internals

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Internal Functions and Methods

These functions are for internal use and/or for upcoming packages or not yet documented.

Keywords
internal
Usage
BFDR(alpha, P0 = 1, prob.discovery, size) CombinedNames(object, xlab = "x", ylab = "y") MakeNames(x, nmvar = c("X", "I"), force = FALSE, n0 = 1) Mfrow(half = FALSE, quarter = FALSE, mfrow, height = 2.5, width = 1.5, mai = c(0.7, 0.7, 0.6, 0.3), ...) PHATs.pvalue(lfdr.fun = "rvalue", pvalue, p0 = NULL, robust = FALSE, monotonic = FALSE, ...) PHATs.stat(lfdr.fun = "lfdr.hbea", stat = NULL, pvalue = NULL, plot = 0, nulltype = 1, bre = 120, df = 7, ...) PValue(object, get.PValue, alternative = default("Greater", "alternative"), verbose = TRUE, ...) PValueFUN(FUN, alternative, ...) Pbinom(x, size, prob, lower.tail = TRUE, correct = default(TRUE, "Pbinom correct"), correction = if (correct) 1/2 else 0, inclusive = TRUE, verbose = FALSE) RepNames(object, times, unique = TRUE, ...) SFDR(alpha, P0, size, prob.discovery) Seq(from, to, return.na.on.error = FALSE) Slot(object, name) Var(...) are(object, class2) are_null(object) are_prob(object, ...) are_unk(object) as_colmatrix(x) as_rowmatrix(x) assert.are(object, class2, ...) assert.is(object, class2, text = "") assumedNull(object, ...) b.notrobust(object, P0, ...) b.robust(object, P0, ...) binom_BFDR(x, size, alpha, p = numeric(0), n = numeric(0), P0 = 1, max.BFDR = 1, FUN = NULL, conservative = logical(0), correct, BFDR.fun, ...) binom_limit(x, size, p, correct = default(TRUE, "binom_limit correct"), ...) binom_prob(x, size, p, alternative = character(0), correct = TRUE, ...) binom_rBFDR(x, size, alpha, n, correct = default(TRUE, "binom_rBFDR correct"), ...) binom_rprob(x, size, n, FUN = binom_prob, ...) blank.plot(legend, ...) blank_CDF(object, param.name = "no parameter") coercenExpressionSet(from, to.fun, ...) compatible(...) confidence_CDF(object, pvalue.fun, param.name, min_param, max_param, ...) default(object, name, verbose, return.value = object) est2list(x) estimated.BFDR(object, alpha, nfeature, P0 = 1, p = numeric(0), n = numeric(0), ndiscovery.correction = 0, correct, verbose = FALSE, ...) estimated.LFDR(object, monotonic = FALSE, p = numeric(0), save.time = FALSE, verbose = FALSE, ties.method = "random", achieved.BFDR.fun = estimated.BFDR, ...) expected.lfdr(object, call.plot = FALSE, ...) get_other.from.testfun(x, y = NULL, test.fun = t.test, paired = FALSE, opt = "parameter", ...) get_pvalues(x, y = NULL, test.fun = t.test, paired = FALSE, ...) get_stats(x, y = NULL, test.fun = t.test, paired = FALSE, ...) grep_or(x, pattern, fixed = FALSE, exact = FALSE, ind = T, unik = T, ...) grepl_or(x, pattern, fixed = FALSE, exact = FALSE, unik = T, ...) hsm(x, na.rm) indSortAsY(x, y, inter = F, ind = T) isInteger(x) is_any(object, class2) is_err(object) is_error(object) is_nd1class(x) is_nd2class(x) is_unique(object) is_unk(object) is_vide(object) lfdr(object, zz, use.s3, factor = numeric(0), max.lfdr = Inf, ...) lfdr.hbe(stat = NULL, pvalue = NULL, nulltype = 1, bre = 120, df = 7, plot = 0, ...) lfdr.hbea(stat = NULL, pvalue = NULL, nulltype = 1, bre = 120, df = 7, plot = 0, ...) lfdr.hbee(stat = NULL, pvalue = NULL, nulltype = 1, bre = 120, df = 7, plot = 0, ...) list2est(x, n.object = NULL) list2matrix(x) loccov(N, N0, p0, d, s, x, X, f, JV, Y, i0, H, h, sw) loccov2(X, X0, i0, f, ests, N) locfdr(zz, bre = 120, df = 7, pct = 0, pct0 = 1/4, nulltype = 1, type = 0, plot = 1, mult, mlests, main = " ", sw = 0) locfdr.rname(nulltype) locmle(z, xlim, Jmle = 35, d = 0, s = 1, ep = 1/1e+05, sw = 0, Cov.in) log_lfdr_se(object, call.plot = FALSE, ...) make_labels(n, nmvar = c("X"), n.ini = 1) matrix2list(x) monotonic.pvalue(object, corrected, uncorrected, ranks = numeric(0), monotonic = TRUE) nCDF(type, ...) nalt(object) ncbind(x, y = NULL, inter = FALSE) ncvalue(object, s3FUN, alternative, ttest.arglis, verbose = TRUE, ...) nempiricalNull(object, nulltype = default(1, "nulltype"), nsilence = 0, silent = NULL, call.browser = FALSE, cvalue.arglis = NULL, verbose = TRUE, max.p0 = 1, ...) new_CDF(object, min_param, max_param, param.name, type) new_alt(object) new_bias.corrected.pvalue(object, uncorrected, ranks = numeric(0)) new_cvalue(pvalue, zz, s3FUN, arglis) new_empiricalNull(PDF, PDF0, CDF0, p0, s3, min_param = default(-Inf, "min_param"), max_param = default(Inf, "max_param"), max.p0 = 1) new_est.lfdr.pvalue(LFDR.hat, p0.hat, pvalue, method = NULL, info = list()) new_est.lfdr.stat(LFDR.hat, p0.hat, stat, method = NULL, info = list()) new_nExpressionSet(x=matrix(0),phenoData=as.data.frame(NULL), featureData=as.data.frame(NULL), annotation = character(0)) new_nxprnSet(phenoData = as.data.frame(NULL), exprs = matrix(0), featureData = as.data.frame(NULL), annotation = character(0)) new_nXprnSet(phenoData = as.data.frame(NULL),exprs = matrix(0), featureData = as.data.frame(NULL),annotation = character(0)) new_ttest(pvalue, stat, df, alternative, level1, level2) nprnSet2matrix(x, y = NULL, paired = FALSE) nrbind(x, y = NULL, inter = FALSE) nsize(x) nttest(x, y, factor.name, level1, level2, alternative = "greater", ...) nunique(x, y = NULL, vip = 1) nx11(height = 2.5, width = 1.5, pointsize = 8, mai = c(0.7, 0.7, 0.6, 0.3), ...) prep.2matrices(x, y = NULL, paired = FALSE, rm.na = T) printGeneric(object, ...) printInvalid(object, ...) print_stats(object, name, ...) probability_CDF(object, param.name = default("parameter", "param.name"), min_param, max_param, ...) pval2stat(x, qFUN = qt, alternative = "greater", ...) removeEQ.from.matrix(x, indx = FALSE) removeNA.from.matrix(x, indx = FALSE) removeRC.from.matrix(x, opt = "NA", indx = FALSE) s3_recursive(object, name, first.call)
sameAsX_names(x = NULL, y = NULL) sameAsY(x = NULL, y = NULL) sameLengths(...) sameXY_names(x = NULL, y = NULL) se.mean(...) silence(z, nsilence, silent = NULL) sorted(object, ...) spline.des(knots, x, ord = 4, derivs = integer(length(x)), outer.ok = FALSE, sparse = FALSE) stat2pval(x, pFUN = pt, alternative = "two.sided", sym.distrib = T, ...) stats(object, ...) string2char(x, ...) t_test_CDF(object, ...) undefAsNA(x) vect2string(x, sep = "", ...) wilkinson.test(x, mu = 0, alternative = "greater")
Aliases
  • internals
  • CDF-class
  • Matrix-class
  • Numeric-class
  • Scalar-class
  • Vector-class
  • alt-class
  • bias.corrected.pvalue-class
  • cvalue-class
  • empiricalNull-class
  • est.lfdr.pvalue-class
  • est.lfdr.stat-class
  • est.lfdr.x-class
  • nExpressionSet-class
  • nXprnSet-class
  • nd1class-class
  • nd2class-class
  • nxprnSetObject-class
  • nxprnSetObjectPair-class
  • nxprnSetObjects-class
  • nxprnSetPair-class
  • nxprnSet-class
  • scalar-class
  • ttest-class
  • Combine-methods
  • Combine,Numeric,Numeric-method
  • Combine,Vector,Vector-method
  • Combine,ttest,ttest-method
  • CorrectLimits-methods
  • CorrectLimits,numeric-method
  • [,Matrix,ANY,ANY-method
  • [,Matrix,ANY,missing-method
  • [,Matrix,missing,ANY-method
  • [<-,Matrix,ANY,missing-method
  • Mean-methods
  • Mean,matrix-method
  • Mean,numeric-method
