R/pcalg_included_functions.R

Defines functions nzdiaghungarian nzdiagbruteforce

# The original source for most of this comes from pcalg package
# code in https://CRAN.R-project.org/package=pcalg
# The credit goes to the authors of the package:
# Markus Kalisch, Alain Hauser , Martin Maechler, Diego Colombo,
# Doris Entner, Patrik Hoyer, Antti Hyttinen, Jonas Peters,
# Nicoletta Andri, Emilija Perkovic, Preetam Nandy, Philipp Ruetimann,
# Daniel Stekhoven, Manuel Schuerch, Marco Eigenmann.
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# EVEN IF SUCH HOLDER OR OTHER PARTY HAS BEEN ADVISED OF THE POSSIBILITY OF
# SUCH DAMAGES.
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#
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# above cannot be given local legal effect according to their terms,
# reviewing courts shall apply local law that most closely approximates
# an absolute waiver of all civil liability in connection with the
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#
# changes to these functions by Nicola Gnecco
# 1. addition to estLiNGAM of the parameter 'fun' to allow for different
#    contrast functions, i.e., "logcosh" and "exp", when calling the
#    function fastICA from fastICA package.

estLiNGAM <- function (X, only.perm = FALSE, fastICA.tol = 1e-14, pmax.nz.brute = 8,
                       pmax.slt.brute = 8, verbose = FALSE, fun = c("logcosh","exp")) {
  p <- ncol(X)
  fun <- match.arg(fun)
  icares <- fastICA::fastICA(X, n.comp = p, tol = fastICA.tol, verbose = if (verbose >=
                                                                             1)
    verbose - 1L
    else FALSE, fun = fun)
  W <- t(icares$K %*% icares$W)
  if (verbose)
    cat("Performing row permutation, nzdiag*() ...\n  ")
  nzd <- if (p <= pmax.nz.brute) {
    if (verbose)
      cat("(Small dimensionality, using brute-force method.): ")
    nzdiagbruteforce(W)
  }
  else {
    if (verbose)
      cat("(Using the Hungarian algorithm.): ")
    nzdiaghungarian(W)
  }
  Wp <- nzd$Wopt
  if (verbose)
    cat("Done!\n")
  if (!only.perm)
    estdisturbancestd <- 1/diag(abs(Wp))
  Wp <- Wp/diag(Wp)
  Best <- diag(p) - Wp
  if (!only.perm)
    cest <- Wp %*% colMeans(X)
  if (verbose)
    cat("Performing permutation for causal order...\n  ")
  slt <- if (p <= pmax.slt.brute) {
    if (verbose)
      cat("(Small dimensionality, using brute-force method.): ")
    sltbruteforce(Best)
  }
  else {
    if (verbose)
      cat("(Using pruning algorithm.): ")
    sltprune(Best)
  }
  causalperm <- slt$optperm
  if (verbose)
    cat("Done!\n")
  if (only.perm && !verbose)
    return(list(k = causalperm))
  Bestcausal <- slt$Bopt
  if (verbose)
    print(Bestcausal)
  percentinupper <- sltscore(Bestcausal)/sum(Bestcausal^2)
  if (verbose) {
    if (percentinupper > 0.2)
      cat("WARNING: Causal B not really triangular at all!!\n")
    else if (percentinupper > 0.05)
      cat("WARNING: Causal B only somewhat triangular!\n")
    else cat("Causal B nicely triangular. No problems to report here.\n")
    if (only.perm)
      return(list(k = causalperm))
  }
  Bestcausal[upper.tri(Bestcausal, diag = FALSE)] <- 0
  icausal <- iperm(causalperm)
  list(B = Bestcausal[icausal, icausal], stde = estdisturbancestd,
       ci = cest, k = causalperm, W = W, pinup = percentinupper)
}

