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, 
                 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'))

_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

        *  ma.svd: function to compute the sigular-value decomposition 
           of a rectangular matrix by using LAPACK routines DEGSVD AND
           ZGESVD.

        *  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

