R/MxAlgebraFunctions.R

Defines functions mxPearsonSelMean mxPearsonSelCov mxRobustLog mxEvaluateOnGrid imxDmvnorm omxPnbinom omxDnbinom lgamma1p logp2z p2z vechs2full vech2full omxSelectRowsAndCols omxSelectCols omxSelectRows ieigenval eigenval ieigenvec eigenvec omxAllInt omxMnor omxApproxEquals omxOr omxAnd omxLessThan omxGreaterThan equalDimensions omxNot imxLookupSymbolTable vec2diag diag2vec vechs vech rvectorize cvectorize tr

Documented in cvectorize diag2vec eigenval eigenvec ieigenval ieigenvec imxDmvnorm imxLookupSymbolTable lgamma1p logp2z mxEvaluateOnGrid mxPearsonSelCov mxPearsonSelMean mxRobustLog omxAllInt omxAnd omxApproxEquals omxDnbinom omxGreaterThan omxLessThan omxMnor omxNot omxOr omxPnbinom omxSelectCols omxSelectRows omxSelectRowsAndCols p2z rvectorize tr vec2diag vech vech2full vechs vechs2full

#
#   Copyright 2007-2018 by the individuals mentioned in the source code history
#
#   Licensed under the Apache License, Version 2.0 (the "License");
#   you may not use this file except in compliance with the License.
#   You may obtain a copy of the License at
# 
#        http://www.apache.org/licenses/LICENSE-2.0
# 
#   Unless required by applicable law or agreed to in writing, software
#   distributed under the License is distributed on an "AS IS" BASIS,
#   WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
#   See the License for the specific language governing permissions and
#   limitations under the License.

tr <- function(x) {
	if (is.matrix(x)) {
		return(sum(diag(x)))
	} else {
		return(NA)
	}
}

"%&%" <- function(x, y) {
	return(x %*% y %*% t(x))
}

"%^%" <- function(x, y) {
	return(kronecker(x, y, '^'))
}

cvectorize <- function(x) {	
	return(matrix(x, length(x), 1))
}

rvectorize <- function(x) {
	return(matrix(t(x), length(x), 1))
}

vech <- function(x) {
	return(x[lower.tri(x, diag=TRUE)])
}

vechs <- function(x) {
	return(x[lower.tri(x, diag=FALSE)])
}

diag2vec <- function(x) {
	return(as.matrix(diag(as.matrix(x))))
}

vec2diag <- function(x) {
	x <- as.matrix(x)
	if (nrow(x) != 1 && ncol(x) != 1) {
		stop("argument must be a row or column vector")
	}
	if (nrow(x) * ncol(x) == 1) {
		return(x)
	} else {
		return(as.matrix(diag(as.numeric(x))))
	}
}

##' imxLookupSymbolTable
##'
##' This is an internal function exported for those people who know
##' what they are doing.
##' 
##' @param name name
imxLookupSymbolTable <- function(name) {
	index <- which(omxSymbolTable["R.name"] == name)
	if(length(index) == 0) {
		stop(paste("Internal error, function",
			name, "cannot be found in OpenMx symbol table"),
			call. = FALSE)
	} else if (length(index) > 1) {
		stop(paste("Internal error, function",
			name, "appears twice in OpenMx symbol table"),
			call. = FALSE)	
	}
	return(as.integer(index - 1))
}

omxNot <- function(x) {
	retval <- as.matrix(!x)
	return(apply(retval, c(1,2), as.numeric))
}

equalDimensions <- function(x, y) {
	if (!identical(dim(x), dim(y))) {
		msg <- paste("Arguments 'x' and 'y'",
			"are not of identical dimensions",
			"in", deparse(width.cutoff = 400L, sys.call(-1)))
		stop(msg, call. = FALSE)
	}
}

omxGreaterThan <- function(x, y) {
	x <- as.matrix(x)
	y <- as.matrix(y)
	equalDimensions(x, y)
	return(apply(x > y, c(1,2), as.numeric))
}

