| funCV {simulatorZ} | R Documentation |
Cross validation function
funCV(obj, fold, y.var, trainFun = masomenos, funCvSubset = cvSubsets,
covar = NULL)
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 |
returns the c statistics of cross validation(CV)
Yuqing Zhang, Christoph Bernau, Levi Waldron
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