a4Base (version 1.18.0)

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)
}