distn.r

使用R语言的马尔科夫链蒙特卡洛模拟（MCMC）源代码程序。

########## Density Functions and Random Number Generators ##########
#
# note: Matthew Fasman has been working on replacing these with
#       functions coded in C++.  These have been moved to the tmp
#       directory.  We need to get the R seeding issues worked out
#       before these functions can be replaced.  In addition, they 
#       need to be documented one distribution at a time (see the
#       existing documentation for an example).

##
## Wishart
##

# rwish delivers a pseudo-random Wishart deviate
#
# USAGE:
#
#   A <- rwish(v, S)
#
# INPUT:
#
#   v    degrees of freedom
#
#   S    Scale matrix
#
# OUTPUT:
#
#  A     a pseudo-random Wishart deviate
#
# Based on code originally posted by Bill Venables to S-news
# on 6/11/1998
#
# KQ on 2/5/2001

"rwish" <-
  function(v, S) {
    if (!is.matrix(S))
      S <- matrix(S)
    if (nrow(S) != ncol(S)) {
      stop(message="S not square in rwish().\n")
    }
    if (v < nrow(S)) {
      stop(message="v is less than the dimension of S in rwish().\n")
    }
    p <- nrow(S)
    CC <- chol(S) 
    Z <- matrix(0, p, p)
    diag(Z) <- sqrt(rchisq(p, v:(v-p+1)))
    if(p > 1) {
      pseq <- 1:(p-1)
      Z[rep(p*pseq, pseq) + unlist(lapply(pseq, seq))] <- rnorm(p*(p-1)/2)
    }
    return(crossprod(Z %*% CC))
  }

# dwish evaluations the Wishart pdf at positive definite matrix W.
# note: uses the Gelman, et. al. parameterization.
#
# USAGE:
#
#   x <- dwish(W, v, S)
#
# INPUT:
#
#   W    positive definite matrix at which to evaluate PDF
#
#   v    degrees of freedom
#
#   S    Scale matrix
#
# OUTPUT:
#
#   x    the PDF evaluated (scalar)
#
# ADM 8/16/2002

"dwish" <-
  function(W, v, S) {
    if (!is.matrix(S))
      S <- matrix(S)
    if (nrow(S) != ncol(S)){
      stop(message="W not square in dwish()\n\n")
    }
    if (!is.matrix(W))
      S <- matrix(W)
    if (nrow(W) != ncol(W)){
      stop(message="W not square in dwish()\n\n")
    }   
    if(nrow(S) != ncol(W)){
      stop(message="W and X of different dimensionality in dwish()\n\n")
    }
    if (v < nrow(S)){
      stop(message="v is less than the dimension of S in  dwish()\n\n")
    }    
    k <- nrow(S)
  
    # denominator
    gammapart <- 1
    for(i in 1:k) {
      gammapart <- gammapart * gamma((v + 1 - i)/2)
    } 
    denom <- gammapart *  2^(v * k / 2) * pi^(k*(k-1)/4)
  
    # numerator
    detS <- det(S)
    detW <- det(W)
    hold <- solve(S) %*% W
    tracehold <- sum(hold[row(hold) == col(hold)])  
    num <- detS^(-v/2) * detW^((v - k - 1)/2) * exp(-1/2 * tracehold)

    return(num / denom)
  }

##
## Inverse Wishart
##

# riwish generates a draw from the inverse Wishart distribution
# (using the Wishart generator)  

"riwish" <-
  function(v, S) {
    return(solve(rwish(v,solve(S))))
  }

# diwish evaluates the inverse Wishart pdf at positive definite
# matrix W.  note: uses the Gelman, et. al. parameterization.
#
# USAGE:
#
#   x <- diwish(W, v, S)
#
# INPUT:
#
#   W    positive definite matrix at which to evaluate PDF
#
#   v    degrees of freedom
#
#   S    Scale matrix
#
# OUTPUT:
#
#   x    the PDF evaluated (scalar)
#
# ADM 8/16/2002
 
