similar to: Bug in coef<-.varIdent method (nlme package) (PR#9831)

Hello, I am using R2.5 under Windows. Looks like the following statement vars <- (obj$sigma^2)*vw in getVarCov.gls method (nlme package) needs to be replaced with: vars <- (obj$sigma*vw)^2 With best regards Andrzej Galecki Douglas Bates wrote: >I'm not sure when the getVarCov.gls method was written or by whom. To >tell the truth I'm not really sure what

Dr. Andrzej Galecki will present his online course "Mixed Effects Models with Applications" May 12 ? June 9 at statistics.com. This course will explain the basic theory of linear and non-linear mixed effects models, including hierarchical linear models (HLM). It will outline the algorithms used for estimation, primarily for models involving normally distributed errors, and will provide

Getting a crash with R1.8.1 on windows 2000 an linux with the strsplit. Version: platform = i386-pc-mingw32 arch = i386 os = mingw32 system = i386, mingw32 status = major = 1 minor = 8.1 year = 2003 month = 11 day = 21 language = R Version: platform = i386-pc-mingw32 arch = i386 os = mingw32 system = i386, mingw32 status = major = 1 minor = 8.1 year = 2003 month = 11 day =

Hi, I would like to fit a model for a factorial design that allows for unequal variances in all groups. If I am not mistaken, this can be done in lm by specifying weights. A function intended to specify weights for unequal variance structures is provided in the nlme library with the varIdent function. Is it apropriate to use these weights with lm? If not, is there another possibility to do

I am using lme from the nlme package to fit a mixed model. We have observations nested in patients(encounters) and patients nested in groups (2 different treatments). We are interested in the differences between the 2 groups, both the means and the standard deviations (are patients in group A less variable than those in group B? both within patient and between patient within group). Here is

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Hello, As I am relatively new to the R environment this question may be either a) Really simple to answer b) Or I am overlooking something relatively simple. I am trying to add a VarIdent structure to my gam model which is fitting smoothing functions to the time variables year and month for a particular species. When I try to add the varIdent weights to variable Month I get this error returned.

I was reading the help but just did not get how to specify starting values for varIdent() of the lme() function, although I managed to do it for corSymm(). Do I specify the values just as they are printed out in an output, like c(1, 1.3473, 1.0195). Or do I need to take the residual and multiply it with these like c(0.2235, 0.2235*1.3473, 0.2235*1.0195) or any other form that I dont know of?

How does one implement a likelihood-ratio test, to test whether the variances of the random effects differ between two groups of subjects? Suppose your data consist of repeated measures on subjects belonging to two groups, say boys and girls, and you are fitting a linear mixed-effects model for the response as a function of time. The within-subject errors (residuals) have the same variance in

I wonder if someone could explain how the following R idiom works (it's used in glm.fit). start <- coef start[fit$pivot] <- coef coef is a vector of coefficients, set by .Fortran("dqrls", ...). fit$pivot is a vector of integer indexes (indicating how dqrls permuted the columns of x). If coef has n elements, fit$pivot is a permutation of seq(1,5). start[fit$pivot]

Dear Martin, I think that your plan makes sense. It's too bad that aov() behaved differently in this respect from lm(), and thus created more work, but it's not be a bad thing that the difference is now explicit and documented. I expect that that other problems like this will surface, particularly with contributed packages (and I know that you're aware that this has already happened

Dear list, I was wondering if it is possible to get the p-values for one-sided tests on the parameters of a linear regression. For instance, I use lm() and store the result in an object. lm() gives me a matrix, using summary() and coef() on which gives me a matrix containing the coefficients, the standard errors, the t-statistics and the two-sided p-values by default. Can I get it to provide me

Dear List, Must be a silly question, but I was wondering whether there is a direct way of calculating "standard error of a HR or exp(coef)" from coxph objects x <- coxph(Surv(time, status) ~ age + inst, lung)> x coef exp(coef) se(coef) z page 0.0190 1.02 0.00925 2.06 0.04inst -0.0104 0.99 0.01028 -1.01 0.31 cheers, Ehsan [[alternative HTML

Dear all, I?m having trouble getting a list of regression variables back into a dataframe. mydf <- data.frame(x1=rnorm(100), x2=rnorm(100), x3=rnorm(100)) mydf$fac<-factor(sample((0:2),replace=T,100)) mydf$y<- mydf$x1+0.01+mydf$x2*3-mydf$x3*19+rnorm(100) dlply(mydf,.(fac),function(df) lm(y~x1+x2+x3,data=df))->dl here I?d like to use ldply(dl,coef(summary)) or something

I was wondering if there is anyway to have Add1() display the coefficient estimates for each candidate predictor along with the F test. This is for lm() btw. Thanks -- View this message in context: http://r.789695.n4.nabble.com/Add1-w-coef-estimates-tp2279662p2279662.html Sent from the R help mailing list archive at Nabble.com.

Hi, how do I create a coef.table as for example returned by summary.glm? I don't see where the significance codes are assigned during summary.glm. Thank you, Benjamin

Hello< I routinely use the following without a problem: fm1<-lme(score~time,data,random=~time|ID) tmp<-coef(fm1, augFrame=T) However, in my current situation, I am running into the following error when I execute the coef() call Error in tapply(as.character(object[[nm]]), groups, FUN[[dClass]]) : arguments must have same length There are some cases with missing data, but I

Hi Everyone, I am using ASReml to fit a spatial model. I do not have all the components of ASReml when I call; names(summary()) e.g. names(summary(fcov.asr2)) [1] "call" "loglik" "nedf" "sigma" "varcomp" I am trying to get the coefs but I get "NULL". Does anybody know the reason? Any help would be much appreciated. Regards

Full_Name: Rick Sayre Version: 2.8.0 OS: windows, linux, os x Submission from: (NULL) (138.72.153.166) PR#8476 and PR#8478 http://bugs.r-project.org/cgi-bin/R/Models-fixed?id=8478 http://bugs.r-project.org/cgi-bin/R/Models-fixed?id=8476 discuss fixing qr.coef to handle complex matrices correctly But it appears the solution now "shipping" only handles square matrices. In 2.8.0 [linux,

Dear maintainers, I'm reporting a bug in qr.coef that mishandles the colnames of matrix. A minimal reproducible example is as follows: x <- cbind(rep(0, 10), rep(0, 10)) y <- rep(0, 10) q <- qr.default(x) qr.coef(q, y) [1] NA NA If x has colnames, then qr.coef will end up with an error: x <- cbind(x1 = rep(0, 10), x2 = rep(0, 10)) y <- rep(0, 10) q <- qr.default(x)