maanova-internal {maanova} | R Documentation

## Internal maanova functions

### Description

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

### Usage

    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'))

### Details

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.

### Author(s)

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

### Examples

    # 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

[Package maanova version 1.10.0 Index]