Compute the Expected Count Matrix from a Contingency Table

This function computes the expected counts matrix $E_{ij}$ from a given $I \times J$ contingency table using the formula: $$E_{ij} = \frac{n_{i.} n_{.j}}{n_{..}}$$ where $n_{i.}$ is the sum of the $i$-th row, $n_{.j}$ is the sum of the $j$-th column, and $n_{..}$ is the total sum of the table.

Usage

get_expected_counts(continTable)

Arguments

continTable: A numeric matrix representing the $I \times J$ contingency table.

Value

A numeric matrix of the same dimension as continTable, containing the expected counts for each cell $(i, j)$ of the contingency table. The expected counts are based on the row and column marginal sums of the contingency table.

Examples

# Create a 6 by 4 contingency table
set.seed(42)
dat_mat <- matrix(rpois(6*4, 20), nrow=6)
dat_mat
#>      [,1] [,2] [,3] [,4]
#> [1,]   26   19   25   18
#> [2,]   17   29   24   12
#> [3,]   20   19   23   27
#> [4,]   15   25   22   21
#> [5,]   19   30   24   21
#> [6,]   26   13   16   18

# Assign row and column names
contin_table <- check_and_fix_contin_table(dat_mat)
contin_table
#>      drug_1 drug_2 drug_3 drug_4
#> AE_1     26     19     25     18
#> AE_2     17     29     24     12
#> AE_3     20     19     23     27
#> AE_4     15     25     22     21
#> AE_5     19     30     24     21
#> AE_6     26     13     16     18

# Compute the expected counts of the contingency table
get_expected_counts(contin_table)
#>          [,1]     [,2]     [,3]     [,4]
#> [1,] 21.26523 23.33988 23.16699 20.22790
#> [2,] 19.81532 21.74853 21.58743 18.84872
#> [3,] 21.50688 23.60511 23.43026 20.45776
#> [4,] 20.05697 22.01375 21.85069 19.07859
#> [5,] 22.71513 24.93124 24.74656 21.60707
#> [6,] 17.64047 19.36149 19.21807 16.77996