  • Median-methods
  • Median,matrix-method
  • Median,numeric-method
  • [,Numeric,ANY,missing-method
  • [<-,Numeric,ANY,missing-method
  • coerce,Numeric,numeric-method
  • coerce<-,Numeric,numeric-method
  • Rank-methods
  • Rank,numeric-method
  • Rep-methods
  • Rep,Vector-method
  • Rep,matrix-method
  • Rep,nxprnSet-method
  • Sd-methods
  • Sd,numeric-method
  • as.numeric-methods
  • as.numeric,est.lfdr.x-method
  • [,bias.corrected.pvalue,ANY,missing-method
  • coerce,bias.corrected.pvalue,numeric-method
  • coerce<-,bias.corrected.pvalue,numeric-method
  • coerce-methods
  • coerce,cvalue,numeric-method
  • coerce,nExpressionSet,nXprnSet-method
  • coerce,nExpressionSet,numeric-method
  • coerce,nExpressionSet,nxprnSet-method
  • coerce,nxprnSet,nExpressionSet-method
  • coerce,nxprnSet,numeric-method
  • coerce,nxprnSetPair,nxprnSet-method
  • coerce,ttest,cvalue-method
  • coerce<--methods
  • colnames-methods
  • colnames,ANY-method
  • colnames,nd1class-method
  • colnames<--methods
  • colnames<-,ANY,ANY-method
  • colnames<-,nd1class,nd1class-method
  • [,cvalue,ANY,missing-method
  • length,cvalue-method
  • names,cvalue-method
  • dim-methods
  • dim,nxprnSet-method
  • dim,nxprnSetPair-method
  • dimnames-methods
  • dimnames,nxprnSet-method
  • dimnames,nxprnSetPair-method
  • [,est.lfdr.x,ANY,missing-method
  • length,est.lfdr.x-method
  • names,est.lfdr.x-method
  • names<-,est.lfdr.x,character-method
  • exp-methods
  • exp,nExpressionSet-method
  • exp,nxprnSet-method
  • exp,nxprnSetPair-method
  • is_paired-methods
  • is_paired,nd1class,nd1class-method
  • is_paired,nd1class,nd2class-method
  • is_paired,nd2class,nd1class-method
  • is_paired,nd2class,nd2class-method
  • is_positive-methods
  • is_positive,nd1class-method
  • is_positive,nd2class-method
  • is_prob-methods
  • is_prob,numeric-method
  • is_prob,est.lfdr.pvalue-method
  • is_prob,est.lfdr.stat-method
  • length-methods
  • length,nExpressionSet-method
  • length,nxprnSet-method
  • length,nxprnSetPair-method
  • length,ttest-method
  • logb-methods
  • logb,ANY,ANY-method
  • logb,nExpressionSet,missing-method
  • logb,nXprnSet,missing-method
  • logb,nxprnSet,missing-method
  • logb,nxprnSetPair,missing-method
  • [,nExpressionSet,ANY,ANY-method
  • [,nExpressionSet,ANY,missing-method
  • [,nExpressionSet,missing,ANY-method
  • names,nExpressionSet-method
  • nannotation,nExpressionSet-method
  • nexprs,nExpressionSet-method
  • nexprs<-,nExpressionSet,matrix-method
  • nfData,nExpressionSet-method
  • nfeatureNames,nExpressionSet-method
  • npData,nExpressionSet-method
  • nxprnSubset,nExpressionSet-method
  • print,nExpressionSet-method
  • nMatrix-methods
  • nMatrix,matrix-method
  • nMatrix,numeric-method
  • nNumeric-methods
  • nNumeric,numeric-method
  • nScalar-methods
  • nScalar,ANY-method
  • new_nxprnSetPair,nXprnSet,nXprnSet,missing-method
  • nexprs,nXprnSet-method
  • print,nXprnSet-method
  • names-methods
  • names,nxprnSet-method
  • names,nxprnSetPair-method
  • names,ttest-method
  • names<--methods
  • nannotation-methods
  • nannotation,nxprnSet-method
  • nannotation,nxprnSetPair-method
  • ncol-methods
  • ncol,ANY-method
  • ncol,NULL-method
  • ncol,nd1class-method
  • nrow,nd1class-method
  • rownames,nd1class-method
  • new_nxprnSetPair-methods
  • new_nxprnSetPair,matrix,matrix,missing-method
  • new_nxprnSetPair,nxprnSet,missing,character-method
  • new_nxprnSetPair,nxprnSet,nxprnSet,missing-method
  • nexprs<--methods
  • nexprs<-,nxprnSet,matrix-method