"diwish" <-
  function(W, v, S) {
    if (!is.matrix(S))
      S <- matrix(S)
    if (nrow(S) != ncol(S)){
      stop("W not square in diwish().\n")
    }
    if (!is.matrix(W))
      S <- matrix(W)
    if (nrow(W) != ncol(W)){
      stop("W not square in diwish().\n")
    }   
    if(nrow(S) != ncol(W)){
      stop("W and X of different dimensionality in diwish().\n")
    }
    if (v < nrow(S)){
      stop("v is less than the dimension of S in  diwish().\n")
    }
    
    k <- nrow(S)   

    # denominator
    gammapart <- 1
    for(i in 1:k) {
      gammapart <- gammapart * gamma((v + 1 - i)/2)
    } 
    denom <- gammapart *  2^(v * k / 2) * pi^(k*(k-1)/4)
  
    # numerator
    detS <- det(S)
    detW <- det(W)
    hold <- S %*% solve(W)
    tracehold <- sum(hold[row(hold) == col(hold)])  
    num <- detS^(v/2) * detW^(-(v + k + 1)/2) * exp(-1/2 * tracehold)

    return(num / denom)
  }

##
## Inverse Gamma
##

## evaluate the inverse gamma density
##
## Kevin Rompala 5/6/2003
## fixed KQ 3/8/2005

"dinvgamma" <-
  function(x, shape, scale = 1) {

    # error checking
    if(shape <= 0 | scale <=0) {
      stop("Shape or scale parameter negative in dinvgamma().\n")
    }
    
    alpha <- shape
    beta <- scale
   
    # done on log scale to allow for large alphas and betas
    log.density <- alpha * log(beta) - lgamma(alpha) -
       (alpha + 1) * log(x) - (beta/x)
    return(exp(log.density))
  }

## generate draws from the inverse gamma density (using
## the gamma simulator)
##
## Kevin Rompala 5/6/2003
## fixed KQ 3/8/2005

"rinvgamma" <-
  function(n, shape, scale = 1) {
    return(1 / rgamma(n, shape, scale))
  }

##
## Dirichlet (Multivariate Beta)
##

# ddirichlet evaluates the density of the Dirichlet at
# vector x given shape parameter vector (or matrix)
# alpha.
#
# note: this code is taken verbatim from the R-package
# "Greg's Miscellaneous Functions" maintained by
# Gregory R. Warnes <[e-mail]>
#
# Kevin Rompala 5/6/2003

"ddirichlet" <-
  function(x, alpha) {

    dirichlet1 <- function(x, alpha) {
      logD <- sum(lgamma(alpha)) - lgamma(sum(alpha))
      s <- sum((alpha-1)*log(x))
      exp(sum(s)-logD)
    }

    # make sure x is a matrix
    if(!is.matrix(x))
      if(is.data.frame(x))
        x <- as.matrix(x)
      else
        x <- t(x)
    if(!is.matrix(alpha))
      alpha <- matrix( alpha, ncol=length(alpha), nrow=nrow(x), byrow=TRUE)

    if( any(dim(x) != dim(alpha)) )
      stop("Mismatch between dimensions of x and alpha in ddirichlet().\n")

    pd <- vector(length=nrow(x))
    for(i in 1:nrow(x))
      pd[i] <- dirichlet1(x[i,],alpha[i,])

    # Enforce 0 <= x[i,j] <= 1, sum(x[i,]) = 1
    pd[ apply( x, 1, function(z) any( z <0 | z > 1)) ] <- 0
    pd[ apply( x, 1, function(z) all.equal(sum( z ),1) !=TRUE) ] <- 0
    return(pd)
  }


# rdirichlet generates n random draws from the Dirichlet at
# vector x given shape parameter vector (or matrix)
# alpha.
#
# note: this code is taken verbatim from the R-package
# "Greg's Miscellaneous Functions" maintained by
# Gregory R. Warnes <[e-mail]>
#
# Kevin Rompala 5/6/2003

