maanova-internal           package:maanova           R Documentation

_I_n_t_e_r_n_a_l _m_a_a_n_o_v_a _f_u_n_c_t_i_o_n_s

_D_e_s_c_r_i_p_t_i_o_n:

     Internal maanova functions. These are generally not to be called
     by the user.

_U_s_a_g_e:

     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, ngenes)
     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"))

_D_e_t_a_i_l_s:

     Some funtion descriptions are:

        *  matsort: Sort matrix in ascending order along specified
           dimension

        *  repmat: Replicate and tile an array

        *  zeros: Create an array with all zeros

        *  ones: Create an array with all ones

        *  blkdiag: Block diagonal concatenation of input arguments

        *  num2yn: convert a logical value to string "Yes" or "No"

        *  rowmax, rowmin, colmax, colmin: find the maximum/minimum
           value for row/columns

        *  sumrow: calculate the sum of rows for a given matrix

        *  matrank: calculate the rank of a matrix

        *  norm: calculate matrix or vector norm, working only for
           vector now

        *  mixed: engine function to solve Mixed Model Equations using
           EM algorithm

        *  parseformula: parse input formula. This is used for mixed
           effect model

        *  makeDesign: function to make a integer list from input
           design object

        *  intpord: function to make the design matrix for interaction
           terms it's working for two way interaction only

        *  makeContrast: function to make the contrast matrix given
           model and the term to be tested number of levels

        *  pinv: calculate the pseudo inverse for a singular matrix.
           Note that I was using ginv function in MASS but it is not
           robust, e.g., sometimes have no result. That's because the
           engine function dsvdc set the maximum number of iteration to
           be 30, which is not enough in some case. I use La.svd
           instead of svd in my function. I don't want to spend time on
           it so it doesn't support complex number

        *  fdr: function to calculate the adjusted P values for FDR
           control.

_A_u_t_h_o_r(_s):

     Hao Wu;  Hyuna Yang, hyuna.yang@jax.org

_E_x_a_m_p_l_e_s:

     # 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

