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...ld residuals #but schresc1 and schresc differ schresc schresc1 #schresc is schresc1 offset by the parameter estimates beta<-as.vector(f1$coef) nbeta<-outer(rep(1,n),beta) nbeta schresc-nbeta schresc1 #is there a reason for the offset #or am I missing something? thanks Greg Greg Dropkin gregd at gn.apc.org

I have a quick question regarding the use of identify to interact with points on a scatterplot. My question is essentially: can identify be used when one is plotting model objects to generate diagnostic plots? Specifically I am using NLME. For example, I am plotting the fitted values on the x axis vs a variable called log2game with the following code: plot(D2C29.nlme, log2game ~ fitted(.),

Hi This is the 2nd time I am posting this question as I never got a reply the 1st time round - apologies to anybody who might take offense at this but I dont know what else to do. I am struggling to split up the plots of the grouped objects in nlme in a usable way. The standard plot command generates plots for each group on a single page. When there are many groups however this does not look

re [R] matrix^(-1/2) re the discussion in November on this thread. I don't know about expm but the problem must be equivalent to solve(B^(1/2)) and a solution will exist iff B is invertible and has a square root A with A%*%A = B. For 2x2 matrices necessary and sufficient conditions for B to have a square root are that either B = diag(0,2) or B%*%B != diag(0,2). This follows from the fact that

re: Cubic splines in package "mgcv" I don't have access to Gu (2002) but clearly the function R(x,z) defined on p126 of Simon Wood's book is piecewise quartic, not piecewise cubic. Like Kunio Takezawa (below) I was puzzled by the word "cubic" on p126. As Simon Wood writes, this basis is not actually used by mgcv when specifying bs="cr". Maybe the point is

If I only run dfs on my samba server, do I still need to worry about the mangle=hash buffer overlow and set "mangle method=hash2" on my older versions of samba? Thank-you, Greg

I have two problems with Dfs that I'm trying to resolve. I'm using samba-2.2.8ap-1 for Dfs and all links point to netapp filer shares. 1. Files not present When a user runs a script, he gets file not found. The script must access the root of the share, then it can access the file. It is not consistent. For example dir \\server\dfs\projects\test\test.txt -> file not found dir

Hi I have found some postings referring to the fact that one can try and understand why a particular model is failing to solve/converge from the verbose output one can generate when fitting a nonlinear mixed model. I am trying to understand this output and have not been able to find out much: **Iteration 1 LME step: Loglik: -237.4517 , nlm iterations: 22 reStruct parameters: subjectno1

Hi I am following the model building strategy that is outlined in the Pinheiro and Bates book wrt including covariates but am having a problem with the plot. Basically I am using 4 covariates (1 of them is continuous) and 3 of them are fine but the 4th one is being shown as a scatterplot despite the fact that it is a factor. I have explicitly declared this to be a factor (pcat<-as.factor(pcat))

hi I'm puzzled as to the relation between the REML score computed by gam and the formula (4) on p.4 here: http://opus.bath.ac.uk/22707/1/Wood_JRSSB_2011_73_1_3.pdf I'm ok with this for poisson, or for quasipoisson when phi=1. However, when phi differs from 1, I'm stuck. #simulate some data library(mgcv) set.seed(1) x1<-runif(500) x2<-rnorm(500)

hello I'm learning mgcv and would like to obtain numerical output corresponding to plot.gam. I can do so when seWithMean=FALSE (the default) but only approximately when seWithMean=TRUE. Can anyone show how to obtain the exact values? Alternatively, can you clarify the explanation in the manual "Note that, if seWithMean=TRUE, the confidence bands include the uncertainty about the

hi Using a ts or tprs basis, I expected gcv to decrease when increasing the basis dimension, as I thought this would minimise gcv over a larger subspace. But gcv increased. Here's an example. thanks for any comments. greg #simulate some data set.seed(0) x1<-runif(500) x2<-rnorm(500) x3<-rpois(500,3) d<-runif(500) linp<--1+x1+0.5*x2+0.3*exp(-2*d)*sin(10*d)*x3