trying to use exact=TRUE feature in R glmnet

I am trying to use exact=TRUE feature in glmnet. But I am getting an error message.

> fit = glmnet(as.matrix(((x_values))), (as.matrix(y_values)),penalty=variable.list$penalty)
> coef.exact = coef(fit, s = 0.03, exact = TRUE)
Error: used coef.glmnet() or predict.glmnet() with `exact=TRUE` so must in addition supply original argument(s)  x and y and penalty.factor  in order to safely rerun glmnet

How can I supply penalty.factor to coef.exact?

Options tried:-

> coef.exact = coef(as.matrix(((x_values))), (as.matrix(y_values)),penalty=variable.list$penalty, s = 0.03, exact = TRUE)
Error: $ operator is invalid for atomic vectors
> 
> coef.exact = coef((as.matrix(((x_values))), (as.matrix(y_values)),penalty=variable.list$penalty), s = 0.03, exact = TRUE)
Error: unexpected ',' in "coef.exact = coef((as.matrix(((x_values))),"
> 
> coef.exact = coef((as.matrix(((x_values))) (as.matrix(y_values)) penalty=variable.list$penalty), s = 0.03, exact = TRUE)
Error: unexpected symbol in "coef.exact = coef((as.matrix(((x_values))) (as.matrix(y_values)) penalty"
> 
> coef.exact = coef(fit(as.matrix(((x_values))), (as.matrix(y_values)),penalty=variable.list$penalty), s = 0.03, exact = TRUE)
Error in fit(as.matrix(((x_values))), (as.matrix(y_values)), penalty = variable.list$penalty) : 
  could not find function "fit"
> 
> coef.exact = coef(glmnet(as.matrix(((x_values))), (as.matrix(y_values)),penalty=variable.list$penalty), s = 0.03, exact = TRUE)
Error: used coef.glmnet() or predict.glmnet() with `exact=TRUE` so must in addition supply original argument(s)  x and y and penalty.factor  in order to safely rerun glmnet
>

Solution

Here is an example using mtcars as sample data. Note it's always advisable to provide a minimal & reproducible code example including sample data when posting on SO.

# Fit mpg ~ wt + disp
x <- as.matrix(mtcars[c("wt", "disp")]);
y <- mtcars[, "mpg"];
fit <- glmnet(x, y, penalty = 0.1); 

# s is our regularisation parameter, and since we want exact results
# for s=0.035, we need to refit the model using the full data (x,y)
coef.exact <- coef(fit, s = 0.035, exact = TRUE, x = x, y = y, penalty.factor = 0.1);
coef.exact;
#3 x 1 sparse Matrix of class "dgCMatrix"
#                      1
#(Intercept) 34.40289989
#wt          -3.00225110
#disp        -0.02016836

The reason why you explicitly need to provide x and y again is given in ?coef.glmnet (also see @FelipeAlvarenga post).

So in your case, the following should work:

fit = glmnet(x = as.matrix(x_values), y = y_values, penalty=variable.list$penalty)
coef.exact = coef(
    fit, 
    s = 0.03, 
    exact = TRUE, 
    x = as.matrix(x_values), 
    y = y_values, 
    penalty.factor = variable.list$penalty)

Some comments

Perhaps the confusion arises from the difference between the model's overall regularisaton parameter (s or lambda) and the penalty.factors that you can apply to every coefficient. The latter allows for differential regularisation of individual parameters, whereas s controls the effect of overall L1/L2 regularisation.