| PreFilter-class {xps} | R Documentation |
Class PreFilter allows to apply different filters to class ExprTreeSet,
i.e. to the expression level data.frame data.
Objects can be created by calls of the form new("PreFilter", ...).
Alternatively, the contructor PreFilter can be used.
mad:"list" describing parameters for madFilter.cv:"list" describing parameters for cvFilter.variance:"list" describing parameters for varFilter.difference:"list" describing parameters for diffFilter.ratio:"list" describing parameters for ratioFilter.gap:"list" describing parameters for gapFilter.hithreshold:"list" describing parameters for highFilter.lothreshold:"list" describing parameters for lowFilter.quantile:"list" describing parameters for quantileFilter.prescall:"list" describing parameters for callFilter.numfilters:"numeric" giving the number of filters applied.
Class "Filter", directly.
signature(object = "PreFilter"): extracts slot prescall.signature(object = "PreFilter", value = "character"): replaces
slot prescall with character vector c(cutoff, samples, condition).signature(object = "PreFilter"): extracts slot cv.signature(object = "PreFilter", value = "numeric"): replaces
slot cv with numeric vector c(cutoff, trim, epsilon).signature(object = "PreFilter"): extracts slot difference.signature(object = "PreFilter", value = "numeric"): replaces
slot difference with numeric vector c(cutoff, trim, epsilon).signature(object = "PreFilter"): extracts slot gap.signature(object = "PreFilter", value = "numeric"): replaces
slot gap with numeric vector c(cutoff, window, trim, epsilon).signature(object = "PreFilter"): extracts slot hithreshold.signature(object = "PreFilter", value = "character"): replaces
slot hithreshold with character vector c(cutoff, parameter, condition).signature(object = "PreFilter"): extracts slot lothreshold.signature(object = "PreFilter", value = "character"): replaces
slot lothreshold with character vector c(cutoff, parameter, condition).signature(object = "PreFilter"): extracts slot mad.signature(object = "PreFilter", value = "numeric"): replaces
slot mad with numeric vector c(cutoff, epsilon).signature(object = "PreFilter"): extracts slot quantile.signature(object = "PreFilter", value = "numeric"): replaces
slot quantile with numeric vector c(cutoff, loquantile, hiquantile).signature(object = "PreFilter"): extracts slot ratio.signature(object = "PreFilter", value = "numeric"): replaces
slot ratio with numeric vector c(cutoff).signature(object = "PreFilter"): extracts slot variance.signature(object = "PreFilter", value = "numeric"): replaces
slot variance with numeric vector c(cutoff, trim, epsilon).Christian Stratowa
related classes Filter, UniFilter.
## for demonstration purposes only: initialize all pre-filters
prefltr <- new("PreFilter")
madFilter(prefltr) <- c(0.5,0.01)
cvFilter(prefltr) <- c(0.3,0.0,0.01)
varFilter(prefltr) <- c(0.6,0.02,0.01)
diffFilter(prefltr) <- c(2.2,0.0,0.01)
ratioFilter(prefltr) <- c(1.5)
gapFilter(prefltr) <- c(0.3,0.05,0.0,0.01)
lowFilter(prefltr) <- c(4.0,3,"samples")
highFilter(prefltr) <- c(14.5,75.0,"percent")
quantileFilter(prefltr) <- c(3.0, 0.05, 0.95)
callFilter(prefltr) <- c(0.02,80.0,"percent")
str(prefltr)