"rdirichlet" <-
  function(n, alpha) {
    l <- length(alpha)
    x <- matrix(rgamma(l*n,alpha),ncol=l,byrow=TRUE)
    sm <- x%*%rep(1,l)
    return(x/as.vector(sm))
  }

##
## Non-Central Hypergeometric
##

# code to evaluate the noncentral hypergeometric density (at a single point
# or at all defined points).
#
# parameters:
#
#    n1, n2 -- number of subjects in group 1 and 2
#
#    Y1, Y2 -- number of subjects with positive outcome [unobserved]
#
#    psi -- odds ratio
#
#    m1 -- sum of observed values of Y1 and Y2 (Y1 + Y2)
#   
# output:
#
#   pi -- Pr(Y1 = x | Y1 + Y2 = m1) x=ll,...,uu
#
#   for ll = max(0, m1-n2) and uu = min(n1, m1)
#  
# if x is NA, then a matrix is returned, with the first column the possible
# values of x, and the second columns the probabilities.  if x is a scalar, 
# the density is evaluated at that point.
#
# ADM on 5/8/2003
#
# note: code adapted from R code published in conjunction with:
#
# Liao, J.G. And Rosen, O. (2001) Fast and Stable Algorithms for Computing and 
# Sampling from the Noncentral Hypergeometric Distribution.  The American
# Statistician 55, 366-369.
#

"dnoncenhypergeom" <-
  function (x = NA, n1, n2, m1, psi) {

    ##
    ## AUXILIARY FUNCTIONS
    ##

    mode.compute <- function(n1, n2, m1, psi, ll, uu) {
      a <- psi - 1
      b <- -( (m1+n1+2)*psi + n2-m1 )     
      c <- psi*(n1+1)*(m1+1)
      q <- b + sign(b)*sqrt(b*b-4*a*c)
      q <- -q/2
                         
      mode <- trunc(c/q) 
      if(uu>=mode && mode>=ll) return(mode)
      else return( trunc(q/a) )      
    }

    r.function <- function(n1, n2, m1, psi, i) {
      (n1-i+1)*(m1-i+1)/i/(n2-m1+i)*psi
    }

    ##
    ## MAIN FUNCTION
    ##

    # upper and lower limits for density evaluation
    ll <- max(0, m1-n2)
    uu <- min(n1, m1)

    # check parameters
    if(n1 < 0 | n2 < 0) {
       stop("n1 or n2 negative in dnoncenhypergeom().\n")
    }
    if(m1 < 0 | m1 > (n1 + n2)) {
       stop("m1 out of range in dnoncenhypergeom().\n")
    }
    if(psi <=0) {
       stop("psi [odds ratio] negative in dnoncenhypergeom().\n")
    }
    if(!is.na(x) & (x < ll | x > uu)) {
       stop("x out of bounds in dnoncenhypergeom().\n")
    }
    if(!is.na(x) & length(x) > 1) {
       stop("x neither missing or scalar in dnoncenhypergeom().\n")
    }  
  
    # evaluate density using recursion (from mode)
    mode <- mode.compute(n1, n2, m1, psi, ll, uu)
    pi <- array(1, uu-ll+1)
    shift <- 1-ll
    
    if(mode<uu) { # note the shift of location
      r1 <- r.function( n1, n2, m1, psi, (mode+1):uu )       
      pi[(mode+1 + shift):(uu + shift)] <- cumprod(r1)       
    }
   
    if(mode>ll) {
       r1 <- 1/r.function( n1, n2, m1, psi, mode:(ll+1) )
       pi[(mode-1 + shift):(ll + shift)] <- cumprod(r1)
    }
            
    pi <- pi/sum(pi)
    if(is.na(x)) return(cbind(ll:uu,pi))
    else return(pi[x + shift])
}

