On 4/3/07, Fang, Yongxiang <[EMAIL PROTECTED]> wrote: > Dear Douglas, > > Thanks for your help. > the error message is: > 'Error in eval(expr, envir, enclos) : Object "dx.200" not found'; > > In fact, dx is the design matrix and now in a data frame: dframe > I have checked if dx.200 in dframe. dframe$dx.200 does exist and in correct > form. > > In addition, when the number of columns of dx is smaller than 200, the lme > runs no prolem. > > Cheers > > Yongxiang
Without a *reproducible* example we cannot be of any assistance. > -----Original Message----- > From: [EMAIL PROTECTED] on behalf of Douglas Bates > Sent: Tue 4/3/2007 5:57 PM > To: Fang, Yongxiang > Cc: r-help@stat.math.ethz.ch > Subject: Re: [R] the numimum number of fixed factors lme package can deal with > > On 4/3/07, Fang, Yongxiang <[EMAIL PROTECTED]> wrote: > > > In my study, mixed effects model is required and the number of fixed > > effects is very large. When lme package is employed, a model error is > > displayed once the number of fixed factors in the formula reaches 200. Is > > this the maximum number of fixed factors can be handled by lme package? > > If not, what is possible reason of the error message? > > What error message? You didn't tell use what you did and what > happened. Please read and follow the instructions in the posting > guide - otherwise we will only be able to guess at what the problem > may be. > > There is no specific limit of 200 fixed factors (perhaps you mean 200 > columns in the model matrix for the fixed effects?) in lme. However > there will be limits on the amount of memory available to store the > model matrices and associated structures needed to fit the model. > > The first thing I would suggest is determining why you want to fit a > model with 200 (or possibly more if you really meant 200 fixed > factors) fixed effects. It is rare to want to examine such a large > number of coefficients. Frequently the number of coefficients gets to > this order because you have a factor with a large number of levels, in > which case why not model such a factor with random effects? > > You could also try using the lmer function from the lme4 package > instead of lme to fit a linear mixed model. It is generally more > efficient than lme in both time and storage. > > However, before we can help you much you will need to be much more > specific in your question and provide us with the recommended > background information. > > ______________________________________________ R-help@stat.math.ethz.ch mailing list https://stat.ethz.ch/mailman/listinfo/r-help PLEASE do read the posting guide http://www.R-project.org/posting-guide.html and provide commented, minimal, self-contained, reproducible code.