nzdiagbruteforce <- function(W) {
  stopifnot((n <- nrow(W)) >= 1)
  allperms <- all.perm(n)
  I.n <- diag(n)
  bestval <- Inf
  besti <- 0
  nperms <- nrow(allperms)
  for (i in 1:nperms) {
    Pr <- I.n[, allperms[i, ]]
    Wtilde <- Pr %*% W
    c <- nzdiagscore(Wtilde)
    if (c < bestval) {
      bestWtilde <- Wtilde
      bestval <- c
      besti <- i
    }
  }
  list(Wopt = bestWtilde, rowp = iperm(allperms[besti, ]))
}

nzdiaghungarian <- function(W) {
  n <- nrow(W)
  S <- matrix(1, n, n)/abs(W)
  c <- as.integer(clue::solve_LSAP(S))
  Pr <- diag(n)[, c]
  Wopt <- Pr %*% W
  list(Wopt = Wopt, rowp = iperm(c))
}

nzdiagscore <- function (W) {
  sum(1/diag(abs(W)))
}

all.perm <- function (n) {
  p <- matrix(1L, 1L, 1L)
  if (n > 1L)
    for (i in 2L:n) {
      p <- pp <- cbind(p, i, deparse.level = 0L)
      ii <- seq_len(i)
      v <- c(ii, seq_len(i - 1L))
      for (j in 2L:i) {
        v <- v[-1L]
        p <- rbind(p, pp[, v[ii]], deparse.level = 0L)
      }
    }
  p
}

iperm <- function (p) {
  sort.list(p, method = "radix")
}

sltbruteforce <- function (B) {
  n <- nrow(B)
  bestval <- Inf
  besti <- 0
  allperms <- all.perm(n)
  nperms <- nrow(allperms)
  for (i in 1:nperms) {
    p.i <- allperms[i, ]
    Btilde <- B[p.i, p.i]
    c <- sltscore(Btilde)
    if (c < bestval) {
      bestBtilde <- Btilde
      bestval <- c
      besti <- i
    }
  }
  list(Bopt = bestBtilde, optperm = allperms[besti, ])
}

sltscore <- function (B) {
  sum((B[upper.tri(B, diag = TRUE)])^2)
}

sltprune <- function (B) {
  n <- nrow(B)
  ind <- sort.list(abs(B))
  for (i in ((n * (n + 1)/2):(n * n))) {
    Bi <- B
    Bi[ind[1:i]] <- 0
    p <- slttestperm(Bi)
    if (any(p != 0)) {
      Bopt <- B[p, p, drop = FALSE]
      break
    }
  }
  list(Bopt = Bopt, optperm = p)
}

slttestperm <- function (B, rowS.tol = 1e-12) {
  stopifnot((n <- nrow(B)) >= 1, rowS.tol >= 0)
  p <- integer(0)
  remnodes <- 1:n
  Brem <- abs(B)
  for (ii in 1:n) {
    rowS <- if (length(Brem) > 1)
      rowSums(Brem)
    else Brem
    therow <- which(rowS < rowS.tol)
    if (length(therow) == 0L)
      return(0L)
    if (ii == n)
      return(c(p, remnodes))
    therow <- therow[1]
    Brem <- Brem[-therow, -therow, drop = FALSE]
    p <- c(p, remnodes[therow])
    remnodes <- remnodes[-therow]
  }
  stop("the program flow should never get here [please report!]")
}

