mcov.R 5.75 KB
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#' @include procmod.frame.R
Eric Coissac committed
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#' @include multivariate.R
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#'
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#' @import expm
#'
#' @author Christelle Gonindard-Melodelima
#' @author Eric Coissac
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NULL

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#' Compute the trace of a square matrix.
#'
#'
#' @param X a square matrix
#' @return the trace of X
#'
#' @examples
#'     m = matrix(1:16,nrow=4)
#'     ProcMod:::.Trace(m)
#'
#' @note Internal function do not use.
#'
#' @rdname internal.getPermuteMatrix
#' @author Eric Coissac
#' @author Christelle Gonindard-Melodelima
#'
.Trace = function(X) sum(diag(X))

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#' Compute the variance, covariance matrix of K coordinate matrices.
#'
#' Covariance between two matrices is defined as the sum of the
#' sigular values of the X'Y matrix. All the matrices must have
#' the same number of rows.
#'
#' @param ... the set of matrices
#'
#' @examples
#'    # Build Three matrices of 3 rows.
#'    A <- matrix(1:9,nrow=3)
#'    B <- matrix(10:15,nrow=3)
#'    C <- matrix(20:31,nrow=3)
#'    # compute the variance covariance matrix
#'    mvar(A,B,C)
#'    mvar(A=A,B=B,C=C)
#'
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#' @author Eric Coissac
#' @author Christelle Gonindard-Melodelima
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#' @export
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varls = function(...,permutations = how(nperm = 999)) {
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  Xs <- list(...)
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  if (length(Xs)==1) {
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    x = Xs[[1]]
    if (is.procmod.frame(x))
      Xs=x
    else if (is.pm(x))
      return(x$cov)
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    else
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      Xs=procmod.frame(x)
  }
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  else
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      Xs=as.procmod.frame(Xs)

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  Xnames=names(Xs)
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  Xs <- ortho(Xs)
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  XXs = as.procmod.frame(mapply(tcrossprod, Xs,
                        SIMPLIFY = FALSE))
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  nX = length(Xs)
  N  = nrow(Xs)-1
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  if (! is.null(permutations)) {
    pmatrix = .getPermuteMatrix(perm=permutations,N=nrow(Xs))
    rCovXXs = matrix(0, nrow = nX * 2, ncol = nX * 2)
    pval = matrix(0, nrow = nX * 2, ncol = nX * 2)
    for (i in 1:(2*nX))
      for (j in 1:(2*nX)) {
        if (i %% 2 && j %% 2) {
          rCovXXs[i,j]=.Trace(sqrtm(XXs[[ceiling(i / 2)]] %*% XXs[[ceiling(j/2)]])) / N
          pval[i,j]=-1
        }
        else if (i ==j) {
          vv = numeric(nrow(pmatrix))
          for (k in seq_len(nrow(pmatrix))) {
            d = Xs[[ceiling(i / 2)]][pmatrix[k,],]
            dd = tcrossprod(d)
            vv[k] = Re(.Trace(sqrtm(dd %*% dd)))
          }
          rCovXXs[i,j]=mean(vv) / N
          pval[i,j]=shapiro.test(vv)$p.value
        }
        else if (i <=j) {
          vv = numeric(nrow(pmatrix))
          for (k in seq_len(nrow(pmatrix))) {
            d = Xs[[ceiling(i / 2)]][pmatrix[k,],]
            dd = tcrossprod(d)
            vv[k] = Re(.Trace(sqrtm(dd %*% XXs[[ceiling(j/2)]])))
          }
          rCovXXs[i,j]=mean(vv) / N
          rCovXXs[j,i]=rCovXXs[i,j]
          pval[i,j]=shapiro.test(vv)$p.value
          pval[j,i]=shapiro.test(vv)$p.value
        }
      }
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    CovXXs=rCovXXs
    Xnames = rep(Xnames,rep(2,length(Xnames)))
    Xsuff  = rep("",length(Xnames))
    Xsuff[seq(from=2,to=length(Xnames),by=2)]="r_"
    Xnames = mapply(paste0, Xsuff,Xnames,collapse="")
    colnames(CovXXs)=Xnames
    rownames(CovXXs)=Xnames
    colnames(pval)=Xnames
    rownames(pval)=Xnames

