gnsc.cv: A function to cross-validate the Group Nearest Shrunken...
In smart: Sparse Multivariate Analysis via Rank Transformation

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

A function to cross-validate the Group Nearest Shrunken Centroid Classifier produced by gnsc.train

1	gnsc.cv(fit, x, y = NULL, z = NULL, nfold = NULL, folds = NULL, verbose = T)

`fit`	The result of a call to gnsc.train
`x`	The test data matrix (variables in the rows, samples in the columns).
`y`	The test class labels for samples, must have the same length as the column length of `x`.
`z`	The test class labels for variables, must have the same length as the row length of `x`.
`nfold`	Number of cross-validation folds. The default value is the smallest class size.
`folds`	The fold lables for each sample, must have the same length as `y` and `max(folds)=nfold`. The default value is `sample(1:nfold,n,replace=T)`, here `n` is the sample size.
`verbose`	If `verbose = FALSE`, tracing information printing is disabled. The default value is `TRUE`.

gnsc.cv carries out a cross-validation for Group Nearest Shrunken Centroid Classifier.

An object with S3 class "gnsccv" is returned:

`lambda`	A vector of the thresholds tried in the shrinkage
`nlambda`	The number of thresholds tried in the shrinkage
`lambda.min`	The index of the threshold which achieves the lowest cross-validation error
`errors`	The number of cross-validation errors for each threshold value
`nonzero`	The number of variables that survived the thresholding
`Thresh.mat`	A list of estimated `tilde{mu}_{mk}`. See Yang, et.al (2012) for details

Fang Han, Han Liu
Maintainer: Fang Han<fhan@jhsph.edu>

1.Juemin Yang, Fang Han, Rafa Irizarry, and Han Liu. Gene Context Analysis on Large-scale Genomic Data. Technical Report, Johns Hopkins University, 2012
2.Robert Tibshirani, Trevor Hastie, Balasubramanian Narasimhan, and Gilbert Chu. Diagnosis of multiple cancer types by shrunken centroids of gene expression PNAS, 99: 6567-6572.

gnsc.train

set.seed(120)
x <- matrix(rnorm(1000*20),ncol=20)
y <- sample(c(1:4),size=20,replace=TRUE)
z <- sample(c(1:10),size=1000,replace=TRUE)
fit=gnsc.train(x, col.struc=y, row.struc=z,lambda.max=5, nlambda=20)
fit
plot(fit)
fit.cv=gnsc.cv(fit,x,y,z)
fit.cv
plot(fit.cv)