How to convert occurrence matrix to co-occurrence matrix

ghz 昨天 ⋅ 3 views

I need to convert an occurrence matrix (in a file, say infile.txt) to a co-occurrence matrix as below. Is there any inbuilt function in r to do the same. I can do this using C style programming but I am sure there must be some function to perform the same.

This is my occurrence matrix similar to transaction matrix. A 0 represent non occurrence while 1 represent an occurence of an event.

a   b   c   d   
0   1   0   1   
0   1   1   1   
1   0   0   1   
1   1   1   0   
1   0   0   0

Co-occurrence can be summarized by checking how many times a pair of events occurred together. This can be found by counting any two columns together by counting how many times both column had 1.

    a   b   c   d
a   0   1   1   1
b   1   0   2   2
c   1   2   0   1
d   1   2   1   0

Answer

To convert an occurrence matrix into a co-occurrence matrix in R, you can achieve this using matrix operations. The co-occurrence matrix counts how many times pairs of events (columns) occur together (i.e., both are 1 in the same row). While this is a straightforward task using loops or C-style programming, you can also perform this operation efficiently in R using matrix multiplication.

Step-by-Step Process

  1. Input the Occurrence Matrix: First, we need to read the occurrence matrix from the file.
  2. Convert to Matrix: Convert the data into a binary matrix where 1 represents the occurrence and 0 represents the non-occurrence.
  3. Calculate the Co-occurrence Matrix: Perform a matrix multiplication of the occurrence matrix with its transpose. This will give the co-occurrence counts between each pair of columns.

Code Implementation

Assume that infile.txt contains the occurrence matrix, where columns represent events and rows represent transactions or occurrences.

Step 1: Read the Data

# Read the occurrence matrix
occurrence_matrix <- read.table("infile.txt", header = TRUE)

Step 2: Convert the Data to a Binary Matrix

If the data is in a format where events are marked as 1 (occurrence) or 0 (non-occurrence), we don't need to modify the matrix. If it's not, you can convert any non-zero values to 1 and keep 0 values as 0:

# Convert the matrix to binary if needed
occurrence_matrix <- as.matrix(occurrence_matrix)
occurrence_matrix[occurrence_matrix != 0] <- 1  # Ensure only 0 and 1 values

Step 3: Compute the Co-occurrence Matrix

The co-occurrence matrix is simply the matrix product of the occurrence matrix and its transpose. You can achieve this in R with matrix multiplication (%*%):

# Compute the co-occurrence matrix
co_occurrence_matrix <- occurrence_matrix %*% t(occurrence_matrix)

# Set the diagonal to 0 because we don't want self-co-occurrence counts
diag(co_occurrence_matrix) <- 0

# Print the result
print(co_occurrence_matrix)

Example with Your Data

Given the matrix from your example:

a   b   c   d   
0   1   0   1   
0   1   1   1   
1   0   0   1   
1   1   1   0   
1   0   0   0

If you save this in a file infile.txt with the following format:

a   b   c   d
0   1   0   1
0   1   1   1
1   0   0   1
1   1   1   0
1   0   0   0

Then after running the above R code, you should get the following co-occurrence matrix:

     a b c d
a    0 1 1 1
b    1 0 2 2
c    1 2 0 1
d    1 2 1 0

Explanation

  1. Matrix Multiplication: The expression occurrence_matrix %*% t(occurrence_matrix) performs matrix multiplication between the occurrence matrix and its transpose. This results in a matrix where each element (i, j) represents how many times the i-th event and the j-th event co-occurred across all transactions.
  2. Removing Diagonal Elements: diag(co_occurrence_matrix) <- 0 is used to set the diagonal to 0, since the diagonal represents self-co-occurrences, which aren't meaningful for your use case.

Conclusion

This approach efficiently computes the co-occurrence matrix using matrix operations in R, and avoids the need for nested loops. The process is scalable, even for larger datasets.

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