## Addition and substraction

The most basic matrix operations are addition and substraction. In the following examples we are going to use the square matrices of the following block of code:

```
A <- matrix(c(10, 8,
5, 12), ncol = 2, byrow = TRUE)
A
B <- matrix(c(5, 3,
15, 6), ncol = 2, byrow = TRUE)
B
```

```
# A # B
[, 1] [, 2] [, 1] [, 2]
[1, ] 10 8 [1, ] 5 3
[2, ] 7 12 [2, ] 15 6
```

These matrices are both of the same dimensions. You can check the dimensions (number of rows and columns, respectively) of a matrix with the `dim`

function.

```
dim(A) # 2 2
dim(B) # 2 2
```

On the one hand, with the `+`

operator you can compute an element-wise sum of the two matrices:

`A + B`

```
[, 1] [, 2]
[1, ] 15 11
[2, ] 20 18
```

On the other hand, the `-`

operator will allow you to substract them:

`A - B`

```
[, 1] [, 2]
[1, ] 5 5
[2, ] -10 6
```

## Transpose a matrix in R

To find the transpose of a matrix in R you just need to use the `t`

function as follows:

`t(A)`

```
[, 1] [, 2]
[1, ] 10 5
[2, ] 8 12
```

`t(B)`

```
[, 1] [, 2]
[1, ] 5 15
[2, ] 3 6
```

## Matrix multiplication in R

There are different types of matrix multiplications: by a scalar, element-wise multiplication, matricial multiplication, exterior and Kronecker product.

### Multiplication by a scalar

In order to multiply or divide a matrix by a scalar you can make use of the `*`

or `/`

operators, respectively:

`2 * A`

```
[, 1] [, 2]
[1, ] 20 16
[2, ] 10 24
```

`A / 2`

```
[, 1] [, 2]
[1, ] 5.0 4
[2, ] 2.5 6
```

### Element-wise multiplication

The element-wise multiplication of two matrices of the same dimensions can also be computed with the `*`

operator. The output will be a matrix of the same dimensions of the original matrices.

`A * B`

```
[, 1] [, 2]
[1, ] 50 24
[2, ] 75 72
```

### Matrix multiplication

In R, a matricial multiplication can be performed with the `%*%`

operator.

`A %*% B`

```
[, 1] [, 2]
[1, ] 170 78
[2, ] 205 87
```

**check that the dimensions are compatible**. The number of columns of the first matrix must be equal to the number of rows of the second.

### Matrix crossproduct

If you need to calculate the matricial product of a matrix and the transpose or other you can type `t(A) %*% B`

or `A %*% t(B)`

, being `A`

and `B`

the names of the matrices. However, in R it is more efficient and faster using the `crossprod`

and `tcrossprod`

functions, respectively.

`crossprod(A, B)`

```
[, 1] [, 2]
[1, ] 125 60
[2, ] 220 96
```

`tcrossprod(A, B)`

```
[,1 ] [, 2]
[1, ] 74 198
[2, ] 61 147
```

### Exterior product

Similarly to the matricial multiplication, in R you can compute the exterior product of two matrices with the `%o%`

operator. This operator is a shorcode for the default `outer`

function.

```
A %o% B
# Equivalent to:
outer(A, B, FUN = "*")
```

```
, , 1, 1
[, 1] [, 2]
[1, ] 50 40
[2, ] 25 60
, , 2, 1
[, 1] [, 2]
[1, ] 150 120
[2, ] 75 180
, , 1, 2
[, 1] [, 2]
[1, ] 30 24
[2, ] 15 36
, , 2, 2
[, 1] [, 2]
[1, ] 60 48
[2, ] 30 72
```

### Kronecker product

The Kronecker product of two matrices A and B, denoted by A \otimes B is the last type of matricial product we are going to review. In R, the calculation can be achieved with the `%x%`

operator.

`A %x% B`

```
[, 1] [, 2] [, 3] [, 4]
[1, ] 50 30 40 24
[2, ] 150 60 120 48
[3, ] 25 15 60 36
[4, ] 75 30 180 72
```

## Power of a matrix in R

There is no a built-in function in R base to calculate the power of a matrix, so we will provide two different alternatives.

On the one hand, you can make use of the `%^%`

operator of the `expm`

package as follows:

```
# install.packages("expm")
library(expm)
A %^% 2
```

```
[, 1] [, 2]
[1, ] 140 176
[2, ] 110 184
```

On the other hand the `matrixcalc`

package provides the `matrix.power`

function:

```
# install.packages("matrixcalc")
library(matrixcalc)
matrix.power(A, 2)
```

```
[, 1] [, 2]
[1, ] 140 176
[2, ] 110 184
```

You can check that the power is correct with the following code:

`A %*% A`

Note that if you want to calculate the element-wise power you just need to use the `^`

operator. In this case the matrix don’t need to be square.

