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...0.6) printcp(fit3) table(predict(fit3,type="class"),pmydata$diabetes) ##after exchage the order of BODY mass and PLASMA glucose neg pos neg 436 64 pos 64 204 #Klimt(fit3,pmydata) Thanks, -------------------------------------------------------------------------------------- Yuanyuan Huang [[alternative HTML version deleted]]

Hi, all: Does anybody know how to avoid the intercept term in cv.glmnet coefficient? When I say "avoid", it does not mean using coef()[-1] to omit the printout of intercept, it means no intercept at all when doing the analysis. Thanks. [[alternative HTML version deleted]]

...xt(fit3,use.n=T,cex=0.6) printcp(fit3) table(predict(fit3,type="class"),pmydata$diabetes) ##after exchage the order of BODY mass and PLASMA glucose neg pos neg 436 64 pos 64 204 Best, -- -------------------------------------------------------------------------------------- Yuanyuan Huang Email: sunnyuan.h at gmail.com -------------- next part -------------- A non-text attachment was scrubbed... Name: ReorderedTree.pdf Type: application/pdf Size: 5984 bytes Desc: not available URL: <https://stat.ethz.ch/pipermail/r-help/attachments/20090522/e4a941c2/attachment-0004.pdf>...

Hi, all: I met "Non-conforming parameters for function %*%" problem, when I run the Jags model in R. My model is like this: model{ for(i in 1:n){ for(j in 1:t[i]){ et[i,j]<-yt[i,j]-beta0+betax*xt[i,j]+betat*t[i,j] } for(a in 1:t[i]){ for(b in 1:t[i]){ sigma[i,a,b]<-pow(rho0,abs(t[a]-t[b])) } } phi[i]<-

Hi, anybody can help me with this? can JAGS solve the inverse of a matrix in the 3-way array? Thank you! for(i in 1:n){ for(a in 1:t[i]){ for(b in 1:t[i]){ sigma[i,a,b]<-pow(rho,t[a]-t[b]) } } sigmainverse[i,,]<-inverse(sigma[i,,]) # this is where jags got error } [[alternative HTML version deleted]]

...practitioners from disparate disciplines, application domains, and perspectives, enabling them to discover and share underlying commonalities in their approaches to making resources, applications, and systems more autonomic. The technical sessions program includes: * Daily Keynote Addresses from Yuanyuan Zhou, University of California, San Diego; Lucy Cherkasova, HP Labs; and Ion Stoica, University of California, Berkeley * Paper presentations on model-driven management and self-adaptation, cloud resource management, network and system management, scheduling and pricing, resource and workload manag...

...practitioners from disparate disciplines, application domains, and perspectives, enabling them to discover and share underlying commonalities in their approaches to making resources, applications, and systems more autonomic. The technical sessions program includes: * Daily Keynote Addresses from Yuanyuan Zhou, University of California, San Diego; Lucy Cherkasova, HP Labs; and Ion Stoica, University of California, Berkeley * Paper presentations on model-driven management and self-adaptation, cloud resource management, network and system management, scheduling and pricing, resource and workload manag...

Dear all: I have a question about how to get the optimal estimate of coefficients using the penalized quantile regression (LASSO penalty in quantile regression defined in Koenker 2005). In R, I found both rq(y ~ x, method="lasso",lambda = 30) and rq.fit.lasso(x, y, tau = 0.5, lambda = 1, beta = .9995, eps = 1e-06) can give the estimates. But, I didn't find a way using either of