similar to: question on using "gl1ce" from "lasso2" package

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'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 = binomial(link=logit), bound = 0.5 ) or >omit <- c(2,3) >test.gl1ce

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.

Hello, I am trying to shrink the coefficients of a logistic regression for a sparse dataset, I am using the lasso (lasso2) and I am trying to determine the shrinkinage factor by cross-validation. I would like please some of the experts here to tell me whether i'm doing it correctly or not. Below is my dataset and the functions I use w= a b c d e P A 0 0 0 0 0 1 879 1 0 0 0 0 1 3 0 1 0 0 0 7 7

Greetings R experts, I'm having some difficulty recovering lda objects that I've saved within sublists using the save.image() function. I am running a script that exports a variety of different information as a list, included within that list is an lda object. I then take that list and create a list of that with all the different replications I've run. Unfortunately I've been

Hi list, One of my co-workers found this problem with 'cor' in his code and I confirm it too (see below). He's using R 2.2.1 under Win 2K and I'm using R 2.3.0 under Win XP. =========================================== > R.Version() $platform [1] "i386-pc-mingw32" $arch [1] "i386" $os [1] "mingw32" $system [1] "i386, mingw32" $status

Hi! The kernlab function kpca() mentions that new observations can be transformed by using predict. Theres also an example in the documentation, but as you can see i am getting an error there (As i do with my own data). I'm not sure whats wrong at the moment. I haven't any predict functions written by myself in the workspace either. I've tested it with using the matrix version and the

Hello, I'll use part of the iris dataset for an example of what I want to do. > data(iris) > iris<-iris[1:10,1:4] > iris Sepal.Length Sepal.Width Petal.Length Petal.Width 1 5.1 3.5 1.4 0.2 2 4.9 3.0 1.4 0.2 3 4.7 3.2 1.3 0.2 4 4.6 3.1 1.5

Dear all, I have a script about iteration classification, like this below data(iris) N <- 5 ir.tr.iter <- vector('list',N) ir.tr <- vector('list',N) for (j in 1:N) { ir.tr[[j]] <- rpart(Species ~., data=iris) ir.tr.iter[j] <- ir.tr[[j]]$frame result <- list(ir.tr=ir.tr, ir.tr.iter=ir.tr.iter) } as.data.frame(as.matrix(ir.tr.iter))

Dear all What is the easy way to drop a variable by using its name (and not its number)? Example: > data(iris) > head(iris) Sepal.Length Sepal.Width Petal.Length Petal.Width Species 1 5.1 3.5 1.4 0.2 setosa 2 4.9 3.0 1.4 0.2 setosa 3 4.7 3.2 1.3 0.2 setosa 4 4.6 3.1

I am making a battery of levelplots and wireframes for several fitted models. I wrote a function that takes the fitted model object as the sole argument and produces these plots. Various strange behavior ensued, but I have identified one very concrete issue (illustrated below): when my figure-drawing function includes the addition of points/lines to trellis plots, some of the

Dear all The trouble with the current duplicated() function in is that it can report duplicates while searching fromFirst _or_ fromLast, but not both ways. Often users will want to identify and extract all the copies of the item that has duplicates, not only the duplicates themselves. To take the example from the man page: > data(iris) > iris[duplicated(iris), ] ##duplicates while

Hello, I am puzzled by the behavior of hist() when generating multiple plots per page on the pdf device. In the following example two pdf files are generated. The first results in 4 plots on one pdf page as expected. However, the second, which swaps one of the plot() calls for hist(), results in a 4 page pdf with one plot per page. How might I get the histogram with 3 other scatter

Hi, [I sent this to the plyr mailing list (late) last night, but it seems to be lost in the moderation queue, so here's a shot to the broadeR community] Apologies in advance for being more verbose than necessary, but I'm not even sure how to ask this question in the context of plyr, so ... here goes. As meaningless as this might be to do with the `iris` data, the spirit of it is what

Hi Dieter and R community: I tried both of these three versions with ylim as suggested, none work: I am getting only single (pch = 16) not overlayed (pch =3) everytime. *vs 1* require(lattice) xyplot(Sepal.Length ~ Sepal.Width | Species , data= iris, panel= function(x, y, subscripts) { panel.xyplot(x, y, pch=16, col = "green4", ylim = c(0, 10)) panel.lmline(x, y, lty=4, col =

Dear all I have a simple problem which I thought is easy to solve but what I tried did not work. I want to change character variables to factor in data frame. It goes easily from factor to character, but I am stuck in how to do backwards conversion. Here is an example irisf<-iris irisf[,2]<-factor(irisf[,2]) # create second factor str(irisf) 'data.frame': 150 obs. of 5

In using cor(data.frame), it is annoying that you have to explicitly filter out non-numeric columns, and when you don't, the error message is misleading: > cor(iris) Error in cor(iris) : missing observations in cov/cor In addition: Warning message: In cor(iris) : NAs introduced by coercion It would be nicer if stats:::cor() did the equivalent *itself* of the following for a data.frame:

I've just tried to merge 2 data sets thinking they would only keep the common columns, but noticed the column count was not adding up. I've then replicated a simple example and got the same thing happening. q1. why doesn't 'b' have a column name? q2. when I merge, why does the new column 'y' have all values as 5.1? Thanks in advance, Mr. confused > a <-

I'm looking for something in R to fit a multivariate linear model robustly, using an M-estimator or any of the myriad of other robust methods for linear models implemented in robustbase or methods based on MCD or MVE covariance estimation (package rrcov). E.g., one can fit an mlm for the iris data as: iris.mod <- lm(cbind(Sepal.Length, Sepal.Width, Petal.Length, Petal.Width) ~ Species,