search for: l1ce

Hi, First, when I try the example Prostate with bound 0.44 (as in the manual), I got a different result: > l1c.P <- l1ce(lpsa ~ ., Prostate, bound=0.44) > l1c.P .... Coefficients: (Intercept) lcavol lweight age lbph svi 1.0435803 0.4740831 0.1953156 0.0000000 0.0000000 0.3758199 lcp gleason pgg45 0.0000000 0.0000000 0.0000000 The relative L1 boun...

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 t...

Dear R-help, I am having trouble with the predict function in lasso2. For example: > data(Iowa) > l1c.I <- l1ce(Yield ~ ., Iowa, bound = 10, absolute.t=TRUE) > predict (l1c.I) # this works is fine > predict (l1c.I,Iowa) Error in eval(exper,envir, enclos) : couldn't find function "Yield" And I have similar trouble whenever I use the newdata argument in prediction. thanks in...

...straint on the parameters. code is: data(Prostate) p.mean <- apply(Prostate, 5,mean) pros <- sweep(Prostate, 5, p.mean, "-") p.std <- apply(pros, 5, var) pros <- sweep(pros, 5, sqrt(p.std),"/") pros[, "lpsa"] <- Prostate[, "lpsa"] l1ce(lpsa ~ . , pros, bound = 0.44) I can't figure out what dose 0.44 come from. On the paper it said it was from generalized cross-validation and it is the optimal choice. paper name: Regression Shrinkage and Selection via the Lasso author: Robert Tibshirani -- View this message in cont...

Hi, I'm using gl1ce with family=binomial like so: >yy succ fail [1,] 76 23 [2,] 32 67 [3,] 56 43 ... [24,] 81 18 >xx c1219 c643 X1 0.04545455 0.64274145 X2 0.17723669 0.90392792 ... X24 0.80629054 0.12239320 >test.gl1ce <- gl1ce(yy ~ xx, family = binomi...

....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 #general least squares model works fine glm.F <- glm(modF, data = F2, family = gaussian) #But there are unresolved errors with gl1ce gl1F<-gl1ce(modF, data=F2, family=gaussian) Error in family(family) : link "family" not available for gaussian family; available links are 'inverse', 'log', 'identity' > gl1F<-gl1ce(modF, data=F2, family=gaussian(link="identity"))...

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(e...

Hi, I am working on a project about hospital efficiency. Due to the high multicolinearlity of the data, I want to fit the model using ridge regression. However, I believe that the data from large hospital(indicated by the number of patients they treat a year) is more accurate than from small hosptials, and I want to put more weight on them. How do I do this with lm.ridge? I know I just need

...ulation ranges I suspect could show different behaviours in the form of statistically different means as per ANOVA analysis. Coming back to my original variable selection need, here's what I did. First : regular lm Second : step Third : all-subsets (regsubsets, package leaps) Fourth : lasso (l1ce, package lasso2) Fifth : (I meant to use the lars package, but it does not allow for formulas; I know I could cast my dataset as matrices, but I didn't find an easy way of doing this and I figured I had enough options already) I'm trying to make sense of the information that is sent back...