funCV {simulatorZ}R Documentation

funCV

Description

Cross validation function

Usage

funCV(obj, fold, y.var, trainFun = masomenos, funCvSubset = cvSubsets, 
    covar = NULL)

Arguments

obj

a ExpressionSet, matrix or RangedSummarizedExperiment object. If it is a matrix, columns represent samples

fold

the number of folds in cross validation

y.var

response variable, matrix, data.frame(with 2 columns) or Surv object

trainFun

training function, which takes gene expression matrix X and response variable y as input, the coefficients as output

funCvSubset

function to divide one Expression Set into subsets for cross validation

covar

other covariates to be added in as predictors

Value

returns the c statistics of cross validation(CV)

Author(s)

Yuqing Zhang, Christoph Bernau, Levi Waldron

Examples

library(curatedOvarianData)
library(GenomicRanges)
set.seed(8)
data( E.MTAB.386_eset )
eset <- E.MTAB.386_eset[1:100, 1:30]
rm(E.MTAB.386_eset)

time <- eset$days_to_death
cens.chr <- eset$vital_status
cens <- rep(0, length(cens.chr))
cens[cens.chr=="living"] <- 1
y <- Surv(time, cens)  
y1 <- cbind(time, cens)

nrows <- 200; ncols <- 6
counts <- matrix(runif(nrows * ncols, 1, 1e4), nrows)
rowRanges <- GRanges(rep(c("chr1", "chr2"), c(50, 150)),
                     IRanges(floor(runif(200, 1e5, 1e6)), width=100),
                     strand=sample(c("+", "-"), 200, TRUE))
colData <- DataFrame(Treatment=rep(c("ChIP", "Input"), 3),
                     row.names=LETTERS[1:6])
sset <- SummarizedExperiment(assays=SimpleList(counts=counts),
                             rowRanges=rowRanges, colData=colData)
time <- c(1588,1929,1813,1542,1830,1775)  
cens <- c(1,0,1,1,1,1)
y.vars <- Surv(time, cens)

funCV(eset, 3, y)
funCV(exprs(eset), 3, y1)
funCV(sset, 3, y.vars)
## any training function will do as long as it takes the gene expression matrix X
## and response variable y(matrix, data.frame or Surv object) as parameters, and
## return the coefficients as its value

[Package simulatorZ version 1.16.0 Index]