Introduction: Matrices in R

While dealing with matrices in R, all columns in the matrix must have the same mode (numeric, character, etc.), and the same length. A matrix is a two-dimensional rectangular data set. It can be created using a vector input to the matrix( ) functions.

The general syntax of creating matrices in R is:

matrix_name <- matrix(vector, nrow = r, ncol = c,
                         byrow = FALSE, dimnames = list(char_vector_rownames,
                                                        char_vector_colnames)
)

byrow = TRUE indicates that the matrix will be filled by rows.

dimnames provides optional labels for the columns and rows.

Creating Matrices in R

Following the general syntax of the matrix( ) function, let us create a matrix from a vector of the first 20 numbers.

Example 1:

# Generate matrix having 5 rows and 4 columns 
y1 <- matrix (1 : 20, nrow = 5, ncol = 4) ; y1

# Output
[,1] [,2] [,3] [,4]
[1,] 1 6 11 16
[2,] 2 7 12 17
[3,] 3 8 13 18
[4,] 4 9 14 19
[5,] 5 10 15 20
y2 <- matrix (1 : 20, nrow = 5, ncol = 4, byrow = FALSE); y2

# Output
[,1] [,2] [,3] [,4]
[1,] 1 6 11 16
[2,] 2 7 12 17
[3,] 3 8 13 18
[4,] 4 9 14 19
[5,] 5 10 15 20
y3 <- matrix (1 : 20, nrow = 5, ncol = 4, byrow = TRUE) ; y3

# Output
[,1] [,2] [,3] [,4]
[1,] 1 2 3 4
[2,] 5 6 7 8
[3,] 9 10 11 12
[4,] 13 14 15 16
[5,] 17 18 19 20

Example 2:

elements <- c(11, 23, 29, 67)
rownames <- c("R1", "R2")
colnames <- c("C1", "C2")

m1 <- matrix(elements, nrow = 2, ncol = 2, byrow = TRUE, 
      dimnames = list(rownames, colnames)
      )

# Output
   C1 C2
R1 11 23
R2 29 67

Try the above example 2 with the following values set to arguments as below

nrow = 4 and ncol = 1, byrow = FALSE

Note the difference. You may also have some errors related to the number of rows or columns. Therefore, if you change the number of rows or columns then ensure that you have the same number of row names and column names too.

Matrix Operations in R

In the R language, there are some operators and functions that can be used to perform computation on one or more matrices. Some basic matrix operations in R are:

Matrix OperationOperator/ Function
Add/ Subtract+, −
Multiply%*%
Transposet( )
Inversesolve ( )
Extract Diagonaldiag( )It is described at the end too
Determinantdet( )

The following are some examples related to these operators and matrix functions.

m1 <- matrix(c(11, 23, 9, 35), nrow = 2)
m2 <- matrix(c(5, 19, 11, 20), nrow =2)
m3 <- m1 + m2
m4 <- m1 - m2
m5 <- m1 %*% m2
m6 <- m1 / m2
m1t <- t(m1)
m1tminv <- solve(m1t %*% m1)
diag(m1tminv)

# Output
> m1
     [,1] [,2]
[1,]   11    9
[2,]   23   35

> m2
     [,1] [,2]
[1,]    5   11
[2,]   19   20

> m3
     [,1] [,2]
[1,]   16   20
[2,]   42   55

> m4
     [,1] [,2]
[1,]    6   -2
[2,]    4   15

> m5
     [,1] [,2]
[1,]  226  301
[2,]  780  953

> m6
         [,1]      [,2]
[1,] 2.200000 0.8181818
[2,] 1.210526 1.7500000

> m1t
     [,1] [,2]
[1,]   11   23
[2,]    9   35
Introduction: Matrices in R

Some other important functions can be used to perform some required computations on matrices in R. These matrix operations in R are described below for matrix $X$. You can use your matrix.

Consider we have a matrix X with elements.

X <- matrix(1:20, nrow = 4, ncol = 5) 
X
FunctionDescription
rowSums(X)Compute the average value of each column of the Matrix $X$
colSums(X)Compute the average value of each row of the Matrix $X$
rowMeans(X)Compute the average value of each column of the Matrix $X$
colMeans(X)Compute the average value of each column of the Matrix $X$
diag(X)Extract diagonal elements of the Matrix $X$, or
Create a Matrix that has required diagonal elements such as diag(1:5), diag(5),
crossprod(X,X)Compute X‘X. It is a shortcut of t(X)%*%X

Obtaining Regression Coefficients using Matrices in R

Consider we have a dataset that has a response variable and few regressors. There are many ways to create data (or variables), such as one can create a vector for each variable, a data frame for all of the variables, matrices, or can read data stored in a file.

Here we try it using vectors, then bind the vectors where required. We will use matrices to obtain the regression coefficients.

y  <- c(5, 6, 7, 9, 8, 4, 3, 2, 1, 6, 0, 7)
x1 <- c(4, 5, 6, 7, 8, 3, 4, 9, 9, 8, 7, 5)
x2 <- c(10, 22, 23, 10, 11, 14, 15, 16, 17, 12, 11, 17)
x  <- cbind(1, x1, x2)

The cbind( ) function is used to create a matrix x. Note that 1 is also bound to get the intercept term (the model with the intercept term). Let us compute $\beta$’s from OLS using matrix functions and operators.

xt <- t(x)
xtx <- xt %*% x
xtxinv <- solve(xtx)
xty <- xt %*% y
b <- xtxinv %*% xty

The output is

#Output
x
        x1 x2
 [1,] 1  4 10
 [2,] 1  5 22
 [3,] 1  6 23
 [4,] 1  7 10
 [5,] 1  8 11
 [6,] 1  3 14
 [7,] 1  4 15
 [8,] 1  9 16
 [9,] 1  9 17
[10,] 1  8 12
[11,] 1  7 11
[12,] 1  5 17

xt
   [,1] [,2] [,3] [,4] [,5] [,6] [,7] [,8] [,9] [,10] [,11] [,12]
      1    1    1    1    1    1    1    1    1     1     1     1
x1    4    5    6    7    8    3    4    9    9     8     7     5
x2   10   22   23   10   11   14   15   16   17    12    11    17

xtx
         x1   x2
    12   75  178
x1  75  515 1103
x2 178 1103 2854

xtxinv
xty
b
Computing regression coefficient, matrices in R

Data Structure Matrix in R

visit https://gmstat.com

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