Answers interpolated below. On Thu, 2005-06-09 at 10:48 -0700, fhong@salk.edu wrote: > Thanks, Ben. That helps a lot! But I still have some questions? Would you > please also help me on this. > > > There were significant changes in the structure of the PLMset object > > between 1.2.x and 1.3.x which is why you are having problems with the > > boxplot(), Mbox() commands on your old PLMset using the new code. > But why when I reload in to R 2.0.1 ( the on ei used to generate PLMset > object), and tried boxplot ( suppose to produce NUSE plot), it gave me > something strange (see attachment) try something like boxplot(Pset,ylim=c(0.9, 1.2)) though I am not really too sure why you have such extreme outliers on your plot. > > > > Also, if you can live without the weights (or alternatively the > > residuals) you could do > > > > Pset <- fitPLM(my.Data,output.param=list(varcov="none",weights=FALSE)) > > > > > > or > > > > Pset <- fitPLM(my.Data,output.param=list(varcov="none",residuals=FALSE)) > > > > which would also reduce the memory overhead. > Will those simplificaiton change the underlying model that is fitted to > the data. e.g., weights=FALSE doesn't this mean it won't use iteratively > reweighted least squares (IRLS)? No the fitting procedure will be unchanged, ie it still uses IRLS. All it means is that the weights aren't kept around after they have been used. Otherwise given that there is a weight for every PM probe a lot of memory gets used up.

Thanks! >> > There were significant changes in the structure of the PLMset object >> > between 1.2.x and 1.3.x which is why you are having problems with the >> > boxplot(), Mbox() commands on your old PLMset using the new code. >> But why when I reload in to R 2.0.1 ( the on ei used to generate PLMset >> object), and tried boxplot ( suppose to produce NUSE plot), it gave me >> something strange (see attachment) > > try something like > > boxplot(Pset,ylim=c(0.9, 1.2)) Does this mean for version 1.3.3, you set up a ylim for both boxplots (RLE and NUSE). The new RLE and NUSE plots I got from version 1.3.3 look different from the ones from version 1.2.5, the extremem outliers didn't show up in the former. > though I am not really too sure why you have such extreme outliers on > your plot. Very strange. Maybe due to a very bad chip ? >> > Also, if you can live without the weights (or alternatively the >> > residuals) you could do >> > >> > Pset <- fitPLM(my.Data,output.param=list(varcov="none",weights=FALSE)) >> > >> > >> > or >> > >> > Pset <- >> fitPLM(my.Data,output.param=list(varcov="none",residuals=FALSE)) >> > >> > which would also reduce the memory overhead. >> Will those simplificaiton change the underlying model that is fitted to >> the data. e.g., weights=FALSE doesn't this mean it won't use iteratively >> reweighted least squares (IRLS)? > > No the fitting procedure will be unchanged, ie it still uses IRLS. All > it means is that the weights aren't kept around after they have been > used. Otherwise given that there is a weight for every PM probe a lot of > memory gets used up. Make sense, thanks! Bests; Fangxin > > -------------------- Fangxin Hong Ph.D. Plant Biology Laboratory The Salk Institute 10010 N. Torrey Pines Rd. La Jolla, CA 92037 E-mail: fhong@salk.edu (Phone): 858-453-4100 ext 1105