# How to get the intercept from a linear model with lasso (lars R package)

I am having an hard time in getting the model estimated by the R package lars for my data.

For example I create a fake dataset x and corresponding values y like this:

```x = cbind(runif(100),rnorm(100))
colnames(x) = c("a","b")
y = 0.5 + 3 * x[,1,drop = FALSE]
```

Next I train a model that uses lasso regularization using the lars function:

```m = lars(x,y,type = "lasso", normalize = FALSE, intercept = TRUE)
```

Now I would like to know what is the estimated model (that I know to be: y = 0.5 + 3 * x[,1] + 0 * x[,2])

I am only interested in the coefficients obtained in the last step:

```cf = predict(m, x, s=1, mode = "fraction", type = "coef")\$coef
cf
a b
3 0
```

These are the coefficients that I expect, but I can't find a way to get the intercept (0.5) from m.

I have tried to check the code of predict.lars, where the fit is done as such:

```fit = drop(scale(newx,
object\$meanx, FALSE) %*% t(newbetas)) + object\$mu)
```

I can see that the variables are scaled, and that the mean of y (object\$mu) is used, but I can't find an easy way to obtain the value of the intercept I am looking for. How can I get that?

intercept=T in lars has the effect of centering the x variables and y variable. It doesn't include an explicit intercept term with a coefficient.

That being said, you could do predict(m,data.frame(a=0,b=0),s=2)\$fit to get the predicted value of y when the covariates are 0 (the definition of a traditional intercept)