omxLessThan <- function(x, y){
	x <- as.matrix(x)
	y <- as.matrix(y)
	equalDimensions(x, y)
	return(apply(x < y, c(1,2), as.numeric))
}

omxAnd <- function(x, y){
	x <- as.matrix(x)
	y <- as.matrix(y)
	equalDimensions(x, y)
	return(apply(x & y, c(1,2), as.numeric))
}

omxOr <- function(x, y){
	x <- as.matrix(x)
	y <- as.matrix(y)
	equalDimensions(x, y)
	return(apply(x | y, c(1,2), as.numeric))
}

omxApproxEquals <- function(x, y, epsilon){
	x <- as.matrix(x)
	y <- as.matrix(y)
	epsilon <- as.matrix(epsilon)
	equalDimensions(x, y)
	if (!identical(dim(x), dim(epsilon))) {
		msg <- paste("Argument 'epsilon'",
                        "is not of the same dimensions",
			"as 'x' and 'y'")
                stop(msg)
        }
	return(omxLessThan(abs(x - y), epsilon))
}

omxMnor <- function(covariance, means, lbound, ubound) {
    covariance <- as.matrix(covariance)
    means <- as.matrix(means)
    lbound <- as.matrix(lbound)
    ubound <- as.matrix(ubound)

    if(nrow(covariance) != ncol(covariance)) {
        stop("Covariance must be square")
    }
    if(nrow(means) > 1 && ncol(means) > 1) {
    	stop("'means' argument must be row or column vector")
    }
    if(nrow(lbound) > 1 && ncol(lbound) > 1) {
    	stop("'lbound' argument must be row or column vector")    
    }
    if(nrow(ubound) > 1 && ncol(ubound) > 1) {
    	stop("'ubound' argument must be row or column vector")    
    }
    
    if(ncol(covariance) != length(means)) {
        stop("'means' must have length equal to diag(covariance)")
    }
    if(ncol(covariance) != length(lbound)) {
        stop("'lbound' must have length equal to diag(covariance)")
    }
    if(ncol(covariance) != length(ubound)) {
        stop("'ubound' must have length equal to diag(covariance)")
    }
    
    retVal <- .Call(callAlgebra,
    	list(covariance, means, lbound, ubound), 
    	imxLookupSymbolTable("omxMnor"), 
		    generateOptionsList(NULL, 0, FALSE))
	if(single.na(retVal)){
		warning('Correlation with absolute value greater than one found.')
	}
    return(as.matrix(as.numeric(retVal)))
    
}

omxAllInt <- function(covariance, means, ...) {
    covariance <- as.matrix(covariance)
    means <- as.matrix(means)
    thresholdMats <- list(...)

    if(nrow(covariance) != ncol(covariance)) {
        stop("'covariance' must be square")
    }
    if(nrow(means) > 1 && ncol(means) > 1) {
    	stop("'means' argument must be row or column vector")
    }
    if(ncol(covariance) != length(means)) {
        stop("'means' must have length equal to diag(cov)")
    }
    
    if(sum(sapply(thresholdMats, ncol)) < ncol(covariance)) {
        stop("'thresholds' must have at least as many total columns as 'covariance'")
    }

    if(min(sapply(thresholdMats, nrow)) < 2) {
        stop("every column of 'thresholds' must have at least two rows: one lower bound and one upper")
    }
    
    retVal <- .Call(callAlgebra,
        c(list(covariance, means), thresholdMats),         # Flatten args into a single list
        imxLookupSymbolTable("omxAllInt"), 
		    generateOptionsList(NULL, 0, FALSE))
    
    return(as.matrix(as.numeric(retVal)))

