# Questions tagged [lars]

LARS stands for Least Angle Regression. It is a penalized estimation and feature selection technique for multiple regression.

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### R - CV.GLMNET LASSO (binomial) variable reduction - highly correlated variables not zeroing out

I have a dataset of about n = 100,000 observations and p = 247 predictors with one binomial dependent variable (values are 0, 1) I run the following code in R: ... 2k views

### lasso - how to evaluate results

I'm working on lasso as an alternative to step-wise forward/backward regression using the lars package in R. I normalized my variables, calculated the ...
54 views

### explaining LARS algorithm in a simple way [duplicate]

I am thinking to write a LARS algorithm that uses different optimizations in each step. Can somebody briefly explain Least angel regression, LARS (see here) to me? then I will try to write my own LARS ...
8k views

### How to interprete lasso from lars correctly?

I tried the lars package with R and got the following result. ...
1 vote
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### how to combine coefficients of a logistic regression model with existing prior knowledge about covariates?

I am working on developing statistical models for fault-localization. on the one hand, i construct a logistic regression model with these considerations: 1-my dependent(response) variable is program ...
7k views

### R - Lasso Regression - different Lambda per regressor

I want to do the following: 1) OLS regression (no penalization term) to get beta coefficients $b_{j}^{*}$; $j$ stands for the variables used to regress. I do this by ...
2k views

### Why is there no intercept in the lars output for LASSO in Stata?

This is my first time using lars, so this question is probably obvious. When I run lars on my data I get an output with a model and coefficients assigned to predictors, but there is no intercept. I ...
11k views

### Exact definition of Deviance measure in glmnet package, with crossvalidation?

For my current reseach I'm using the Lasso method via the glmnet package in R on a binomial dependent variable. In glmnet the optimal lambda is found via cross-validation and the resulting models ...
2k views

### CV for LASSO tuning parameter using LARS

If I use the LARS algorithm to fit the LASSO path, is it sufficient to cross-validate using the values of $\lambda$ at each step in LARS or is it better to use a finer grid of $\lambda$ values? I ...
1 vote
172 views

1 vote