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# Translating the S-Plus/R Least Angle Regression package to Mata - PowerPoint PPT Presentation

Translating the S-Plus/R Least Angle Regression package to Mata. Adrian Mander MRC-Human Nutrition Research Unit, Cambridge. Outline. LARS package Lasso (the constrained OLS) Forward Stagewise regression Least Angle Regression Translating Hastie & Efron’s code from R to Mata

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### Translating the S-Plus/R Least Angle Regression package to Mata

MRC-Human Nutrition Research Unit, Cambridge

SUG London 2007 Least Angle Regression

Outline Mata

• LARS package

• Lasso (the constrained OLS)

• Forward Stagewise regression

• Least Angle Regression

• Translating Hastie & Efron’s code from R to Mata

• The lars Stata command

SUG London 2007 Least Angle Regression

Lasso Mata

Let y be the dependent variable and xj be the m covariates

The usual linear predictor

Want to minimise the squared differences

Subject to this constraint, large t gives OLS solution

N.B. Ridge regression does constraint on L2 norm

SUG London 2007 Least Angle Regression

• The constraints can be seen below.

One property of this constraint is that there will be coefficients =0 for a subset of variables

SUG London 2007 Least Angle Regression

Ridge Regression Mata

• The constraints can be seen below.

The coefficients are shrunk but does not have the property of parsimony

SUG London 2007 Least Angle Regression

• Using constraints

• The function of current correlations is

• Move the mean in the direction of the greatest correlation for some small ε

• FORWARD STEPWISE is greedy and selects

SUG London 2007 Least Angle Regression

• The LARS (S suggesting LaSso and Stagewise)

• Starts like classic Forward Selection

• Find predictor xj1 most correlated with the current residual

• Make a step (epsilon) large enough until another predictor xj2 has as much correlation with the current residual

• LARS – now step in the direction equiangular between two predictors until xj3 earns its way into the “correlated set”

SUG London 2007 Least Angle Regression

• Two covariates x1 and x2 and the space L(x1 ,x2) that is spanned by them

x2

x2

• Start at μ0 =0

• y2 is the projection of y onto L(x1 ,x2)

y2

y1

μ0

μ1

x1

SUG London 2007 Least Angle Regression

Continued… Mata

The current correlations only depend on the projection of y on L(x1 ,x2) I.e. y2

SUG London 2007 Least Angle Regression

The code comparing Splus to Mata looks incredibly similar

SUG London 2007 Least Angle Regression

There are some differences though

Array of arrays… beta[[k]] = array

Indexing on the left hand side…

beta[positive] = beta0

Being able to “join” null matrices.

Row and column vectors are not very strict in Splus.

beta[-positive]

“Local”-ness of mata functions within mata functions? Local is from the first call of Mata

Not the easiest language to debug when you don’t know what you are doing (thanks to statalist/Kit to push start me).

SUG London 2007 Least Angle Regression

Stata command Mata

LARS is very simple to use

lars y <varlist>, a(lar)

lars y <varlist>, a(lasso)

lars y <varlist>, a(stagewise)

Not everything in the Splus package is implemented because I didn’t have all the data required to test all the code

SUG London 2007 Least Angle Regression

Stata command Mata

SUG London 2007 Least Angle Regression

Graph output Mata

SUG London 2007 Least Angle Regression

Conclusions Mata

• Mata could be a little easier to use

• Translating Splus code is pretty simple

• Least Angle Regression/Lasso/Forward Stagewise are all very attractive algorithms and certainly an improvement over Stepwise.

SUG London 2007 Least Angle Regression