait, snpmat) fmod <- lmekin(y~ mydata[,i] , data= snpdata, random = ~1|id, varlist=list(kmat)) $coefficients[2,4] # does not work Ultimately I want to put into the loop: for(i in 3:length(snpdata)) { P <- vector (mode="numeric", length = 1000) P[i] <- lmekin(trait1~ snpdata[,i] , data= snpdata, random = ~1...

Hello, Why the correlation between the random effects is negative? library(coxme) rats1 <- coxme(Surv(time, status) ~ (1|litter), rats) random.effects(rats1)[[1]] #one value for each of the 50 litters print(rats1) rats2 <- lmekin(time ~ (1|litter), rats) fixed.effects(rats2) random.effects(rats2)[[1]] #one value for each of the 50 litters print(rats2) cor(random.effects(rats1)[[1]],random.effects(rats2)[[1]]) Thanks [[alternative HTML version deleted]]

...s there a way to turn the message off? I as a developer need to see it, but users don't and it may worry them unnecessarily. Updating all 17 of my test/*.Rout.save files is a nuisance as well, but only a nuisance. I'd like to upload this to CRAN soon as I have users asking for the updated lmekin function (which uses Matrix). In the long term all the bdsmatrix functions will be replaced by Matrix, but that requires major changes to C code so "long" is the operative word. Thanks, Terry T.

...ith R? I first > thought I could handle it somehow with nlme's correlation structures, > but these within-group structures are quite a different thing, right? > > Any suggestions would be highly appreciated- > Lutz > > -- > Lutz Ph. Breitling > University of Leeds/UK lmekin and coxme in Terry Therneau's kinship package may help. David Duffy.

Could anyone help with a linear mixed model fitting problem ? The model is : Y= Xp + Zu + e where X, Z are known design matrix, p is fixed effect factor, u is random effect, u~ (0, G) , e~(0,R) The main problem is , I want to fix the covariance matrix G to be a constant times a known covariance matrix A, G = c*A (c is positive constant, A is a predefined matrix with values manually set by