prune <- function (X, k, method = "resampling", prunefactor = 1, npieces = 10,
                   verbose = FALSE) {
  if (verbose)
    cat("Pruning the network connections...\n")
  stopifnot(length(k) == (p <- nrow(X)))
  ndata <- ncol(X)
  X.k <- X[k, ]
  ik <- iperm(k)
  method <- match.arg(method)
  switch(method, resampling = {
    stopifnot(is.numeric(npieces), length(npieces) == 1,
              npieces%%1 == 0, 1 <= npieces, npieces <= ndata)
    piecesize <- floor(ndata/npieces)
    Bpieces <- array(0, c(p, p, npieces))
    diststdpieces <- array(0, c(p, npieces))
    cpieces <- array(0, c(p, npieces))
    Bfinal <- matrix(0, p, p)
    I.p <- diag(p)
    for (i in 1:npieces) {
      Xp <- X.k[, ((i - 1) * piecesize + 1):(i * piecesize)]
      Xpm <- rowMeans(Xp)
      Xp <- Xp - Xpm
      C <- (Xp %*% t(Xp))/ncol(Xp)
      if (TRUE) {
        C <- C + diag(10^(-10), dim(C)[1])
      }
      while (min(eigen(C, only.values = TRUE)$values) <
             0) {
        C <- C + diag(10^(-10), dim(C)[1])
      }
      L <- tridecomp(invsqrtm(C), choice = "ql", only.B = TRUE)
      diag.L <- diag(L)
      newestdisturbancestd <- 1/abs(diag.L)
      L <- L/diag.L
      Bnewest <- I.p - L
      cnewest <- L %*% Xpm
      Bnewest <- Bnewest[ik, ik]
      newestdisturbancestd <- newestdisturbancestd[ik]
      cnewest <- cnewest[ik]
      Bpieces[, , i] <- Bnewest
      diststdpieces[, i] <- newestdisturbancestd
      cpieces[, i] <- cnewest
    }
    for (i in 1:p) {
      Bp.i <- Bpieces[i, , ]
      for (j in 1:p) {
        themean <- mean(Bp.i[j, ])
        thestd <- stats::sd(Bp.i[j, ])
        Bfinal[i, j] <- if (abs(themean) < prunefactor *
                            thestd) 0 else themean
      }
    }
    stde <- rowMeans(diststdpieces)
    cfinal <- rowMeans(cpieces)
  }, olsboot = {
    stop(gettextf("Method '%s' not implemented yet!", method),
         domain = NA)
  }, wald = {
    stop(gettextf("Method '%s' not implemented yet!", method),
         domain = NA)
  }, bonferroni = {
    stop(gettextf("Method '%s' not implemented yet!", method),
         domain = NA)
  }, hochberg = {
    stop(gettextf("Method '%s' not implemented yet!", method),
         domain = NA)
  }, modelfit = {
    stop(gettextf("Method '%s' not implemented yet!", method),
         domain = NA)
  })
  if (verbose)
    cat("Done!\n")
  list(Bpruned = Bfinal, stde = stde, ci = cfinal)
}

tridecomp <- function (W, choice = "qr", only.B = FALSE) {
  stopifnot(1 <= (m <- nrow(W)), 1 <= (n <- ncol(W)))
  Jm <- matrix(0, m, m)
  Jm[m:1, ] <- diag(m)
  Jn <- matrix(0, n, n)
  Jn[n:1, ] <- diag(n)
  switch(choice, qr = {
    r <- qr(W)
    if (only.B) qr.R(r) else list(A = qr.Q(r), B = qr.R(r))
  }, lq = {
    r <- qr(t(W))
    if (only.B) t(qr.Q(r)) else list(A = t(qr.R(r)), B = t(qr.Q(r)))
  }, ql = {
    r <- qr(Jm %*% W %*% Jn)
    if (only.B) Jm %*% qr.R(r) %*% Jn else list(A = Jm %*%
                                                  qr.Q(r) %*% Jm, B = Jm %*% qr.R(r) %*% Jn)
  }, rq = {
    r <- qr(Jn %*% t(W) %*% Jm)
    if (only.B) t(Jn %*% qr.Q(r) %*% Jn) else list(A = t(Jn %*%
                                                           qr.R(r) %*% Jm), B = t(Jn %*% qr.Q(r) %*% Jn))
  }, stop("invalid 'choice': ", choice))
}

invsqrtm <- function (A){
  e <- eigen(A)
  V <- e$vectors
  lam.m.5 <- 1/sqrt(e$values)
  tcrossprod(V * rep(lam.m.5, each = nrow(V)), V)
}
nicolagnecco/causalXtreme documentation built on April 21, 2024, 4:22 a.m.