Description Usage Arguments Value Author(s) Examples
Cross validation function
1 2 3 4 |
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
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 | 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
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