similar to: Cross-validation for logistic regression with lasso2

X is a matrix and F is a vector. F2 <- data.frame(cbind(X,F)) F2 V1 V2 V3 F 1 -0.250536332 -1.4755883 1.9580974 -2.136487 2 -0.009856084 0.4953269 0.5486092 -2.744482 3 -0.406962682 0.7729631 0.1861905 -2.891821 4 1.938780097 0.7469251 1.2537781 -1.212992 5 -0.332370358 1.1943637 0.7114278 -1.830441 modF<-formula(F ~ V1 + V2 + V3) #no error message

Hi, I tried several settings by using the "family=gaussian" in "gl1ce", but none of them works. For the case "glm" can work. Here is the error message I got: > glm(Petal.Width~Sepal.Length+Sepal.Width+Petal.Length ,data=iris,family=gaussian()) > gl1ce(Petal.Width~Sepal.Length+Sepal.Width+Petal.Length ,data=iris,family=gaussian()) Error in eval(expr, envir,

Full_Name: Grant Izmirlian Version: 2.2.0 OS: SuSe Linux version 9.2 Submission from: (NULL) (156.40.34.177) Sorry about the last submission, my bug-fix had an error in it because ifelse doesn't vectorize. I'll repost with the correct bug-fix. ------------------------------------------------------------------------------- The option exists to include all parameters, including the

Dear all, I am quite puzzled about the bound and absolute.t arguments to the l1ce function in the lasso2 package. (The l1ce function estimates the regression parameter b in a regression model y=Xb+e subject to the constraint that |b|<t for some value t). The doc says: bound numeric, either a single number or a vector: the constraint(s) that is/are put onto the L1 norm of the parameters.

I have a model with 3 fixed factors (type, stress, MorD) and two significant two-way interactions (type*stress, stress*MorD). x$summary # Estimate Std.Error DF t.value pvals ci950 ci990 ci999 #(Intercept) 241.738 8.757 994 27.606 0e+00 TRUE TRUE TRUE #typePsPr -26.516 5.905 994 -4.490 1e-05 TRUE TRUE TRUE #stressPN -21.820