Transposition is another operation that can also be carried out on matrices. Just as when we transpose a vector, we transpose a matrix by taking each column in turn and making it a row. In other words, we interchange each column and row, so the first column becomes the first row, the second column becomes the second row, etc. For example,
Formally, if A is an \(n \times k\) matrix with elements \(a_{ij}\), then the transpose of A, denoted \(\mathbf{A}^\intercal\) is a \(k \times n\) matrix where element \(a^\intercal_{ij}=a_{ji}\). Some properties of the matrix transpose are:
\((\lambda\mathbf{A})^{^{\intercal}} = \lambda \mathbf{A}^{\intercal}\) where \(\lambda\) is a scalar
\((\mathbf{A} + \mathbf{B})^{^{\intercal}} = \mathbf{A}^{\intercal} + \mathbf{B}^{\intercal}\) (This property extends to more than two matrices; \((\mathbf{A} + \mathbf{B} + \mathbf{C})^{^{\intercal}} = \mathbf{A}^{\intercal} + \mathbf{B}^{\intercal} + \mathbf{C}^{\intercal}\).)
\((\mathbf{A}\mathbf{B})^{^{\intercal}} = \mathbf{B}^{\intercal} \mathbf{A}^{\intercal}\) (This property also extends to more than two matrices; \((\mathbf{A}\mathbf{B}\mathbf{C})^{^{\intercal}} = \mathbf{C}^{\intercal}\mathbf{B}^{\intercal} \mathbf{A}^{\intercal}\).)
Computationally, the t() function will produce the transpose of a matrix in R.
# Create AA =matrix(data =c(112, 134, 86, 94, 0, 0),nrow =2)# Display AA