CODEWITHSUNDEEP
rglmnet
itthrill
itthrill

Reputation: 1376

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
>

Upvotes: 0

Views: 3443

Answers (2)

Maurits Evers
Maurits Evers

Reputation: 50678

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.

Upvotes: 1

Felipe Alvarenga
Felipe Alvarenga

Reputation: 2652

In coef the parameter s corresponds to the penalty parameter. In the help files:

s Value(s) of the penalty parameter lambda at which predictions are required. Default is the entire sequence used to create the model.

[...]

With exact=TRUE, these different values of s are merged (and sorted) with object$lambda, and the model is refit before predictions are made. In this case, it is required to supply the original data x= and y= as additional named arguments to predict() or coef(). The workhorse predict.glmnet() needs to update the model, and so needs the data used to create it. The same is true of weights, offset, penalty.factor, lower.limits, upper.limits if these were used in the original call. Failure to do so will result in an error.

Therefore, to use exact = T you must assign your original penalties, x, y and any other parameter you inputted in your original model

Upvotes: 0

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