spikeslab (version 1.1.6)

sparsePC.spikeslab: Multiclass Prediction using Spike and Slab Regression

Description

Variable selection for the multiclass gene prediction problem.

Usage

sparsePC.spikeslab(x = NULL, y = NULL, n.rep = 10,
  n.iter1 = 150, n.iter2 = 100, n.prcmp = 5, max.genes = 100,
  ntree = 1000, nodesize = 1, verbose = TRUE, ...)

Arguments

x

x matrix of gene expressions.

y

Class labels.

n.rep

Number of Monte Carlo replicates.

n.iter1

Number of burn-in Gibbs sampled values (i.e., discarded values).

n.iter2

Number of Gibbs sampled values, following burn-in.

n.prcmp

Number of principal components.

max.genes

Maximum number of genes in final model.

ntree

Number of trees used by random forests.

nodesize

Nodesize of trees.

verbose

If TRUE, verbose output is sent to the terminal.

...

Further arguments passed to or from other methods.

Value

Invisibly, the final set of selected genes as well as the complete set of genes selected over the n.rep Monte Carlo replications. The random forest classifier is also returned.

The misclassification error rate, error rate for each class, and other summary information are output to the terminal.

Details

Multiclass prediction using a hybrid combination of spike and slab linear regression and random forest multiclass prediction (Ishwaran and Rao, 2009). A pseudo y-vector of response values is calculated using each of the top n.prcmp principal components of the x-gene expression matrix. The generalized elastic net obtained from using spike and slab regression is used to select genes; one regression fit is used for each of the pseduo y-response vectors. The final combined set of genes are passed to random forests and used to construct a multiclass forest predictor. This procedure is repeated n.rep times with each Monte Carlo replicate based on balanced cross-validation with 2/3rds of the data used for training and 1/3rd used for testing.

---> Miscellanea:

Test set error is only computed when n.rep is larger than 1. If n.rep=1 the full data is used without any cross-validation.

References

Ishwaran H. and Rao J.S. (2009). Generalized ridge regression: geometry and computational solutions when p is larger than n.

See Also

spikeslab.

Examples

# NOT RUN {
#------------------------------------------------------------
# Example 1:  leukemia data
#------------------------------------------------------------

data(leukemia, package = "spikeslab")
sparsePC.out <- sparsePC(x = leukemia[, -1], y = leukemia[, 1], n.rep = 3)
rf.obj <- sparsePC.out$rf.obj
varImpPlot(rf.obj)
# }