[R] questions on rpart (tree changes when rearrange the order of covariates?!)

ing mass & glucose") text(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 #Klimt(fit3,pmydata) Thanks, ---

[R] bug in rpart?

cose") text(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, -- ----

[R] cv.glmnet

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]] __

[R] rwmetrop

Hi, all: Can anybody check where is wrong with my code? I tried a lot of times, but did not find an error. The parameters' estimator is not accurate. It's a simple model about a multiple regression, with five covariates. rwmetrop is supposed to give a much more accurate estimand. Thanks a lot. rm

[R] Jags problem

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(rh

[R] Cannot evaluate subset expression for sigmainverse

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 }

[R] penalized quantile regression (rq.fit.lasso)

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 = .9