OK, I'm using William Browne's MLPowSim to create an R script which will simulate samples for estimation of sample size in mixed models. I have subjects nested in hospitals with hospitals treated as random and all of my covariates at level 1. My outcome is death, so it's binary and I'll have a fixed and random intercept. My interest is in the relation of the covariates to the outcome. My most important variable is gestational age (GA) which my investigators divide thusly: 23-24, 25-26, 27-28, 29-30 and 31-32. I have recoded the dummies for GA in the script according to the MLPowSim instructions to a random multinomial variable: macpred<-rmultinom(n2,1,c(.1031,.1482,.2385,.4404,.0698)) x[,3]<-macpred[1,][l2id] x[,4]<-macpred[2,][l2id] x[,5]<-macpred[3,][l2id] x[,6]<-macpred[4,][l2id]
GA 23-24 is the reference with p=.0698. I started with a structured sampling scheme of 20, 60, 100, 120 and 140 level 2 units. My level 2 units have different sizes. So at 20 I had 5 hospitals with 100 patients, 4 with 280, 3 with 460, 3 with 640, 3 with 820 and 2 with 1000. Thus, at 60 hospitals, I have 15, 12, 9, 9, 9, 6 with the same cell sample sizes. According to the MLPowSim documentation, with small probablities it's possible to have a column of zeroes in the X matrix if there are not many units in the random factor. R will choke on this but MLWin sets the associated fixed effects to 0. When R choked, I increased from 20 to 60 as my minimum as suggested in the MLPowSim documentation. Still no luck. ----- Original Message ----- From: Uwe Ligges <lig...@statistik.tu-dortmund.de> To: r-help@r-project.org Cc: Scott Raynaud <scott.rayn...@yahoo.com> Sent: Wednesday, November 16, 2011 1:01 PM Subject: Re: [R] package installtion On 16.11.2011 17:37, Scott Raynaud wrote: > That might be an option if it weren't my most important predictor. I'm > thinking my best bet is to use MLWin for the estimation since it will > properly set fixed effects > to 0. All my other sample size simulation programs use SAS PROC IML which I >don't have/can't afford. I like R since it's free, but I can't work around >the problem > I'm currently having. Then you really have to describe your problem much better: If you most important predictor is really all zero, then you have a real problem ..... Uwe Ligges > > > ----- Original Message ----- > From: Uwe Ligges<lig...@statistik.tu-dortmund.de> > To: Scott Raynaud<scott.rayn...@yahoo.com> > Cc: "r-help@r-project.org"<r-help@r-project.org> > Sent: Wednesday, November 16, 2011 9:48 AM > Subject: Re: [R] package installtion > > > > On 16.11.2011 16:08, Scott Raynaud wrote: >> All right. I upped my level 2 sample size to 60. My log displays the >> following: >> >> Simulation for sample sizes of 60 macro and unbalanced >>micro units >> Iteration remain= 990 >> Iteration remain= 980 >> There were 27 warnings (use warnings() to see them) >> Error in diag(vcov(fitmodel)) : >> error in evaluating the argument 'x' in selecting a method for function >>'diag': Error in asMethod(object) : matrix is not symmetric [1,2] >> >> Looking at the warnings I see: >> >> 26: glm.fit: algorithm did not converge >> 27: In mer_finalize(ans) : gr cannot be computed at initial par (65) >> >> The first 25 are like 26. So, it seems I'm having the same problem as >> before. Again, if this is due to a column of zeroes in my x matrix, the >> best solution would be to assign zeroes to the fixed effects, but I'm not >> sure if there's a way to do this. > > Why don't you simply delete that variable and hence don't estimate > coefficients for it.... > > Uwe Ligges > > > > >> >> ----- Forwarded Message ----- >> From: Scott Raynaud<scott.rayn...@yahoo.com> >> To: "r-help@r-project.org"<r-help@r-project.org> >> Cc: >> Sent: Wednesday, November 16, 2011 7:28 AM >> Subject: Re: [R] package installtion >> >> Well, I could increase the sample size for my second level in hopes that my >> simulation would run correctly. However, a better solution would be to >> assign values of 0 to the fixed effects for this pass through the >> simulation. I'm such a novice with R that I don't know if that can be >> done. I've looked at the documentation but it's still not clear. >> >> >> ----- Original Message ----- >> From: Uwe Ligges<lig...@statistik.tu-dortmund.de> >> To: Scott Raynaud<scott.rayn...@yahoo.com> >> Cc: "r-help@r-project.org"<r-help@r-project.org> >> Sent: Wednesday, November 16, 2011 2:44 AM >> Subject: Re: [R] package installtion >> >> >> >> On 15.11.2011 21:34, Scott Raynaud wrote: >>> OK, I think I see the problem. Rather than setting method="nAGQ" I need >>> nAGQ=1. Doing so throws the following error: >> >> Congratulations, now you understood what R meant with its message >> "Argument ‘method’ is deprecated." >> >>> "Warning messages: >>> 1: glm.fit: algorithm did not converge >>> 2: In mer_finalize(ans) : gr cannot be computed at initial par (65) >>> Error in diag(vcov(fitmodel)) : >>> error in evaluating the argument 'x' in selecting a method for >>>function 'diag': Error in asMethod(object) : matrix is not symmetric [1,2]" >>> >>> I need some help interpreting and debugging this. One thing that I suspect >>> is that there is a column of zeroes in the design matrix, >> >> So have you not even tried to get rid of that? Oh, come on. >> >> Uwe Ligges >> >> >> >>> but I'm not sure. Any other possibilities here and how can I diagnose? >>> >>> ----- Original Message ----- >>> From: Scott Raynaud<scott.rayn...@yahoo.com> >>> To: "r-help@r-project.org"<r-help@r-project.org> >>> Cc: >>> Sent: Tuesday, November 15, 2011 2:11 PM >>> Subject: Re: package installtion >>> >>> Never mind-I fixed it. >>> >>> My script is throwing the following error: >>> >>> "Error in glmer(formula = modelformula, data = data, family = binomial(link >>> = logit), : >>> Argument ‘method’ is deprecated. >>> Use ‘nAGQ’ to choose AGQ. PQL is not available." >>> >>> I remember hearing somewhere that PQL is no longer available on lme4 but I >>> have AGQ specified. >>> >>> Here's the line that fits my model: >>> >>> (fitmodel<- >>> lmer(modelformula,data,family=binomial(link=logit),method="AGQ")) >>> >>> If I change it to nAGQ I still get an error. >>> >>> Any ideas as to what's going on? >>> >>> ----- Original Message ----- >>> From: Scott Raynaud<scott.rayn...@yahoo.com> >>> To: "r-help@r-project.org"<r-help@r-project.org> >>> Cc: >>> Sent: Tuesday, November 15, 2011 1:50 PM >>> Subject: package installtion >>> >>> I'm getting the following error in a script: "Error: could not find >>> function "lmer." I'm wondering of my lme4 package is installed >>> incorrectly. Can someone tell me the installation procedure? I looked at >>> the support docs but couldn't translate that into anything that would work. >>> >>> ______________________________________________ >>> R-help@r-project.org 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. >> >> ______________________________________________ >> R-help@r-project.org 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. > > > ______________________________________________ > R-help@r-project.org 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. ______________________________________________ R-help@r-project.org 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.
