lassoReg: Multiple regression using the Lasso algorithm as implemented...
In a4Base: Automated Affymetrix Array Analysis Base Package

Description Usage Arguments Value Author(s) References See Also Examples

Multiple regression using the Lasso algorithm as implemented in the glmnet package. This is a theoretically nice approach to see which combination of genes predict best a continuous response. Empirical evidence that this actually works with high-dimensional data is however scarce.

1	lassoReg(object, covariate)

`object`	object containing the expression measurements; currently the only method supported is one for ExpressionSet objects
`covariate`	character string indicating the column containing the continuous covariate.

object of class glmnet

Willem Talloen

Goehlmann, H. and W. Talloen (2009). Gene Expression Studies Using Affymetrix Microarrays, Chapman \& Hall/CRC, pp. 211.

lassoClass

if (require(ALL)){
 data(ALL, package = "ALL")
 ALL <- addGeneInfo(ALL)
 ALL$BTtype <- as.factor(substr(ALL$BT,0,1))
 resultLasso <- lassoReg(object = ALL[1:100,], covariate = "age")
 plot(resultLasso, label = TRUE,
	   main = "Lasso coefficients in relation to degree of penalization.")
  featResultLasso <- topTable(resultLasso, n = 15)
}