Fit a linear model with an L1 penalty.


mdl = lassoFit(y, X, lambda)
  • y (Nx1 vector) – The target, or dependent variable.

  • X (NxP matrix) – The model features, or independent variables.

  • lambda (Scalar, or Kx1 vector) – The L1 penalty parameter(s).

  • ctl (struct) –

    Optional input, an instance of a lassoControl structure. An instance named ctl will have the following members:


    Scalar, or vector of L1 penalties. The model will be estimated for each lambda value. If ctl.lambdas is an empty matrix, {}, then lassoFit() will create a vector of decreasing values. Default = {} (empty matrix).


    Scalar, if ctl.lambdas is an empty matrix, ctl.nlambdas controls the number of lambda values in the lambda path created internally. Default=100.


    Scalar, the tolerance for convergence of the coordinant descent optimization for each lambda value. Default = 1e-5.


    Scalar, if a path of lambda values is computed internally, the smallest lambda value will be greater than the value of the largest lambda value multiplied by ctl.lambda_min_ratio. Default = 1e-3.


    The maximum number of iterations for the coordinate descent optimization for each provided lambda. Default = 1000.


mdl (struct) –

An instance of a lassoModel structure. An instance named mdl will have the following members:


(1 x nlambdas vector) The estimated value for the intercept for each provided lambda.


(P x nlambdas matrix) The estimated parameter values for each provided lambda.


(nlambdas x 1 vector) The mean squared error for each set of parameters, computed on the training set.


(nlambdas x 1 vector) The lambda values used in the estimation.


(nlambdas x 1 vector) The degrees of freedom for each estimated model.


Example 1: Basic Estimation and Prediction


library gml;

// Specify dataset with full path
dataset = getGAUSSHome() $+ "pkgs/gml/examples/qsar_fish_toxicity.csv";

// Load dependent and independent variable
y = loadd(dataset, "LC50");
X = loadd(dataset, ". -LC50");

// Split data into training sets
y_test = y[1:636];
X_test = X[1:636,.];
y_train = y[637:rows(y)];
X_train = X[637:rows(X),.];

// Declare 'mdl' to be an instance of a
// lassoModel structure to hold the estimation results
struct lassoModel mdl;

// Estimate the model with default settings
mdl = lassoFit(y_train, X_train);

After the above code, mdl.beta_hat will be a \(6 \times 75\) matrix, where each column contains the estimates for a different lambda value. The graph below shows the path of the parameter values as the value of lambda changes.


Continuing with our example, we can make test predictions like this:

// Make predictions on the test set
y_hat = X_test * mdl.beta_hat + mdl.alpha_hat;

After the above code, y_hat will be a matrix with the same number of observations as y_test. However, it will have one column for each value of lambda used in the estimation. We can compute the mean-squared error (MSE) for each of our predictions with the following code:

// Compute MSE for each prediction
mse_test = meanc((y_test - y_hat).^2);

Below is a plot of the change in MSE with the changes in lambda.



Each variable (column of X) is centered to have a mean of 0 and scaled to have unit length, (i.e. the vector 2-norm of each column of X is equal to 1).

See also