# code to generate random deviates from the noncentral hypergeometric density 
#
# parameters:
#
#    n -- the number of draws to make
#
#    n1, n2 -- number of subjects in group 1 and 2
#
#    Y1, Y2 -- number of subjects with positive outcome [unobserved]
#
#    psi -- odds ratio
#
#    m1 -- sum of observed values of Y1 and Y2 (Y1 + Y2)
#   
# output:
#
#   output -- a list of length n of random deviates
#  
#
# ADM on 5/9/2003
#
# note: code adapted from R code published in conjunction with:
#
# Liao, J.G. And Rosen, O. (2001) Fast and Stable Algorithms for Computing and 
# Sampling from the Noncentral Hypergeometric Distribution.  The American
# Statistician 55, 366-369.
#

"rnoncenhypergeom" <-
  function (n, n1, n2, m1, psi) {

    ##
    ## AUXILIARY FUNCTIONS
    ##  

    mode.compute <- function(n1, n2, m1, psi, ll, uu) {
      a <- psi - 1
      b <- -( (m1+n1+2)*psi + n2-m1 )     
      c <- psi*(n1+1)*(m1+1)
      q <- b + sign(b)*sqrt(b*b-4*a*c)
      q <- -q/2
                         
      mode <- trunc(c/q) 
      if(uu>=mode && mode>=ll) return(mode)
      else return( trunc(q/a) )
      
    }  
  
    sample.low.to.high <- function(lower.end, ran, pi, shift) { 
      for(i in lower.end:uu) {                                
        if(ran <= pi[i+shift]) return(i)
        ran <- ran - pi[i+shift]
        }                                
    }
       
    sample.high.to.low <- function(upper.end, ran, pi, shift) {
      for(i in upper.end:ll) {                              
        if(ran <= pi[i+shift]) return(i)
        ran <- ran - pi[i+shift]
      } 
    }
    
    single.draw <- function(n1, n2, m1, psi, ll, uu, mode, pi) {
      ran <- runif(1)
      shift <- 1-ll  
      if(mode==ll) return(sample.low.to.high(ll, ran, pi, shift))            
      if(mode==uu) return(sample.high.to.low(uu, ran, pi, shift))                                         
      if(ran < pi[mode+shift]) return(mode)             
      ran <- ran - pi[mode+shift]
      lower <- mode - 1                                                                            
      upper <- mode + 1
          
      repeat {           
        if(pi[upper + shift] >= pi[lower + shift]) {              
          if(ran < pi[upper+shift]) return(upper)
          ran <- ran - pi[upper+shift]
          if(upper == uu) return( sample.high.to.low(lower, ran, pi, shift) )
          upper <- upper + 1                            
        }
        else {
          if(ran < pi[lower+shift]) return(lower)
          ran <- ran - pi[lower+shift]
          if(lower == ll) return( sample.low.to.high(upper, ran, pi, shift) )
          lower <- lower - 1                   
        }     
      }
    }
  
    ##
    ## MAIN FUNCTION
    ##

    # upper and lower limits for density evaluation
    ll <- max(0, m1-n2)
    uu <- min(n1, m1)
    
    # check parameters
    if(n1 < 0 | n2 < 0) {
       stop("n1 or n2 negative in rnoncenhypergeom().\n")
    }
    if(m1 < 0 | m1 > (n1 + n2)) {
       stop("m1 out of range in rnoncenhypergeom().\n")
    }
    if(psi <=0) {
       stop("psi [odds ratio] negative in rnoncenhypergeom().\n")
    }


    # get density and other parameters
    mode <- mode.compute(n1, n2, m1, psi, ll, uu) 
    pi <- dnoncenhypergeom(NA, n1, n2, m1, psi)[,2]
    
    output <- array(0,n)
    for(i in 1:n) output[i] <- single.draw(n1, n2, m1, psi, ll, uu, mode, pi)    
    return(output)
  }