similar to: Constrained regressions (suggestions welcome)

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

Hi all, I want to know some details about the c routine “lasso” in the functions of “gl1ce()” . However, I have following troubles. First, I can not find the routine in the local directories of this function (or package). Second, if I found the routine, could I call it just like this way, say, fit <- .C("lasso", …,PACKAGE = "lasso2") in my own functions. My system is

Dear all, I was recently contacted by a user about an alledged problem/bug in the latest version of lasso2. After some investigation, we found out that it was a user error which boils down to the following: > x <- matrix(rnorm(200), ncol=2) > var <- "fred" > apply(x, 2, var) Error in get(x, envir, mode, inherits) : variable "fred" of mode "function"

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,

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

Dear List, I am looking for a function that will find the best subset of negative binomial models. I have a large data set with 15 variables that I am interested in. I want an easy way to run all possible models and find a subset of the "best" models that I can then look at in more detail. I have found two functions that seem to provide what I am looking for, but am not sure which

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

I have had problems like this before. Probably there is something important that I don't know about routing. Let me introduce to you "Lasso2", a CentOS 4 www server that has been working perfectly well for years. Now I added a second nw card (eth1), automatically using kudzu. I cannot get this dual nw setup to work. The first nw card (eth0) stopped at once working properly,

Greetings -- I'm programming a data mining system in R for protein structural data. As a seasoned Perl and Python and Ada and ML, et al., programmer, I am severely befuddled by the environment problem, where data is not found in a 3rd level nested function. I did peruse the parent frame not on the search path idea, and came up with a hack which kinda works, also below with the code which

Thanks a lot for help in advance. I am switching from matlab to R and I guess I need some time to get rolling. I was wondering why this code : > fit.0 <- lm( Response ~ 1, data = ds3) > step(fit.0,scope=list(upper=~.,lower=~1),data=ds3) Start: AIC= -32.66 Response ~ 1 Call: lm(formula = Response ~ 1, data = ds3) Coefficients: (Intercept) 1.301 is not working

Hi everyone - I'm trying to do a forward stepwise regression (I've tried both step and stepAIC) inside of a function. I can do it outside the function with no problems (first example in code below). I can also do a backward stepwise regression inside a function (second example), but forward stepwise regression ( third example ) fails with the error: "Error in

Dear list, given this formula: > fmla <- formula(y1 ~ spp1 + spp2 + spp3 + spp5) > fmla[[3]] spp1 + spp2 + spp3 + spp5 is this the intended behaviour of as.character: > as.character(fmla[[3]]) [1] "+" "spp1 + spp2 + spp3" "spp5" ? Where does the extra "+" come from? > as.character(fmla) [1] "~"

Hello, I am trying to "paste" together a formula to use in the mob function of party. This means the formula will be of the form y ~ x1+ ...+xM | z1+..zN. I am doing some preliminary fits of y ~ x1+ ...+xM, then want to add the conditional part of the equation using update(). Here's the test code: var1 <- 1:78 x1 <- paste("x", var1, sep="") f1 <-

Greetings. I've written a convenience function for multicollinearity diagnosis. I'd like to report to the user the formula that is used in a regression. I get output like this: > mcDiagnose(m1) [1] "The following auxiliary models are being estimated and returned in a list:" [1] "`x1` ~ ." formula(fmla)() [1] "`x2` ~ ." I'd like to fill in the period

Hi, I'm trying to compile half precision program for ARM, while it seems LLVM fails to automatically generate fused-multiply-add instructions for c += a * b. I'm wondering whether I did something wrong, if not, is it a missing feature that will be supported later? (I know there're fp16 FMLA intrinsics though) Test programs and outputs, $ clang -O3 -march=armv8.2-a+fp16fml

There are some googlable ways to feed a list of filenames to vim, but I stumble on weird results. With my filelist, I try to do cat list | xargs vim ...to edit the files listed in the file "list". Here's what happens: [root at lasso2 tempdir]# ls -l total 8 -rw-r--r-- 1 root root 0 May 17 18:28 a -rw-r--r-- 1 root root 0 May 17 18:28 b -rw-r--r-- 1 root root 3 May 17

I want use survfit() and basehaz() inside a function, but it doesn't work. Could you take a look at this problem. Thanks for your help. Following is my codes: library(survival) n <- 50 # total sample size nclust <- 5 # number of clusters clusters <- rep(1:nclust,each=n/nclust) beta0 <- c(1,2) set.seed(13) #generate phmm data set Z <- cbind(Z1=sample(0:1,n,replace=TRUE),

Dear R-help, Here's a simple example of nonlinear curve fitting where nls seems to get the answer wrong on a very simple exponential fit (my R version 2.7.2). Look at this code below for a very basic curve fit using nls to fit to (a) a logarithmic and (b) an exponential curve. I did the fits using self-start functions and I compared the results with a more simple fit using a straight lm()

Hi, I have a dataframe ds1.2 - 503 categorial variables and 1 continuous response variables. I ran aregImpute to deal with NA's and got the followig error: > fmla = terms( Response ~ . ,data=ds1.2) > ds.i = aregImpute(fmla,data=ds1.2) Error in matrix(as.double(1), nrow = n, ncol = p, dimnames = list(rnam, : length of dimnames [2] not equal to array extent Could you explain