similar to: LARS error

Hi, I am writing to seek some guidance regarding using Lasso regression with the R package LARS. I have introductory statistics background but I am trying to learn more. Right now I am trying to duplicate the results in a paper for shRNA prediction "An accurate and interpretable model for siRNA efficacy prediction, Jean-Philippe Vert et. al, Bioinformatics" for a Bioinformatics project

Hello, I have a question about the lars package. I am using this package to get the coefficients at a specific LASSO parameter s. data(diabetes) attach(diabetes) object <- lars(x,y,type="lasso") cvres<-cv.lars(x,y,K=10,fraction = seq(from = 0, to = 1, length = 100)) fits <- predict.lars(object, type="coefficients", s=0.1, mode="fraction") Can I assign

Hi, I know how to use LASSO for model selection based on the Cp criterion. I heard that we can also use cross validation as a criterion too. I used cv.lars to give me the lowest predicted error & fraction. But I'm short of a step to arrive at the number of variables to be included in the final model. How do we do that? Is it the predict.lars function? i tried >

I am trying to use the lars package in R to carry out lasso analysis. However, I am having some problems. Please could you help me with the following questions: 1) Exactly what format do x and y need to be in for cv.lars(x, y) and lars (x, y)? And what information do x and y need to contain exactly? I have tried using to test just a simple matrix of numeric values for x and a simple vector of

Hi! When I apply the lars (least-angle-regression) method to my data (3655 features, only 355 data points, no I did not mistype), I observe a strange behaviour: 1) The beta values tend to grow into real high values quite fast up to a point where they overflow and get negative. The overflow is not a problem, I don't need the last part of the analysis anyway, but why do they just

Hi.., is there a function in R to fit bivariate splines ? I came across 'polymars' (POLSPLINE) and 'mars' (mda) packages. Are these the one to use or are there other specific commands? Thanks. Harsh

Hi, I am trying to do a lasso regression using the lars package with the following data (see attached): FastestTime WinPercentage PlacePercentage ShowPercentage BreakAverage FinishAverage Time7Average Time3Average Finish 116.90 0.14 0.14 0.29 4.43 3.29 117.56 117.77 5.00 116.23 0.29 0.43 0.14 6.14 2.14 116.84 116.80 2.00 116.41 0.00 0.14 0.29 5.71 3.71 117.24

Dear List, I'm running simulations using the glmnet package. I need to use an 'automated' method for model selection at each iteration of the simulation. The cv.glmnet function in the same package is handy for that purpose. However, in my simulation I have p >> N, and in some cases the selected model from cv.glmet is essentially shrinking all coefficients to zero. In this case,

hello, I have the following questions with the R package lars(). I would really appreciate som help here. 1. do I have to standardize the predictors and the response? 2. the function cv.lars(): how can I specify it is for lasso not stagewise or lars? your help will be really appreciated. thanks,

Hi,   I'm using lars package to run some regression analysis and my doubt now is how can I predict my model to another dataset? Let me explain a little better: I have a dataset from which I withhold some data. With the data that wasn't withheld, I create the model. Now, what I'm not being able to do is apply the model back to the data that I withheld. Any suggestions?   Here it goes

Dear List, Given, y <- matrix(c(0,1,1,1,0,0,0,4,4), ncol = 3, byrow = TRUE) rownames(y) <- c("a","b","c") colnames(y) <- c("1","2","3") y y2 <- y[2:3, ] rownames(y2) <- c("x","z") y2 how can I stop merge(y, y2, all = TRUE, sort = FALSE) squishing the extra rows? Ideally I want the same as: rbind(y,

Hi, I installed package 'lars' earlier tonight and did not get any sort of error message. ### ** building package indices ... ** testing if installed package can be loaded Loaded lars 0.9-8 * DONE (lars) ### However, when I try to load it, I get ### > library(lars, lib.loc = "~/R/x86_64-redhat-linux-gnu-library/2.11") Error : .onLoad failed in loadNamespace() for

Hi.. I have to invert a 15000 x 15000 matrix (generalized inverse). I do run the process on a fairly powerful computer. but still complains indufficient memory. Is there a way one can invert a large matrix in some other efficient manner. Thanks Harsh --------------------------------- [[alternative HTML version deleted]]

We have been using Least Angle Regression (lars) to help identify predictors in models where the outcome is continuous. To do so we have been relying on the lars package. Theoretically, it should be possible to use the lars procedure within a general linear model (glm) framework - we are particular interested in a logistic regression model. Does anyone have examples of using lars with logistic

Hi here is the code as example lars is in package lars > x<-matrix(rnorm(20*5,0,1),20,5) > bs<-matrix(sample(seq(1:10),5),5,1) > er<-rnorm(20,0,1) > y<-x%*%bs+er > lobj<-lars(x,y,type="lasso") > names(lobj) [1] "call" "type" "df" "lambda" "R2" [6] "RSS"

hello I tried to use lars() but neither with my own data nor with the sample data it works. I get in both cases the following error prompt: > data(diabetes) > par(mfrow=c(2,2)) > attach(diabetes) > x<-lars(x,y) Error in one %*% x : requires numeric matrix/vector arguments > x<-lars(x,y, type="lasso") Error in one %*% x : requires numeric matrix/vector arguments

Hello, I have a question about lars package, probably basic. The returned values of lars function include R squares along the variable selection path. However, such values are always slightly different from the R squares returned by the regression function lm using the same models. Anyone know the reasons? Very important, and needs quick answers. Thanks a million! -- View this message in

Hi, I'm having trouble running 'lars'. When I install it I get the following warning: >install.packages('lars') Warning in install.packages("lars") : argument 'lib' is missing: using 'C:\Users\Anna\Documents/R/win-library/2.10' --- Please select a CRAN mirror for use in this session --- trying URL

I am trying to extract coefficients from lars fit and can't find how to get intercept. E.g. y = rnorm(10) x = matrix(runif(50),nrow=10) X = data.frame(y,x) fit1 = lars(as.matrix(X[,2:6]),as.matrix(X[,1])) fit2 = lm(y~.,data=X) Then, if I do: > predict(fit1,s=1,mode='fraction',type='coefficients')$coef X1 X2 X3 X4 X5 0.3447570

Hi, I am trying to use lars on my data. After getting the model and did the prediction, I compare the predicted value and the coefficients and it seems like there should be a constant term in the model. I have gone through the documentation of the lars package and I can't seem to find how to get this piece of information from the lars object. Can somebody help me? Thanks a lot! Mark