}

eigenvec <- function(x) {
    x <- as.matrix(x)
    if(nrow(x) != ncol(x)) {
        stop("matrix must be square")
    }
    
    retval <- .Call(callAlgebra,
        list(x),         # Flatten args into a single list
        imxLookupSymbolTable("eigenvec"), 
        generateOptionsList(NULL, 0, FALSE))
        
    return(matrix(as.numeric(retval), nrow(x), ncol(x)))
}

ieigenvec <- function(x) {
    x <- as.matrix(x)
    if(nrow(x) != ncol(x)) {
        stop("matrix must be square")
    }
    
    retval <- .Call(callAlgebra,
        list(x),         # Flatten args into a single list
        imxLookupSymbolTable("ieigenvec"), 
        generateOptionsList(NULL, 0, FALSE))
        
    return(matrix(as.numeric(retval), nrow(x), ncol(x)))
}

eigenval <- function(x) {
    x <- as.matrix(x)
    if(nrow(x) != ncol(x)) {
        stop("matrix must be square")
    }
    
    retval <- .Call(callAlgebra,
        list(x),         # Flatten args into a single list
        imxLookupSymbolTable("eigenval"), 
        generateOptionsList(NULL, 0, FALSE))

    return(as.matrix(as.numeric(retval)))
}

ieigenval <- function(x) {
    x <- as.matrix(x)
    if(nrow(x) != ncol(x)) {
        stop("matrix must be square")
    }
    
    retval <- .Call(callAlgebra,
        list(x),         # Flatten args into a single list
        imxLookupSymbolTable("ieigenval"), 
        generateOptionsList(NULL, 0, FALSE))
        
    return(as.matrix(as.numeric(retval)))
}

omxSelectRows <- function(x, selector) {
    if(nrow(selector) != 1) selector <- t(selector)
    if(nrow(selector) != 1) {
        stop("Selector must have a single row or a single column")
    }
    if(nrow(x) != ncol(selector)) {
        stop("selector must have one column for each row of x")
    }
    return(x[as.logical(selector), , drop=FALSE])
}

omxSelectCols <- function(x, selector) {
    if(nrow(selector) != 1) selector <- t(selector)
    if(nrow(selector) != 1) {
        stop("Selector must have a single row or a single column")
    }
    if(ncol(x) != ncol(selector)) {
        stop("selector must have one column for each column of x")
    }    
    return(x[, as.logical(selector), drop=FALSE])    
}

omxSelectRowsAndCols <- function(x, selector) {
    if(nrow(selector) != 1) selector <- t(selector)
    if(nrow(selector) != 1) {
        stop("Selector must have a single row or a single column")
    }
    if(nrow(x) != ncol(selector) || ncol(x) != ncol(selector)) {
        stop("selector must have one column for each row and column of x")
    }
    selector <- as.logical(selector)
    return(x[selector, selector, drop=FALSE])    
}

vech2full <- function(x) {
	
	if(is.matrix(x)) {
		if (nrow(x) > 1 && ncol(x) > 1) {
			stop("Input to the full vech2full must be a (1 x n) or (n x 1) matrix.")
		}
		
		dimension <- max(dim(x))
		
	} else if(is.vector(x)) {
		dimension <- length(x)
	} else {
		stop("Input to the function vech2full must be either a matrix or a vector.")
	}
	
	k <- sqrt(2.0 * dimension + 0.25) - 0.5
	
	ret <- matrix(0, nrow=k, ncol=k)
	if(nrow(ret) != k) {
		stop("Incorrect number of elements in vector to construct a matrix from a half-vectorization.")
	}
	ret[lower.tri(ret, diag=TRUE)] <- as.vector(x)
	ret[upper.tri(ret)] <- t(ret)[upper.tri(ret)]
	return(ret)
}

vechs2full <- function(x) {

	if(is.matrix(x)) {
		if (nrow(x) > 1 && ncol(x) > 1) {
			stop("Input to the full vechs2full must be a (1 x n) or (n x 1) matrix.")
		}
		
		dimension <- max(dim(x))
		
