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PsiHat (version 1.0)

internals: Internal Functions and Methods

Description

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

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")

Arguments