Matrix Algebra

Most of the methods on this website actually describe the programming of matrices. It is built deeply into the R language. This section will simply cover operators and functions specifically suited to linear algebra. Before proceeding you many want to review the sections on Data Typesand Operators.

Matrix facilites

In the following examples, A and B are matrices and x and b are a vectors.

Operator or Function Description
A * B Element-wise multiplication
A %*% B Matrix multiplication
A %o% B Outer product. AB'
** A'B and A'A** respectively.
t(A) Transpose
diag(x) Creates diagonal matrix with elements of x in the principal diagonal
diag(A) Returns a vector containing the elements of the principal diagonal
diag(k) If k is a scalar, this creates a k x k identity matrix. Go figure.
solve(A, b) Returns vector x in the equation b = Ax (i.e., A-1b )
solve(A) Inverse of A where A is a square matrix.
ginv(A) Moore-Penrose Generalized Inverse of A.
ginv(A) requires loading the MASS package.
y<-eigen(A) y$val are the eigenvalues of A
y$vec are the eigenvectors of A
y<-svd(A) Single value decomposition of A.
y$d = vector containing the singular values of A
= matrix with columns contain the left singular vectors of** A**
y$v = matrix with columns contain the right singular vectors of A
R <- chol(A) Choleski factorization of A. Returns the upper triangular factor, such that R'R = A.
y <- qr(A) QR decomposition of A.
y$qr has an upper triangle that contains the decomposition and a lower triangle that contains information on the Q decomposition.
y$rank is the rank of A.
y$qraux a vector which contains additional information on Q.
y$pivot contains information on the pivoting strategy used.
cbind(A,B,...) Combine matrices(vectors) horizontally. Returns a matrix.
rbind(A,B,...) Combine matrices(vectors) vertically. Returns a matrix.
rowMeans(A) Returns vector of row means.
rowSums(A) Returns vector of row sums.
colMeans(A) Returns vector of column means.
colSums(A) Returns vector of column sums.

Matlab Emulation

The matlab package contains wrapper functions and variables used to replicate MATLAB function calls as best possible. This can help porting MATLAB applications and code to R.

Going Further

The Matrix package contains functions that extend R to support highly dense or sparse matrices. It provides efficient access to BLAS (Basic Linear Algebra Subroutines), Lapack (dense matrix), TAUCS (sparse matrix) and UMFPACK (sparse matrix) routines.

To Practice

Take the Introduction to Statistics in R course to grow your statistical skills futher.