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RLASSO procedure

Performs lasso using iteratively reweighted least-squares (D.A. Murray & P.H.C. Eilers).


PRINT = string token What output to print (estimates, best, crossvalidation, progress, correlation, fitted, monitoring); default best
PLOT = string tokens What graphs to plot (correlation, coefficients); default * i.e. none
TERMS = formula Explanatory model
FACTORIAL = scalar Limit on number of factors/covariates in a model term; default 3
LAMBDA = variate or scalar Values for the parameter lambda; must be set
VALIDATIONMETHOD = string token Which cross-validation method to use (crossvalidation, gcv); default gcv
NCROSSVALIDATIONGROUPS = scalar Number of groups for k-fold cross-validation; default 10
NBOOT = scalar Number of times to bootstrap data to estimate standard errors and confidence limits for fitted values; default 100
SEED = scalar Seed for random numbers to use in cross-validation and then in bootstrapping; default 0
CIPROBABILITY = scalar Probability level for confidence interval for fitted values; default 0.95
MAXCYCLE = scalar Maximum number of iterations for the iterative process
TOLERANCE = variate Contains two values to define the convergence criterion for iterative least-squares and the adjustment to avoid division by zero in the penalty term; default !(0.0001,1e-08)


Y = variates Response variate
BESTLAMBDA = scalars Saves the optimal lambda value from cross-validation
CVSTATISTICS = matrices Saves the cross-validation statistics
RESIDUALS = variates Saves residuals for the optimal LAMBDA
FITTEDVALUES = variates Saves fitted values for the optimal LAMBDA
ESTIMATES = variates Saves parameter estimates for the optimal LAMBDA
SE = variates Saves standard errors of the parameter estimates for the optimal LAMBDA
SEFITTED = variates Saves standard errors of the fitted values, from bootstrapping, for the optimal LAMBDA
LOWER = variates Saves lower confidence limits for the fitted values, from bootstrapping, for the optimal LAMBDA
UPPER = variates Saves upper confidence limits for the fitted values, from bootstrapping, for the optimal LAMBDA


The RLASSO procedure performs L1-penalized regression (lasso) using iteratively reweighted sums of squares. The lasso method minimizes the residual sums of squares subject to the constraint that the sum of the absolute values of the model coefficients is less than a constant or tuning parameter λ.

The response variate is specified by the Y parameter. The model to be fitted is defined by the TERMS option. The FACTORIAL option sets a limit on the number of variates and/or factors in the model terms generated from the TERMS model formula (as in the FIT directive).

Printed output is controlled by the PRINT option, with settings:

estimates to print, for each value of λ, the lasso coefficients their standard errors on the standardized and original scales.
 best prints the lasso estimates for the optimal λ
    crossvalidation to print the cross-validation results, with optimal lambda value,
    progress shows the progress of the k-fold cross-validation,,
 correlation   to print the correlations between the explanatory variables in the TERMS formula,
fitted to print the fitted values for the optimal λ with their standard errors and confidence limits
     monitoring  to print monitoring information during boot strapping.

By default,PRINT=best.

Graphical output is controlled by the PLOT option:

    coefficients plots the standardized coefficient estimates against the shrinkage factor, and correlation, and
    correlation uses the DCORRELATION procedure to produce a graphical representation of the correlation matrix for elements in TERMS.

By default, nothing is plotted.

The LAMBDA option must be set to a variate defining the values to try for the tuning parameter λ. The MAXCYCLE option specifies the number of iterations (default 200). The TOLERANCE option specifies the convergence criterion for the iterative procedure (default 0.0001), and the adjustment to use to avoid division by zero in the penalty term (default 10-8).

The VALIDATIONMETHOD option controls how RLASSO estimates the tuning parameter λ:

    crossvalidation uses k-fold cross-validation where the prediction error is calculated using the mean squared error,
    gcv uses the generalized cross-validation, as specified by Tibshirani (1996).

By default , VALIDATIONMETHOD=gcv.

For k-fold cross-validation the NCROSSVALIDATIONGROUPS option defines the number of subsets to use (default 10). The data are divided into roughly equal-sized subsets and the model is fitted with each subset removed in turn. The mean squared error is calculated for the omitted subset based on the model from fitting the remaining subsets. The value that minimizes the mean prediction error is taken as the optimal λ, and used to get the lasso estimates. The optimal value of λ can be saved by the BESTLAMBDA parameter, and the prediction error values can be saved by the CVSTATISTICS parameter.

RLASSO can use bootstrapping to provide standard errors and lower and upper confidence intervals for the fitted values. The NBOOT option specifies the number of bootstrap samples that are taken, and the CIPROBABILITY option sets the size of the confidence limits.

You can save results from the optimal fit using the RESIDUALS, FITTEDVALUES, ESTIMATES and SE, SEFITTED, LOWER and UPPER parameters. Note that the residuals are the simple residuals, rather than standardized residuals.




Lasso is carried out by using iteratively reweighted least-squares. RLASSO approximates the absolute sum of the coefficients ∑|β| by ∑(β2/|β|), and the penalty term λ∑(β2/|β|) is imposed on the sum of squares of the parameter estimates β. The penalty term is applied to the diagonal elements of the sums-of-squares-and-products matrix by setting the RIDGE option of the TERMS directive. For a given value of λ, the algorithm iterates to find the lasso estimates. The shrinkage factor s is estimated by

s = t / ∑|β(0)|

where ∑|β(0)| is the absolute sum of the full least squares estimates, and t is the absolute sum of the lasso estimates subject to

t ≤ ∑|β(0)|.

The columns of the design matrix in TERMS are standardized. However, estimated coefficients are available for both the standardized and unstandardized data.

Action with RESTRICT

There must be no restrictions.


Hastie, T., Tibshirani, R. & Friedman, J (2009). The Elements of Statistical Learning: Data Mining, Inference, and Prediction. 2nd Edition. Springer, New York.

Tibshirani, R. (1996). Regression shrinkage and selection by lasso. Journal of the Royal Statistical Society, Series B, 58, 267-288.

See also

Procedure: LRIDGE.

Commands for: Regression analysis.


CAPTION   'RLASSO example'; STYLE=meta
" Prostate cancer data examining the correlation between the level of
  prostate-specific antigen and some clinical measures. See Tibshirani (1996),
  Regression and Selection by Lasso, JRSS B, 58, 267-288."
SPLOAD    '%GENDIR%/Examples/RLAS-1.gsh'
SUBSET    [train.eq.2] lcavol,lweight,age,lbph,svi,lcp,gleason,pgg45,lpsa
CALCULATE lambdas = 10**(!(1.8,1.7...-2))
RLASSO    [PRINT=correlation,estimates,cross,best;\
          PLOT=coefficients,correlation; LAMBDA=lambdas;\
          Y=lpsa; BEST=optlambda; ESTIMATES=estimates; SE=se
PRINT     optlambda
PRINT     estimates,se
Updated on October 28, 2020

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