`A ^ 2`

```
[, 1] [, 2]
[1, ] 100 64
[2, ] 25 144
```

## Determinant of a matrix in R

The determinant of a matrix A, generally denoted by |A|, is a scalar value that encodes some properties of the matrix. In R you can make use of the `det`

function to calculate it.

```
det(A) # 80
det(B) # -15
```

## Inverse of a matrix in R

In order to calculate the inverse of a matrix in R you can make use of the `solve`

function.

```
M <- solve(A)
M
```

```
[, 1] [, 2]
[1, ] 0.1500 -0.100
[2, ] -0.0625 0.125
```

As a matrix multiplied by its inverse is the identity matrix we can verify that the previous output is correct as follows:

`A %*% M`

```
[, 1] [, 2]
[1, ] 1 0
[2, ] 0 1
```

Moreover, as main use of the `solve`

function is to solve a system of equations, if you want to calculate the solution to A%*% X = B you can type:

`solve(A, B)`

```
[, 1] [, 2]
[1, ] -0.7500 -0.1500
[2, ] 1.5625 0.5625
```

## Rank of a matrix in R

The rank of a matrix is maximum number of columns (rows) that are linearly independent. In R there is no base function to calculate the rank of a matrix but we can make use of the `qr`

function, that in addition to calculate the QR decomposition returns the rank of the input matrix. An alternative is to use the `rankMatrix`

function from the `Matrix`

package.

```
qr(A)$rank # 2
qr(B)$rank # 2
# Equivalent to:
library(Matrix)
rankMatrix(A)[1] # 2
```

## Matrix diagonal in R

The `diag`

function allows you to extract or replace the diagonal of a matrix:

```
# Extract the diagonal
diag(A) # 10 12
diag(B) # 5 6
# Replace the diagonal
# diag(A) <- c(0, 2)
```

Applying the `rev`

function to the columns of the matrix you can also extract off the elements of the secondary diagonal matrix in R:

```
# Extract the secondary diagonals
diag(apply(A, 2, rev)) # 5 8
diag(apply(B, 2, rev)) # 15 3
```

### Diagonal matrix

With the `diag`

function you can also make a diagonal matrix, passing a vector as input of the function.

`diag(c(7, 9, 2))`

```
[, 1] [, 2] [, 3]
[1, ] 7 0 0
[2, ] 0 9 0
[3, ] 0 0 2
```

### Identity matrix in R

In addition to the previous functionalities, the `diag`

function also allows creating identity matrices, specifying the dimension of the desired matrix.

`diag(4)`

```
[, 1] [, 2] [, 3] [, 4]
[1, ] 1 0 0 0
[2, ] 0 1 0 0
[3, ] 0 0 1 0
[4, ] 0 0 0 1
```

## Eigenvalues and eigenvectors in R

Both the eigenvalues and eigenvectors of a matrix can be calculated in R with the `eigen`

function.

On the one hand, the eigenvalues are stored on the `values`

element of the returned list. The eigenvalues will be shown in decreasing order:

```
eigen(A)$values # 17.403124 4.596876
eigen(B)$values # 12.226812 -1.226812
```

On the other hand, the eigenvectors are stored on the `vectors`

element:

`eigen(A)$vectors`

```
[, 1] [, 2]
[1, ] -0.7339565 -0.8286986
[2, ] -0.6791964 0.5596952
```

`eigen(B)$vectors`

```
[, 1] [, 2]
[1, ] -0.3833985 -0.4340394
[2, ] -0.9235830 0.9008939
```

## Singular, QR and Cholesky decomposition in R

In this final section we are going to discuss how to perform some decompositions related with matrices.

First, the Singular Value Decomposition (SVD) can be calculated with the `svd`

function.

`svd(A)`

```
$d
[1] 17.678275 4.525328
$u
[, 1] [, 2]
[1, ] -0.7010275 -0.7131342
[2, ] -0.7131342 0.7010275
$v
[, 1] [, 2]
[1, ] -0.5982454 -0.8013130
[2, ] -0.8013130 0.5982454
```

The function will return a list, where the element `d`

is a vector containing the singular values sorted in decreasing order and `u`

and `v`

are matrices containing the left and right singular vectors of the original matrix, respectively.

Second, the `qr`

function allows you to calculate the QR decomposition. The first element of the output will return a matrix of the same dimension as the original matrix, where the upper triangle matrix contains the \bold{R} of the decomposition and the lower the \bold{Q}.

`qr(A)$qr`

```
[, 1] [, 2]
[1, ] -11.1803399 -12.521981
[2, ] 0.4472136 7.155418
```

Last, you can compute the Cholesky factorization of a real symmetric **positive-definite square matrix** with the `chol`

function.

`chol(A)`

```
[, 1] [, 2]
[1, ] 3.162278 2.529822
[2, ] 0.000000 2.366432
```

`chol`

function doesn’t check for symmetry. However, you can make use of the `isSymmetric`

function to check it.