    print(pval)
  }
  else {
    Xx <- rep(1:nX,nX)
    Xy <- rep(1:nX,rep(nX,nX))
    CovXXs <- mapply(function(x,y) .Trace(sqrtm(XXs[[x]] %*% XXs[[y]])),
                     Xx,Xy,
                     SIMPLIFY = TRUE) / N
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    dim(CovXXs)=c(nX,nX)
    colnames(CovXXs)=Xnames
    rownames(CovXXs)=Xnames
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  }

  return(Re(CovXXs))
}


#' Compute the variance, covariance matrix of K coordinate matrices.
#'
#' Covariance between two matrices is defined as the sum of the
#' sigular values of the X'Y matrix. All the matrices must have
#' the same number of rows.
#'
#' @param ... the set of matrices
#'
#' @examples
#'    # Build Three matrices of 3 rows.
#'    A <- matrix(1:9,nrow=3)
#'    B <- matrix(10:15,nrow=3)
#'    C <- matrix(20:31,nrow=3)
#'    # compute the variance covariance matrix
#'    varls2(A,B,C)
#'    varls2(A=A,B=B,C=C)
#'
#' @author Eric Coissac
#' @author Christelle Gonindard-Melodelima
#' @export
varls2 = function(...,permutations = how(nperm = 999)) {
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  Xs <- list(...)
  if (length(Xs)==1) {
    x = Xs[[1]]
    if (is.procmod.frame(x))
      Xs=x
    else if (is.pm(x))
      return(x$cov)
    else
      Xs=procmod.frame(x)
  }
  else
      Xs=as.procmod.frame(Xs)

  Xnames=names(Xs)

  Xs <- ortho(Xs)

  XXs = as.procmod.frame(mapply(tcrossprod, Xs,
                        SIMPLIFY = FALSE))

  nX = length(Xs)
  N  = nrow(Xs)-1

  CovXXs = matrix(0, nrow = nX, ncol = nX)

  for (i in seq_len(nX))
    for (j in i:nX) {
        CovXXs[i,j] = .Trace(sqrtm(XXs[[i]] %*% XXs[[j]]))
      }

  if (! is.null(permutations)) {
    pmatrix = .getPermuteMatrix(perm=permutations,N=nrow(Xs))
    nP = nrow(pmatrix)
    rCovXXs = array(0,dim=c(nX,nX,nP))

    for (k in seq_len(nP)) {
      Xp = Xs[pmatrix[k,],]
      for (i in seq_len(nX)) {
        dd = tcrossprod(Xp[[i]])
        for (j in i:nX)
          rCovXXs[i,j,k] = Re(.Trace(sqrtm(dd %*% XXs[[j]])))
      }
    }

    for (i in seq_len(nX))
      for (j in i:nX)
        CovXXs[i,j] = CovXXs[i,j] - mean(rCovXXs[i,j,])


  }

  for (i in seq_len(nX))
    for (j in i:nX)
      CovXXs[j,i] = CovXXs[i,j]

  CovXXs = CovXXs / N
  colnames(CovXXs)=Xnames
  rownames(CovXXs)=Xnames

  return(Re(CovXXs))
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}

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#' Compute the person correlation matrix of K coordinate matrices
#'
#' @author Eric Coissac
#' @author Christelle Gonindard-Melodelima
#' @export
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corls = function(...,permutations = how(nperm = 999)) {
  cov = varls(...,permutations = permutations)
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  s = sqrt(diag(cov))
  vv= outer(s,s)
  return(cov/vv)
}

#' Compute the person partial correlation matrix of K coordinate matrices
#'
#' @author Eric Coissac
#' @author Christelle Gonindard-Melodelima
#' @export
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corls.partial = function(...,permutations = how(nperm = 999)) {
  C = solve(corls(...,permutations = permutations))
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  D = sqrt(diag(C) %o% diag(C))
  return(C/D)
}