	} else if(is.vector(x)) {
		dimension <- length(x)
	} else {
		stop("Input to the function vechs2full must be either a matrix or a vector.")
	}
	
	k <- sqrt(2.0 * dimension + 0.25) + 0.5
	
	ret <- matrix(0, nrow=k, ncol=k)
	if(nrow(ret) != k) {
		stop("Incorrect number of elements in vector to construct a matrix from a strict half-vectorization.")
	}
	ret[lower.tri(ret, diag=FALSE)] <- as.vector(x)
	ret[upper.tri(ret)] <- t(ret)[upper.tri(ret)]
	return(ret)
}

p2z <- function(x){
  return(qnorm(x))
}
logp2z <- function(x){
	return(qnorm(p=x,log.p=TRUE))
}
lgamma1p <- function(x){
	x <- as.matrix(x)
	retVal <- .Call(callAlgebra, list(x), imxLookupSymbolTable("lgamma1p"), 
									generateOptionsList(NULL, 0, FALSE))
	return(retVal)
}

#These two functions accept a different number of arguments from their 'stats' counterparts.  Therefore, they need to have different symbols.
#Otherwise, collisions result when they appear in expressions passed to mxEval():
omxDnbinom <- function(x,size,prob,mu,give_log){
	x <- as.matrix(x)
	size <- as.matrix(size)
	prob <- as.matrix(prob)
	mu <- as.matrix(mu)
	give_log <- as.matrix(give_log)
	retval <- .Call(callAlgebra, list(x,size,prob,mu,give_log), imxLookupSymbolTable("omxDnbinom"), 
									generateOptionsList(NULL, 0, FALSE))
	return(retval)
}
omxPnbinom <- function(q,size,prob,mu,lower_tail,give_log){
	q <- as.matrix(q)
	size <- as.matrix(size)
	prob <- as.matrix(prob)
	mu <- as.matrix(mu)
	lower_tail <- as.matrix(lower_tail)
	give_log <- as.matrix(give_log)
	retval <- .Call(callAlgebra, list(q,size,prob,mu,lower_tail,give_log), imxLookupSymbolTable("omxPnbinom"), 
									generateOptionsList(NULL, 0, FALSE))
	return(retval)
}


##' A C implementation of dmvnorm
##'
##' This API is visible to permit testing. Please do not use.
##'
##' @param loc loc
##' @param mean mean
##' @param sigma sigma
imxDmvnorm <- function(loc, mean, sigma) .Call(Dmvnorm_wrapper, loc, mean, sigma)

mxEvaluateOnGrid <- function(algebra, abscissa) {
	stop(paste("mxEvaluateOnGrid is not compatible with mxEval.",
		   "It can only be evaluated in an mxRun context.",
		   "For an example of usage, see the manual ?mxEvaluateOnGrid"))
}

mxRobustLog <- function(pr) {
	result <- log(pr)
	result[pr == 0.0] <- -745
	result
}

mxPearsonSelCov <- function(origCov, newCov) {
  m1 <- match(colnames(newCov), colnames(origCov))

  if (any(is.na(m1))) {
	  stop(paste("schurComplementC: cannot find variables",
		  omxQuotes(colnames(newCov)[is.na(m1)])))
  }

  rpp <- origCov[m1,m1,drop=F]
  rpq <- origCov[m1,-m1,drop=F]
  rqq <- origCov[-m1,-m1,drop=F]
  irpp <- solve(rpp)
  origCov[m1,m1] <- newCov
  tmp <- newCov %*% irpp %*% rpq
  origCov[-m1,m1] <- t(tmp)
  origCov[m1,-m1] <- tmp
  origCov[-m1,-m1] <- rqq - t(rpq) %*% (irpp - irpp %*% newCov %*% irpp) %*% rpq
  origCov
}

mxPearsonSelMean <- function(origCov, newCov, origMean) {
  m1 <- match(colnames(newCov), colnames(origCov))
  
  if (any(is.na(m1))) {
    stop(paste("schurComplementC: cannot find variables",
               omxQuotes(colnames(newCov)[is.na(m1)])))
  }
  
  rpp <- origCov[m1,m1,drop=F]
  rqp <- origCov[-m1,m1,drop=F]
  irpp <- solve(rpp)
  origMean[-m1,] <- origMean[-m1,] + rqp %*% irpp %*% origMean[m1,,drop=F]
  origMean
}

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OpenMx documentation built on Oct. 5, 2018, 5:05 p.m.