distillery (version 1.2-1)

MatrixSqrt: Square-Root of a Square Matrix

Description

Find the (approximate ) square-root of a square matrix that is possibly not positive definite using the singular-value decomposition.

Usage

MatrixSqrt( Sigma, verbose = getOption("verbose") )

Arguments

Sigma

matrix for which the square root is to be taken.

verbose

logical, should progress information be printed to the screen.

Value

A matrix is returned.

Details

The eigen function is first called in order to obtain the eigen values and vectors. If any are complex then a symmetry transformation is applied (i.e., Sigma = 0.5 * ( Sigma + t( Sigma ) ) ) and then the eigen function is called again. Eigen values that are less than zero, but close to zero, are set to zero. If the matrix is positive definite, then the chol function is called in order to return the Cholesky decomposition. Otherwise, U sqrt( D ) U' is returned, where U is the matrix of eigen vectors and D a diagonal matrix whose diagonal contains the eigen values. The function will try to find the square root even if it is not positive definite, but it may fail.

References

Hocking, R. R. (1996) Methods and Applications of Linear Models. Wiley Series in Probability and Statistics, New York, NY, 731 pp.

See Also

eigen, chol

Examples

# NOT RUN {
# Simulate 3 random variables, Y, X1 and X2, such that
# Y is correlated with both X1 and X2, but X1 and X2
# are uncorrelated.

set.seed( 2421 );

Z <- matrix( rnorm( 300 ), 100, 3 );
R1 <- cbind( c( 1, 0.8, 0.6 ), c( 0.8, 1, 0 ), c( 0.6, 0, 1 ) );
R2 <- MatrixSqrt( R1 );

# R1;
# R2 %*% t( R2 );
# zapsmall( R2 %*% t( R2 ) );

Z <- Z 
# }
# NOT RUN {
<!-- %*% R2; -->
# }
# NOT RUN {
Y <- Z[,1];
X1 <- Z[,2];
X2 <- Z[,3];
cor( Y, X1 );
cor( Y, X2 );
cor( X1, X2 );
plot( Y, X1, pch = 20, col = "darkblue",
     bg = "darkblue", cex = 1.5 );
points( Y, X2, col = "darkgray", pch = "+", cex = 1.5 );
plot( X1, X2 );

# }
# NOT RUN {
# The following line will give an error message.
# chol( R1 );
# }
# NOT RUN {
# }