maanova-internal {maanova} | R Documentation |
Internal maanova functions. These are generally not to be called by the user.
JS(X, var) JSshrinker(X, df, meanlog, varlog) buildtree(ct, binstr, depth, parent, idx.node, idx.leave) calPval(fstar, fobs, pool) calVolcanoXval(matestobj) caldf(model, term) check.confounding(model, term1, term2) checkContrast(model, term, Contrast) cluster2num(clust) consensus.hc(macluster, level, draw) consensus.kmean(macluster, level, draw) dist.cor(x) findgroup(varid, ndye) getPval.volcano(matestobj, method, idx) glowess(object, method, f, iter, degree, draw) intprod(terms, intterm) linlog(object, cg, cr, draw) linlog.engine(data, cutoff) linlogshift(object, lolim, uplim, cg, cr, n.bin, draw) locateTerm(labels, term) make.ratio(object, norm.std=TRUE) makeAB(ct, coord, treeidx, startx, maxdepth) makeCompMat(n) makeD(s20, dimZ) makeDesign(design) makeHq(s20, y, X, Z, Zi, ZiZi, dim, b, method) makeShuffleGroup(sample.mtx, ndye, narray) makeZiZi(Z, dimZ) makelevel(model, term) matest.engine(anovaobj, term, mv, test.method, Contrast, is.ftest, partC, verbose=FALSE) matest.perm(n.perm, FobsObj, data, model, term, Contrast, inits20, mv, is.ftest, partC, MME.method, test.method, shuffle.method, pool.pval) meanvarlog(df) plot.consensus.hc(x, title, ...) plot.consensus.kmean(x, ...) print.maanova(x, ...) print.madata(x, ...) print.summary.mamodel(x, ...) ratioVarplot(logsum, logdiff, n) rlowess(object, method, grow, gcol, f, iter, degree, draw) shift(object, lolim, uplim, draw) shuffle.maanova(data, model, term) solveMME(s20, dim, XX, XZ, ZZ, a) summary.madata(object, ...) summary.mamodel(object, ...) volcano.ftest(matestobj, threshold, method, title,highlight.flag) volcano.ttest(matestobj, threshold, method, title,highlight.flag, onScreen) matsort(mat, index=1) repmat(mat, n.row, n.col, ...) zeros(dim) ones(dim) blkdiag(...) rowmax(x) rowmin(x) colmax(x) colmin(x) sumrow(x) matrank(X) norm(X) mixed(y, X, Z, XX, XZ, ZZ, Zi, ZiZi, dimZ, s20, method = c("noest", "MINQE-I", "MINQE-UI", "ML", "REML"), maxiter = 100) parseformula(formula, random, covariate) makeContrast(model, term) pinv(X, tol) fdr(p, method = c("stepup", "adaptive", "stepdown"))
Some funtion descriptions are:
Hao Wu, hao@jax.org
# for matsort a<-matrix(c(1,6,4,3,5,2),2,3) matsort(a,1) matsort(a,2) # for ones and zeros ones(c(2,2)) zeros(c(2,3,2)) # for repmat a<-c(1,2) repmat(a,2,1) a<-matrix(1:4,2,2) repmat(a,1,2) # for blkdiag a<-matrix(1:4,2,2) b<-matrix(3:6,2,2) blkdiag(a,b) blkdiag(a,b,c(1,2)) # others examples are omitted