Confusion

One of the types of objects in Praat.

An object of type Confusions represents a confusion matrix, with stimuli as row labels and responses as column labels. The entry at position [i] [j] represents the number of times response j was given to the stimulus i.

## Creating a Confusion from data in a text file

Suppose you have two objects A and B. In one way or another, you have acquired the following confusions: δAA = 6, δAB = 2 , δBA = 1, and δBB = 7.

You can create a simple text file like the following:

`"ooTextFile" ! to make Praat recognize your file
` `"Confusion" ! The line that tells Praat about the contents
` `2 "A" "B" ! Number of columns, and column labels
` `2 ! Number of rows
` `"A" 6 2 ! Row label A, A-A value, A-B value
` `"B" 1 7 ! Row label B, B-A value, B-B value
`

This text file can be read with the Read from file... command.

## Commands

Creation:

  • Categories: To Confusion
  • Create simple Confusion...

Drawing

  • Draw as numbers...
  • Draw as squares...

Query

  • Get fraction correct
  • Confusion: Get stimulus sum...
  • Confusion: Get response sum...
  • Get grand sum

Modification

  • Formula...
  • Confusion: Increase...

Grouping

  • Confusion: Group stimuli...
  • Confusion: Group responses...

Analysis:

  • Confusion: To Similarity...
  • Confusion: To Dissimilarity (pdf)...

## Inside a Confusion

With Inspect you will see the following attributes:

numberOfRows
  the number of stimuli.
numberOfColumns
  the number of responses.
rowLabels
  the names of the stimuli.
columnLabels
  the names of the responses.

### Links to this page

- ClassificationTable: To Confusion...
- Confusion & ClassificationTable: Increase confusion count
- Confusion: Condense...
- Confusion: To Dissimilarity...
- Confusion: To TableOfReal (marginals)
- Discriminant analysis
- Feedforward neural networks 2. Quick start
- Feedforward neural networks 3. FFNet versus discriminant classifier
- INDSCAL analysis