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, mv, is.ftest, partC, MME.method, test.method, shuffle.method, pool.pval, ngenes) meanvarlog(df) ## S3 method for class 'consensus.hc': plot(x, title, ...) ## S3 method for class 'consensus.kmean': plot(x, ...) ## S3 method for class 'madata': print(x, ...) ## S3 method for class 'summary.mamodel': print(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) ## S3 method for class 'madata': summary(object, ...) ## S3 method for class 'mamodel': summary(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) ma.svd(x, nu=min(n,p), nv=min(n,p), method=c("dgesvd","dgesdd")) fdr(p, method = c("stepup", "adaptive", "stepdown", 'jsFDR'))
Some funtion descriptions are:
Hao Wu; Hyuna Yang, hyuna.yang@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