Thanks for your post. Yes, your example is indeed similar to my question. If i means group, j means individual(subject) h:indicator(0:control;1:experiment) k:repeat(if no repeat then k=1) the the model is also X_hijk = alpha_h + h * b_i + r_(ij) + e_hijk.
After I posted this question, I found out how to do it in R. So I would like to share with you guys and hear the comments from you. X:response,b_i subject effect, r(ij) nested effect within subject lme(X~alpha_h,data=dataset,random=list(subject=~h-1,r=~1),method="ML ",na.action="na.omit") the fixed effect part is alpha_h the random effect is subject effect, the corresponding coefficient is h and -1 means no random intercept of subject. and random effect of r(nested effect within subject) Thanks for your help 引用 Peter Muhlberger <[EMAIL PROTECTED]>: > Hi Chung Cheng: This seems related to a problem I'm having in > some data of > mine as well. I'm new to R (played w/ it some a year ago) & to > lme > modeling, so take this w/ a grain of salt, but here are some > thoughts: > > In my problem, D would be an indicator of whether a subject was > in the > control condition or not. In the control condition, all people > participated > individually, in the experimental condition there was small-group > based > discussion. r(ij) would be some variable that affects the > outcome, but > whose effect may be moderated by the group the discussion was in. > > The model assumes that the non-control condition values will have > a > distribution of coefficients for r(ij). The coefficient for > r(ij) in the > controls need not have the same central value as for the > non-controls > (though it would be nice to be able constrain it so it would be). > So, it > might make some sense to split the variable into two variables, > one with > zeros for the controls & one w/ zeros for the experimental groups > and > estimate the former w/ random effects & the other not. > > I'm not 100% sure that's what you're asking, but it seems > related. > > Peter > > >Dear all, > > > >I have a somewhat unusual linear mixed model that I can't seem > >to code in lme. It's only unusual in that one random effect is > >applied only to some of the observations (I have an indicator > >variable > >that specifies which observations have this random effect). > > > >The model is: > > > >X_hijk = alpha_h + h * b_i + r_(ij) + e_hijk , where > > > > h = 0 or 1 (indicator) > > i = 1, ..., N > > j = 1, ..., n_i > > k = 1, ..., K > >alpha is fixed, and the rest are random. > >I'm willing to assume b, r, and e are mutually independent > >and normal with var(b) = sigma^2_b, var(r) = sigma^2_r, and > >var(e) = sigma^2. > > > >Any help in writing this model in lme() would be greatly > >appreciated. > > > >Thanks, > > > >Chung Cheng > ______________________________________________ [email protected] mailing list https://stat.ethz.ch/mailman/listinfo/r-help PLEASE do read the posting guide! http://www.R-project.org/posting-guide.html
