Michael Fugate <[EMAIL PROTECTED]> writes: > ############## BEGINNING OF CODE ########################### > # a fake dataset to make the bumps with > nn <- 30 # of data points > mm <- 7 # number of support sites for x(s) > # create sites s > ss <- seq(1,10,length=nn) > # create the data y > e1 <- rnorm(nn,sd=0.1) > e2 <- cos(ss/10*2*pi*4)*.2 > yy <- sin(ss/10*2*pi)+e2+e1 > plot(ss,yy) > > # locations of support points > ww <- seq(1-2,10+2,length=mm) > # width of kernel > sdkern <- 2 > > # create the matrix KK > KK <- matrix(NA,ncol=mm,nrow=nn) > for(ii in 1:mm){ > KK[,ii] <- dnorm(ss,mean=ww[ii],sd=sdkern) > } > > # create a dataframe to hold the data > df1 <- data.frame(y=yy,K=KK,sub=1) > df1$sub <- as.factor(df1$sub) > > # now fit a mixed model using lme > a1 <- lme(fixed= y ~ 1, > random= pdIdent(~KK-1), > data=df1,na.action=na.omit) > > # obtain and plot the fitted values > a1p <- as.vector(predict(a1,df1)) > lines(ss,a1p,lty=1)
lme in S-PLUS is older than the one in R, and some things changed. I think you want df1 <- data.frame(y=yy,K=I(KK),sub=1) a1 <- lme(fixed= y ~ 1, random= list(sub=pdIdent(~K-1)), data=df1,na.action=na.omit) lines(ss,predict(a1,df1,1)) (Apparently you can't do a level-0 prediction in a model with only an intercept, which looks like a bit of a bug. Of course, that is just the intercept for all observations, but...) -- O__ ---- Peter Dalgaard Blegdamsvej 3 c/ /'_ --- Dept. of Biostatistics 2200 Cph. N (*) \(*) -- University of Copenhagen Denmark Ph: (+45) 35327918 ~~~~~~~~~~ - ([EMAIL PROTECTED]) FAX: (+45) 35327907 ______________________________________________ [EMAIL PROTECTED] mailing list https://www.stat.math.ethz.ch/mailman/listinfo/r-help