a4 (version 1.20.0)

lassoReg: Multiple regression using the Lasso algorithm as implemented in the glmnet package

Description

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.

Usage

lassoReg(object, covariate)

Arguments

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.

Value

• object of class glmnet

References

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

See Also

lassoClass

Examples

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)
}