  • nexprs<-,nExpressionSet,numeric-method
  • nexprs<-,nxprnSet,numeric-method
  • nexprs-methods
  • nexprs,nxprnSet-method
  • nexprs,nxprnSetPair-method
  • nfData,nxprnSetPair-method
  • npData,nxprnSetPair-method
  • nfData-methods
  • nfData,nxprnSet-method
  • nfeatureNames-methods
  • nfeatureNames,nxprnSet-method
  • nfeatureNames,nxprnSetObjectPair-method
  • nfeatureNames,nxprnSetPair-method
  • npData-methods
  • npData,nxprnSet-method
  • nrow-methods
  • nrow,ANY-method
  • nrow,NULL-method
  • nscalar-methods
  • nscalar,ANY-method
  • nscalar,numeric-method
  • nscalar,scalar-method
  • [,nxprnSetPair,ANY,missing-method
  • print,nxprnSetPair-method
  • removeMissing,nxprnSetPair-method
  • nxprnSubset-methods
  • nxprnSubset,nxprnSet-method
  • [,nxprnSet,ANY,ANY-method
  • [,nxprnSet,ANY,missing-method
  • [,nxprnSet,missing,ANY-method
  • print,nxprnSet-method
  • removeMissing,nxprnSet-method
  • sample_size,nxprnSet-method
  • print-methods
  • print,ANY-method
  • removeMissing-methods
  • rownames-methods
  • rownames,ANY-method
  • s3-methods
  • s3,ANY,character-method
  • s3,ANY,missing-method
  • sameNames-methods
  • sameNames,ANY-method
  • sample_size-methods
  • sample_size,numeric-method
  • sample_size,matrix-method
  • [,ttest,ANY,missing-method
  • [-methods
  • [<--methods
  • BFDR
  • CombinedNames
  • MakeNames
  • Mfrow
  • PHATs.pvalue
  • PHATs.stat
  • PValue
  • PValueFUN
  • Pbinom
  • RepNames
  • SFDR
  • Seq
  • Slot
  • Var
  • are
  • are_null
  • are_prob
  • are_unk
  • as_colmatrix
  • as_rowmatrix
  • assert.are
  • assert.is
  • assumedNull
  • b.notrobust
  • b.robust
  • binom_BFDR
  • binom_limit
  • binom_prob
  • binom_rBFDR
  • binom_rprob
  • blank.plot
  • blank_CDF
  • coercenExpressionSet
  • compatible
  • confidence_CDF
  • default
  • est2list
  • estimated.BFDR
  • estimated.LFDR
  • expected.lfdr
  • get_other.from.testfun
  • get_pvalues
  • get_stats
  • grep_or
  • grepl_or
  • hsm
  • indSortAsY
  • isInteger
  • is_any
  • is_err
  • is_error
  • is_nd1class
  • is_nd2class
  • is_unique
  • is_unk
  • is_vide
  • lfdr
  • lfdr.hbe
  • lfdr.hbea
  • lfdr.hbee
  • list2est
  • list2matrix
  • loccov
  • loccov2
  • locfdr
  • locfdr.rname
  • locmle
  • log_lfdr_se
  • make_labels
  • matrix2list
  • monotonic.pvalue
  • nCDF
  • nalt
  • ncbind
  • ncvalue
  • nempiricalNull
  • new_CDF
  • new_alt
  • new_bias.corrected.pvalue
  • new_cvalue
  • new_empiricalNull
  • new_est.lfdr.pvalue
  • new_est.lfdr.stat
  • new_nExpressionSet
  • new_nxprnSet
  • new_nXprnSet
  • new_ttest
  • nprnSet2matrix
  • nrbind
  • nsize
  • nttest
  • nunique
  • nx11
  • prep.2matrices
  • printGeneric
  • printInvalid
  • print_stats
  • probability_CDF
  • pval2stat
  • removeEQ.from.matrix
  • removeNA.from.matrix
  • removeRC.from.matrix
  • s3_recursive
  • sameAsX_names
  • sameAsY
  • sameLengths
  • sameXY_names
  • se.mean
  • silence
  • sorted
  • spline.des
  • stat2pval
  • stats
  • string2char
  • t_test_CDF
  • undefAsNA
  • vect2string
  • wilkinson.test
  • is_positive
  • CorrectLimits
  • is_prob
  • is_paired
  • s3
  • Rank
  • sameNames
  • Mean
  • Median
  • Sd
  • nNumeric
  • nscalar
  • nScalar
  • nMatrix
  • nexprs
  • nfeatureNames
  • npData
  • nfData
  • nannotation
  • nexprs<-
  • nxprnSubset
  • new_nxprnSetPair
  • removeMissing
  • Rep
  • sample_size
  • Combine
Documentation reproduced from package PsiHat, version 1.0, License: GPL-3

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