search for: dropkin

...Schoenfeld 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

...g #is there a value of phi which makes this work? f<-function(t) (Dpq/(2*t) + F3q + Mp/2*log(2*pi*t) - m3$gcv)^2 optimise(f,interval=c(0.5,1.5))$min->phix Dpq/(2*phix) + F3q + Mp/2*log(2*pi*phix) m3$gcv #but what is phix - not the Pearson estimate or the scale returned by gam? thanks Greg Dropkin

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

...rk? > f<-function(t) (Dpq/(2*t) + F3q + Mp/2*log(2*pi*t) - m3$gcv)^2 > optimise(f,interval=c(0.5,1.5))$min->phix > > Dpq/(2*phix) + F3q + Mp/2*log(2*pi*phix) > m3$gcv > #but what is phix - not the Pearson estimate or the scale returned by gam? > > thanks > > Greg Dropkin > > > -- Simon Wood, Mathematical Science, University of Bath BA2 7AY UK +44 (0)1225 386603 http://people.bath.ac.uk/sw283

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

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