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Hello, Consider this function for generalized ridge regression: gre <- function (X,y,D){ n <- dim(X)[1] p <- dim(X)[2] intercept <- rep(1, n) X <- cbind(intercept, X) X2D <- crossprod(X,X)+ D Xy <- crossprod(X,y) bth <- qr.solve(X2D, Xy) } # suppose X is an (nxp) design matrix and y is an (nx1) response vector p <- dim(x)[2] D<- diag(rep(1.5,p)) bt

I haven't find any helpful thread. How can i calculate BIC and Hosmer-Lemeshow statistic for a logistic regression model. I have used glm for logistic fit. -- View this message in context: http://www.nabble.com/BIC-and-Hosmer-Lemeshow-statistic-for-logistic-regression-tf3945943.html#a11193273 Sent from the R help mailing list archive at Nabble.com.

Hi users, I need to calculate .632 (and .632+) bootstrap and the cross-validation of area under curve (AUC) to compare my models. Is there any package for the same. I know about 'ipred' and using it i can calculate misclassification errors. Please help. It's urgent. -- View this message in context:

Dear R-users, Recently, I am facing some problems when converting mlbench data into matrix format. library(mlbench) data(BostonHousing) X<- BostonHousing[,1:13] y<-BostonHousing[,14] I want to convert X and y into matrix form. I am getting these obvious errors... > t(X)%*%y Error in t(X) %*% y : requires numeric/complex matrix/vector arguments > t(as.matrix(X))%*%(as.matrix(y))

I have downloaded latest version of R2HTML (v1.54) for 64-bit windows PC. My R version 2.5.0. My problem arises when i want to install SciViews-R which need R2HTML package. > library(R2HTML) Error in `parent.env<-`(`*tmp*`, value = NULL) : use of NULL environment is defunct Error: package/namespace load failed for 'R2HTML' Any remedy ? Regards -- View this message in

Qusetion #1 ********* Model selection in GAM can be done by using: 1. step.gam {gam} : A directional stepwise search 2. gam {mgcv} : Smoothness estimation using GCV or UBRE/AIC criterion Suppose my model starts with a additive model (linear part + spline part). Using gam() {mgcv} i got estimated degrees of freedom(edf) for the smoothing splines. Now I want to use the functional form of my model

Dear R-users, Suppose I want to modify and use internal functions of an R-package as my requirement. By any way is it possible to explore the internal coding structure of a package and get a list of internal functions? thanks. -- View this message in context: http://www.nabble.com/R-package-question-tp24554613p24554613.html Sent from the R help mailing list archive at Nabble.com.

Dear all, I considered an ordinary ridge regression problem. I followed three different ways: 1. estimate beta without any standardization 2. estimate standardized beta (standardizing X and y) and then again convert back 3. estimate beta using lm.ridge() function X<-matrix(c(1,2,9,3,2,4,7,2,3,5,9,1),4,3) y<-t(as.matrix(cbind(2,3,4,5))) n<-nrow(X) p<-ncol(X) #Without

Hi all, I need some solution in the following problem. The following error appears when i use "mgcv" package for implementing GAM. But the same formula works fine in "gam" package. > model.gam <- gam(formula = RES ~ > CAT01+s(NUM01,5)+CAT02+CAT03+s(NUM02,5)+CAT04+ + CAT05+s(NUM03,5)+CAT06+CAT07+s(NUM04,5)+CAT08+s(NUM05,5)+CAT09+ +

Dear all, I am using modified LARS algorithm (ref: The Adaptive Lasso and Its Oracle Properties, Zou 2006) for adaptive lasso penalized linear regression. 1. w(j) <- |beta_ols(j)|^(-gamma) gamma>0 and j = 1,...,p 2. define x_new(j) <- x(j)*w(j) 3. apply LARS to solve modified lasso problem out.adalasso <- lars(X_new,y,type="lasso") or enet(X_new,