search for: zibi

...%t(datai$Zi))%*%(datai$yi-datai$Xi%*%beta) return(temp1+temp2) } ####################################################################### #functions to evaluate the conditional expection, saved as Efun v2.R #Eh1new=E(bibi') #Eh2new=E(X'(y-Zbi)) #Eh3new=E(bi'Z'Zbi) #Eh4new=E(Y-Xibeta-Zibi)'(Y-Xibeta-Zibi) #Eh5new=E(Xibeta-yi)'Zibi #Eh6new=E(bi) Eh1new=function(datai=data.list[[i]], weights.m=weights.mat) { #one way #temp=matrix(0,2,2) #for (i in 1:nrow(bi)) #{ #temp=temp+bi[i,]%*%t(bi[i,])*weights.m[i,1]*weights.m[i,2] #} #print(temp) #the other way bi=datai$b tempb=bi*...

...t(datai$Zi))%*%(datai$yi-datai$Xi%*%beta) return(temp1+temp2) } ####################################################################### #functions to evaluate the conditional expection, saved as Efun v2.R #Eh1new=E(bibi') #Eh2new=E(X'(y-Zbi)) #Eh3new=E(bi'Z'Zbi) #Eh4new=E(Y-Xibeta-Zibi)'(Y-Xibeta-Zibi) #Eh5new=E(Xibeta-yi)'Zibi #Eh6new=E(bi) Eh1new=function(datai=data.list[[i]], weights.m=weights.mat) { #one way #temp=matrix(0,2,2) #for (i in 1:nrow(bi)) #{ #temp=temp+bi[i,]%*%t(bi[i,])*weights.m[i,1]*weights.m[i,2] #} #print(temp) #the other way bi=dat...

...ear R-users, I have longitudinal data and would like to fit a model where both the variance-covariance matrix of the random effects and the residual variance are conditional on a (binary) grouping variable. I guess the model would have the following form (in hierarchical notation) Yi|bi,k ~ N(XiB+Zibi, sigmak*Ident) bi|k ~ N(0, Dk) K~Bernoulli(p) I can obtain different sigmas (sigma0 and sigma1 based on the factor 'dx') using the weights option in the call to lme: lme(fixed = height ~ -1 + bage + mat_ht + pat_ht + dx + time:dx + time2:dx , weights=varIdent(form=~1|dx...