Module 10

 

The deliberate bug in the provided code is the use of && instead of & inside the for loop for determining outliers. The && operator evaluates only the first element of the logical vectors, which leads to incorrect outlier detection.

Here's the corrected version of the function:

r

tukey_multiple <- function(x) {

  outliers <- array(TRUE, dim = dim(x))

  for (j in 1:ncol(x)) {

    outliers[, j] <- outliers[, j] & tukey.outlier(x[, j])

  }

  outlier.vec <- vector(length = nrow(x))

  for (i in 1:nrow(x)) { 

    outlier.vec[i] <- all(outliers[i, ]) 

  } 

  return(outlier.vec) 

}

To test the corrected function, we can apply it to a sample dataset and observe if it returns the expected results without errors:

r

# Sample dataset

set.seed(123)

data <- matrix(rnorm(100), ncol = 5)

# Applying the fixed function

outliers <- tukey_multiple(data)

print(outliers)